Optimal identification of unknown groundwater contaminant sources in conjunction with designed monitoring networks

Human activities and improper management practices have resulted in widespread deterioration of groundwater quality worldwide. Groundwater contamination has seriously threatened its beneficial use in recent decades. Remediation processes are necessary for groundwater management. In the remediation of contaminated aquifer sites, identification of unknown groundwater contaminant sources has a crucial role. In other words, an effective groundwater remediation process needs an accurate identification of contaminant sources in terms of contaminant source locations, magnitudes and time-release. On the other hand, the efficiency and reliability of contaminant source identification depend on the availability, adequacy, and accuracy of hydrogeologic information and contaminant concentration measurements data. Whereas, generally when groundwater contaminations are detected, only limited and sparse measured contaminant concentration values are available. Usually, groundwater contaminations are detected after a long time, years or even decades after the starting of contaminant source activities or even after their extinction. Therefore, usually, there is not enough information regarding the number of contaminant sources, the duration of sources' activities and the contaminant magnitudes, as well as the hydrogeologic parameters of the contaminated aquifers. Simulations of groundwater flow and solute transport involve intrinsic uncertainties due to this sparse information or lack of enough hydrogeologic information of the porous medium. Therefore, for groundwater management, developing and applying an efficient procedure for identification of unknown contaminant sources is essential. Moreover, available observed contaminant concentration values are usually erroneous and this erroneous data could cause instability in the solution results. Various combinations of source characteristics can result in similar effects at observation locations and cause non-uniqueness in the solution. Due to these instabilities and non-uniqueness in solution (Datta, 2002), the source identification problem is known as an "ill-posed problem" (Yeh, 1986). The non-uniqueness and uncertainties involved in this ill-posed problem make this problem a difficult and complex task. Suggested methodologies to tackle this task are not completely efficient. For instance, the crux of previous approaches is highly vulnerable to the accuracy and adequacy of contaminant concentration measurements and hydrogeologic data. As a result, many of the previously suggested approaches are not applicable to real-world cases and application of relevant approaches to real-world contaminant aquifer sites is usually tedious and time-consuming. The suggested methodologies involve enormous computational time and cost due to repeated runs of the numerical simulation models within the optimisation algorithms. Therefore, to identify the unknown characteristics of contaminant sources, different surrogate models were developed. Three different algorithms were utilized for developing the surrogate models: Self-Organising Maps (SOM), Gaussian Process Regression (GPR), and Multivariate Adaptive Regression Splines (MARS). Performance of the developed procedures was assessed for potential applicability in two hypothetical, an experimental, and a real-world contaminated aquifer sites. In the used contaminated aquifer sites, only limited contaminant concentrations data were assumed to be available. In three cases, it was also assumed that the contaminant concentrations data were collected a long time after the start of the first potential contaminant source activities. The performance evaluations of the developed surrogate models show that these models could accurately mimic the behaviour of simulation models of groundwater flow and solute transport. These surrogate models solutions showed acceptable errors in comparison to the more robust numerical model solutions. These surrogate models were also used for identification of unknown groundwater contaminant sources when utilized to solve the inverse problem. The SOM algorithm was chosen as the surrogate model type in this study for directly addressing the source identification problem as well. The SOM algorithm was chosen for its classification capabilities. In source identification problems, the number of actual contaminant sources is uncertain and usually, a set of a larger number of potential contaminant sources are assumed. Therefore, screening the active sources by SOM-based Surrogate Models (SOM-based SMs) may simplify the source identification problems. The performance of the developed SOM-based SMs was assessed for different scenarios. Results indicate that the developed models could also accurately screen the active sources among all potential contaminant sources with sparse contaminant concentrations data and uncertain hydrogeologic information. For comparison purposes, MARS and GPR algorithms that are precise prediction tools were also utilized for developing MARS and GPR-based Surrogate Models (MARS and GPR-based SM) for source identification. Performance of the developed surrogate models for source identification was evaluated in terms of Normalized Absolute Error of Estimation (NAEE). For example, the performance of the developed SOM, MARS and GPR-based SMs was assessed in an illustrative hypothetical contaminated aquifer site. The results for testing data in terms of NAEE were equal to 16.3, 4.9 and 6.6%, respectively. Performance of the developed SOM, MARS and GPR-based SMs was also evaluated in an experimental contaminated aquifer site. The results for testing data in terms of NAEE were equal to 15.8, 14.1 and 16.2%. These performance evaluation results of the developed surrogate models indicate that the MARS-based SMs can be more accurate models than the SOM and GPR-based SMs in source identification problems. The most important advantage of the developed methodologies is their direct application for source identification in an inverse mode without linking to an optimisation model. Surrogate Model-Based Optimisation (SMO) was also developed and utilized for source identification. In this developed SMO, MARS and Genetic Algorithm (GA) were utilized as the surrogate model and the optimisation model types, respectively. MARS-based SMOs performance was assessed in an illustrative hypothetical contaminated aquifer site and in a real-world contaminated aquifer site. The result of the developed MARS-based SMO for testing data in the illustrative hypothetical contaminated aquifer site in terms of Root Mean Square Error (RMSE) was equal to 0.92. Obtained solution results of the developed MARS-based SM in the real contaminated study area for testing data in terms of RMSE was equal to 42.5. The performance evaluation results of the developed methodologies in different hypothetical and real contaminated study areas demonstrate the capabilities of the constructed SOM, GPR, and MARS-based SMs and MARS-based SMO for source identification. Also, in order to increase the accuracy of source identification results, and based on the preliminary solution results of the developed SOM-based SMs, a sequential sampling method can be applied adaptively for updating the developed surrogate models. Information from a hypothetical contaminated aquifer site was used to assess the performance of this procedure. Performance evaluation results of adaptively developed MARS and GPR-based SMs in terms of NAEE were equal to 1.9 and 2.1%, respectively. The results show 3 and 4.5% improvements for source identification results by applying adaptively developed MARS and GPR-based SMs, respectively. Another difficulty with source identification problems has been the limitation and sparsity of observed contaminant concentrations data. Previously suggested methodologies usually need long-term observation data at numerous locations which can involve large costs. Therefore, developing an effective monitoring network design procedure was one of the main goals of this study. In designing the monitoring networks, two main objectives were considered: 1. Maximizing the accuracy of source identification results, and 2. Limiting the number of monitoring locations. It was supposed that by implementing obtained results from the designed monitoring networks for developing surrogate models, the source identification results would significantly improve. In this study, different algorithms were utilized to identify potentially important and effective monitoring locations which probably could improve source identification results. These algorithms are Random Forests (RF), Tree Net (TN) and CART. The performance of these algorithms was evaluated in different scenarios. Results indicate the potential applicability of these algorithms in recognising the most important components of prediction models. As a result, these algorithms could apply for designing monitoring networks for improving the source identification efficiency and accuracy. Concentration measurement information from a designed monitoring network and from a set of arbitrary monitoring sites was utilized to develop MARS-based surrogate models for source identification. The solution results for these two scenarios of designed monitoring and arbitrary measurements were compared for a hypothetical study area for evaluation purpose. Performance evaluation results of the developed surrogate model using information from the designed monitoring network showed improvement in source identification error in terms of RMSE for testing data by 0.7. The obtained information from the designed monitoring network was used to develop MARSbased SM for source identification of testing data in a real contaminated aquifer site. Source identification results of the developed MARS-based SM with testing data for the real contaminated aquifer site showed improvement by 35.3 in terms of RMSE compared to the solution results of MARS-based SM, which was developed by using obtained information from arbitrary monitoring locations. Performance evaluation results for the developed monitoring network procedure demonstrate the potential applicability of this procedure for source identification

Evaluation of unknown groundwater contaminant sources characterization efficiency under hydrogeologic uncertainty in an experimental aquifer site by utilizing surrogate models

Characterization of unknown groundwater contaminant sources is an important but difficult step in effective groundwater management. The difficulties arise mainly due to the time of contaminant detection which usually happens a long time after the start of contaminant source(s) activities. Usually, limited information is available which also can be erroneous. This study utilizes Self-Organizing Map (SOM) and Gaussian Process Regression (GPR) algorithms to develop surrogate models that can approximate the complex flow and transport processes in a contaminated aquifer. The important feature of these developed surrogate models is that unlike the previous methods, they can be applied independently of any linked optimization model solution for characterizing of unknown groundwater contaminant sources. The performance of the developed surrogate models is evaluated for source characterization in an experimental contaminated aquifer site within the heterogeneous sand aquifer, located at the Botany Basin, New South Wales, Australia. In this study, the measured contaminant concentrations and hydraulic conductivity values are assumed to contain random errors. Simulated responses of the aquifer to randomly specified contamination stresses as simulated by using a three-dimensional numerical simulation model are utilized for initial training of the surrogate models. The performance evaluation results obtained by using different surrogate models are also compared. The evaluation results demonstrate the different capabilities of the developed surrogate models

Development of a sustainable groundwater management strategy and sequential compliance monitoring to control saltwater intrusion in coastal aquifers

The coastal areas of the world are characterized by high population densities, an abundance of food, and increased economic activities. These increasing human settlements, subsequent increases in agricultural developments and economic activities demand an increasing amount quantity of freshwater supplies to different sectors. Groundwater in coastal aquifers is one of the most important sources of freshwater supplies. Over exploitation of this coastal groundwater resource results in seawater intrusion and subsequent deterioration of groundwater quality in coastal aquifers. In addition, climate change induced sea level rise, in combination with the effect of excessive groundwater extraction, can accelerate the seawater intrusion. Adequate supply of good quality water to different sectors in coastal areas can be ensured by adoption of a proper management strategy for groundwater extraction. Optimal use of the coastal groundwater resource is one of the best management options, which can be achieved by employing a properly developed optimal groundwater extraction strategy. Coupled simulation-optimization (S-O) approaches are essential tools to obtain the optimal groundwater extraction patterns. This study proposes approaches for developing multiple objective management of coastal aquifers with the aid of barrier extraction wells as hydraulic control measure of saltwater intrusion in multilayered coastal aquifer systems. Therefore, two conflicting objectives of management policy are considered in this research, i.e. maximizing total groundwater extraction for advantageous purposes, and minimizing the total amount of water abstraction from barrier extraction wells. The study also proposes an adaptive management strategy for coastal aquifers by developing a three-dimensional (3-D) monitoring network design. The performance of the proposed methodologies is evaluated by using both an illustrative multilayered coastal aquifer system and a real life coastal aquifer study area. Coupled S-O approach is used as the basic tool to develop a saltwater intrusion management model to obtain the optimal groundwater extraction rates from a combination of feasible solutions on the Pareto optimal front. Simulation of saltwater intrusion processes requires solution of density dependent coupled flow and solute transport numerical simulation models that are computationally intensive. Therefore, computational efficiency in the coupled S-O approach is achieved by using an approximate emulator of the accompanying physical processes of coastal aquifers. These emulators, often known as surrogate models or meta-models, can replace the computationally intensive numerical simulation model in a coupled S-O approach for achieving computational efficiency. A number of meta-models have been developed and compared in this study for integration with the optimization algorithm in order to develop saltwater intrusion management model. Fuzzy Inference System (FIS), Adaptive Neuro Fuzzy Inference System (ANFIS), Multivariate Adaptive Regression Spline (MARS), and Gaussian Process Regression (GPR) based meta-models are developed in the present study for approximating coastal aquifer responses to groundwater extraction. Properly trained and tested meta-models are integrated with a Controlled Elitist Multiple Objective Genetic Algorithm (CEMOGA) within a coupled S-O approach. In each iteration of the optimization algorithm, the meta-models are used to compute the corresponding salinity concentrations for a set of candidate pumping patterns generated by the optimization algorithm. Upon convergence, the non-dominated global optimal solutions are obtained as the Pareto optimal front, which represents a trade-off between the two conflicting objectives of the pumping management problem. It is observed from the solutions of the meta-model based coupled S-O approach that the considered meta-models are capable of producing a Pareto optimal set of solutions quite accurately. However, each meta-modelling approach has distinct advantages over the others when utilized within the integrated S-O approach. Uncertainties in estimating complex flow and solute transport processes in coastal aquifers demand incorporation of the uncertainties related to some of the model parameters. Multidimensional heterogeneity of aquifer properties such as hydraulic conductivity, compressibility, and bulk density are considered as major sources of uncertainty in groundwater modelling system. Other sources of uncertainty are associated with spatial and temporal variability of hydrologic as well as human interventions, e.g. aquifer recharge and transient groundwater extraction patterns. Different realizations of these uncertain model parameters are obtained from different statistical distributions. FIS based meta-models are advanced to a Genetic Algorithm (GA) tuned hybrid FIS model (GA-FIS), to emulate physical processes of coastal aquifers and to evaluate responses of the coastal aquifers to groundwater extraction under groundwater parameter uncertainty. GA is used to tune the FIS parameters in order to obtain the optimal FIS structure. The GA-FIS models thus obtained are linked externally to the CEMOGA in order to derive an optimal pumping management strategy using the coupled S-O approach. The evaluation results show that the proposed saltwater intrusion management model is able to derive reliable optimal groundwater extraction strategies to control saltwater intrusion for the illustrative multilayered coastal aquifer system. The optimal management strategies obtained as solutions of GA-FIS based management models are shown to be reliable and accurate within the specified ranges of values for different realizations of uncertain groundwater parameters. One of the major concerns of the meta-model based integrated S-O approach is the uncertainty associated with the meta-model predictions. These prediction uncertainties, if not addressed properly, may propagate to the optimization procedures, and may deteriorate the optimality of the solutions. A standalone meta-model, when used within an optimal management model, may result in the optimization routine producing actually suboptimal solutions that may undermine the optimality of the groundwater extraction strategies. Therefore, this study proposes an ensemble approach to address the prediction uncertainties of meta-models. Ensemble is an approach to assimilate multiple similar or different algorithms or base learners (emulators). The basic idea of ensemble lies in developing a more reliable and robust prediction tool that incorporates each individual emulator's unique characteristic in order to predict future scenarios. Each individual member of the ensemble contains different input -output mapping functions. Based on their own mapping functions, these individual emulators provide varied predictions on the response variable. Therefore, the combined prediction of the ensemble is likely to be less biased and more robust, reliable, and accurate than that of any of the individual members of the ensemble. Performance of the ensemble meta-models is evaluated using an illustrative coastal aquifer study area. The results indicate that the meta-model based ensemble modelling approach is able to provide reliable solutions for a multilayered coastal aquifer management problem. Relative sea level rise, providing an additional saline water head at the seaside, has a significant impact on an increase in the salinization process of the coastal aquifers. Although excessive groundwater withdrawal is considered as the major cause of saltwater intrusion, relative sea level rise, in combination with the effect of excessive groundwater pumping, can exacerbate the already vulnerable coastal aquifers. This study incorporates the effects of relative sea level rise on the optimized groundwater extraction values for the specified management period. Variation of water concentrations in the tidal river and seasonal fluctuation of river water stage are also incorporated. Three meta-models are developed from the solution results of the numerical simulation model that simulates the coupled flow and solute transport processes in a coastal aquifer system. The results reveal that the proposed meta-models are capable of predicting density dependent coupled flow and solute transport patterns quite accurately. Based on the comparison results, the best meta-model is selected as a computationally cheap substitute of the simulation model in the coupled S-O based saltwater intrusion management model. The performance of the proposed methodology is evaluated for an illustrative multilayered coastal aquifer system in which the effect of climate change induced sea level rise is incorporated for the specified management period. The results show that the proposed saltwater intrusion management model provides acceptable, accurate, and reliable solutions while significantly improving computational efficiency in the coupled S-O methodology. The success of the developed management strategy largely depends on how accurately the prescribed management policy is implemented in real life situations. The actual implementation of a prescribed management strategy often differs from the prescribed planned strategy due to various uncertainties in predicting the consequences, as well as practical constraints, including noncompliance with the prescribed strategy. This results in actual consequences of a management strategy differing from the intended results. To bring the management consequences closer to the intended results, adaptive management strategies can be sequentially modified at different stages of the management horizon using feedback measurements from a deigned monitoring network. This feedback information can be the actual spatial and temporal concentrations resulting from the implementation of actual management strategy. Therefore, field-scale compliance of the developed coastal aquifer management strategy is a crucial aspect of an optimally designed groundwater extraction policy. A 3-D compliance monitoring network design methodology is proposed in this study in order to develop an adaptive and sequentially modified management policy, which aims to improve optimal and justifiable use of groundwater resources in coastal aquifers. In the first step, an ensemble meta-model based multiple objective prescriptive model is developed using a coupled S-O approach in order to derive a set of Pareto optimal groundwater extraction strategies. Prediction uncertainty of meta-models is addressed by utilizing a weighted average ensemble using Set Pair Analysis. In the second step, a monitoring network is designed for evaluating the compliance of the implemented strategies with the prescribed management goals due to possible uncertainties associated with field-scale application of the proposed management policy. Optimal monitoring locations are obtained by maximizing Shannon's entropy between the saltwater concentrations at the selected potential locations. Performance of the proposed 3-D sequential compliance monitoring network design is assessed for an illustrative multilayered coastal aquifer study area. The performance evaluations show that sequential improvements of optimal management strategy are possible by utilizing saltwater concentrations measurements at the proposed optimal compliance monitoring locations. The integrated S-O approach is used to develop a saltwater intrusion management model for a real world coastal aquifer system in the Barguna district of southern Bangladesh. The aquifer processes are simulated by using a 3-D finite element based combined flow and solute transport numerical code. The modelling and management of seawater intrusion processes are performed based on very limited hydrogeological data. The model is calibrated with respect to hydraulic heads for a period of five years from April 2010 to April 2014. The calibrated model is validated for the next three-year period from April 2015 to April 2017. The calibrated and partially validated model is then used within the integrated S-O approach to develop optimal groundwater abstraction patterns to control saltwater intrusion in the study area. Computational efficiency of the management model is achieved by using a MARS based meta-model approximately emulating the combined flow and solute transport processes of the study area. This limited evaluation demonstrates that a planned transient groundwater abstraction strategy, acquired as solution results of a meta-model based integrated S-O approach, is a useful management strategy for optimized water abstraction and saltwater intrusion control. This study shows the capability of the MARS meta-model based integrated S-O approach to solve real-life complex management problems in an efficient manner

Modelling of geotechnical structures using multi-variate adaptive regression spline (MARS) and genetic programming (GP)

The soil is considered as a complex material produced by the weathering of solid rock. Due to its uncertain behavior, modeling the behavior of such materials is complex by using more traditional forms of mechanistic based engineering methods like analytical and finite element methods etc. Very often it is difficult to develop theoretical/statistical models due to the complex nature of the problem and uncertainty in soil parameters. These are situations where data driven approach has been found to more appropriate than model oriented approach. To take care of such problems in artificial intelligence (AI) techniques has been developed in the computational methods. Though AI techniques has proved to have the superior predictive ability than other traditional methods for modeling complex behavior of geotechnical engineering materials, still it is facing some criticism due to the lack of transparency, knowledge extraction and model uncertainty. To overcome this problem there are developments of improvised AI techniques. Different AI techniques as ‘black box’ i.e artificial neural network (ANN), ‘grey box’ i.e Genetic programming (GP) and ‘white box’ i.e multivariate adaptive regression spline (MARS) depending upon its transparency and knowledge extraction. Here, in this study of GP and MARS ‘grey box’ and ‘white box’ AI techniques are applied to some geotechnical problems such as prediction of lateral load capacity of piles in clay, pull-out capacity of ground anchor, factor of safety of slope stability analysis and ultimate bearing capacity of shallow foundations.. Different statistical criteria are used to compare the developed GP and MARS models with other AI models like ANN and support vector machine (SVM) models. It was observed that for the problems considered in the present study, the MARS and GP model are found to be more efficient than ANN and SVM model and the model equations are also found to be more comprehensive

Arctic cold spring mineralogy as an indicator of spring deposits, water, and habitable environments on Mars

Springs exist in many terrestrial settings and have supported microbial communities throughout Earth’s history. There is mounting evidence for spring deposits on Mars from Noachian age to present, implying that water may be circulating in Mars’ subsurface despite current cold, arid conditions. Current datasets for most of Mars are limited to mineralogy via orbital spectroscopy and geomorphology from visual imagery and laser altimetry. Much is known about terrestrial spring morphology, but few springs exist in Mars analogue settings, and of those, few have been investigated for mineralogy. This study reports on two sets of cold spring sites in the Canadian arctic where permafrost, frigid temperatures, and arid conditions approximate Mars’ environment. The first are acidic cold seeps forming the jarosite-rich Golden Deposit (GD) in Northwest Territories, Canada. The second are perennial saline spring systems associated with three gypsum/anhydrite diapirs on Axel Heiberg Island, Nunavut, Canada: Wolf spring (WS; also known as Lost Hammer), Colour Peak (CP), and Gypsum Hill (GH) springs. Reflectance spectra were collected to determine how similar spring deposits would appear from Mars orbit, and compared to X-ray diffraction (XRD) and inductively coupled plasma emission spectrometry (ICP-ES) results. Spectrally, GD appears to consist only of jarosite, but XRD analysis also detected natrojarosite, hydronium jarosite, goethite, quartz, clays, and hematite. In samples from WS gypsum and mirabilite are spectrally visible via strong features in the ranges of all current Mars orbital datasets, owing to their hydrated states. Halite and thenardite are spectrally detectable, but the strongest absorption features lay outside the ranges of the highest resolution Mars datasets. XRD analysis of WS samples detected primarily halite, thenardite, gypsum, and mirabilite, with other sulfates and elemental sulfur. Results from this study are applied in the search for potential spring sites on Mars, and an ovoid jarosite-rich deposit in Mawrth Vallis is proposed as a landing site for future Mars missions. Jarosite, gypsum, and thenardite facilitate preservation of organic material, and thus suspected spring deposits containing these sulfate minerals are excellent candidates in the search for evidence of life on Mars

Rock mass classification for predicting environmental impact of blasting on tropically weathered rock

Tropical climate and post tectonic impact on the rock mass cause severe and deep weathering in complex rock formations. The uniqueness of tropical influence on the geoengineering properties of rock mass leads to significant effects on blast performance especially in the developmental stage. Different rock types such as limestone and granite exhibit different weathering effects which require special attention for classifying rock mass for blastability purpose. Rock mass classification systems have been implemented for last century for various applications to simplify complexity of rock mass. Several research studies have been carried out on rock mass and material properties for five classes of weathered rock- fresh, slightly, moderately, highly and completely weathered rock. There is wide variation in rock mass properties- heterogeneity and strength of weathered rocks in different weathering zones which cause environmental effects due to blasting. Several researchers have developed different techniques for prediction of air overpressure (AOp), peak particle velocity (PPV) and flyrock primarily for production blast. These techniques may not be suitable for prediction of blast performance in development benches in tropically weathered rock mass. In this research, blast monitoring program were carried out from a limestone quarry and two granite quarries. Due to different nature of properties, tropically weathered rock mass was classified as massive, blocky and fractured rock for simpler evaluation of development blast performance. Weathering Index (WI) is introduced based on porosity, water absorption and Point Load Index (PLI) strength properties of rock. Weathering index, porosity index, water absorption index and point load index ratio showed decreasing trend from massive to fractured tropically weathered rock. On the other hand, Block Weathering Index (BWI) was developed based on hypothetical values of exploration data and computational model. Ten blasting data sets were collected for analysis with blasting data varying from 105 to 166 per data set for AOp, PPV and flyrock. For granite, one data set each was analyzed for AOp and PPV and balance five data sets were analyzed for flyrock in granite by variation in input parameters. For prediction of blasting performance, varied techniques such as empirical equations, multivariable regression analysis (MVRA), hypothetical model, computational techniques (artificial intelligence-AI, machine learning- ML) and graphical charts. Measured values of blast performance was also compared with prediction techniques used by previous researchers. Blastability Index (BI), powder factor, WI are found suitable for prediction of all blast performance. Maximum charge per delay, distance of monitoring point are found to be critical factors for prediction of AOp and PPV. Stiffness ratio is found to be a crucial factor for flyrock especially during developmental blast. Empirical equations developed for prediction of PPV in fractured, blocky, and massive limestone showed R2 (0.82, 0.54, and 0.23) respectively confirming that there is an impact of weathering on blasting performance. Best fit equation was developed with multivariable regression analysis (MVRA) with measured blast performance values and input parameters. Prediction of flyrock for granite with MVRA for massive, blocky and fractured demonstrated R2 (0.8843, 0.86, 0.9782) respectively. WI and BWI were interchangeably used and results showed comparable results. For limestone, AOp analysed with model PSO-ANN showed R2(0.961); PPV evaluated with model FA-ANN produced R2 (0.966). For flyrock in granite with prediction model GWO-ANFIS showed R2 (1) The same data set was analysed by replacing WI with BWI showed equivalent results. Model ANFIS produced R2 (1). It is found the best performing models were PSO-ANN for AOp, FA-ANN for PPV and GWO-ANFIS for flyrock. Prediction charts were developed for AOp, PPV and flyrock for simple in use by site personnel. Blastability index and weathering index showed variation with reclassified weathering zones – massive, blocky and fractured and they are useful input parameters for prediction of blast performance in tropically weathered rock

Unveiling buried aeolian landscapes: reconstructing a late Holocene dune environment using 3D ground-penetrating radar

Across the UK, sandy beaches and dunes protect coastal infrastructure from waves and extreme water levels during large-scale storms, while providing important habitats and recreational opportunities. Understanding their long-term evolution is vital in managing their condition in a changing climate. Recently, ground-penetrating radar (GPR) methods have grown in popularity in geomorphological applications, yielding centimetre-scale resolution images of near-surface stratigraphy and structure, thus allowing landscape evolution to be reconstructed. Additionally, abrupt changes in palaeo-environments can be visualized in three dimensions. Although often complemented by core data, GPR allows interpretations to be extended into areas with minimal ground-truth control. Nonetheless, GPR data interpretation can be non-intuitive and ambiguous, and radargrams may not initially resemble the expected subsurface geometry. Interpretation can be made yet more onerous when handling the large 3D data volumes that are facilitated with modern GPR technology. Here we describe the development of novel semi-automated GPR feature-extraction tools, based on ‘edge detection’ and ‘thresholding’ methods, which detect regions of increased GPR reflectivity which can be applied to aid in the reconstruction of a range Quaternary landscapes. Since reflectivity can be related to lithological and/or pore fluid changes, the 3D architecture of the palaeo-landscape can be reconstructed from the features extracted from a geophysical dataset. We present 500 MHz GPR data collected over a buried Holocene coastal dune system in North Wales, UK, now reclaimed for use as an airfield. Core data from the site, reaching a maximum depth 2 m, suggest rapid vertical changes from sand to silty-organic units, and GPR profiles suggest similar lateral complexity. By applying thresholding methods to GPR depth slices, these lateral complexities are effectively and automatically mapped. Furthermore, automatic extraction of the local reflection power yields a strong correlation with the depth variation of organic content, suggesting it is a cause of reflectivity contrast. GPR-interpolated analyses away from core control thus offer a powerful proxy for parameters derived from invasive core logging. The GPR data collected at Llanbedr airfield highlight a complex dune system to a depth of 2.8 m, probably deposited in several phases over ~700 years, similar to elsewhere in North Wales

Development of sustainable groundwater management methodologies to control saltwater intrusion into coastal aquifers with application to a tropical Pacific island country

Saltwater intrusion due to the over-exploitation of groundwater in coastal aquifers is a critical challenge facing groundwater-dependent coastal communities throughout the world. Sustainable management of coastal aquifers for maintaining abstracted groundwater quality within permissible salinity limits is regarded as an important groundwater management problem necessitating urgent reliable and optimal management methodologies. This study focuses on the development and evaluation of groundwater salinity prediction tools, coastal aquifer multi-objective management strategies, and adaptive management strategies using new prediction models, coupled simulation-optimization (S/O) models, and monitoring network design, respectively. Predicting the extent of saltwater intrusion into coastal aquifers in response to existing and changing pumping patterns is a prerequisite of any groundwater management framework. This study investigates the feasibility of using support vector machine regression (SVMR), an innovative artificial intelligence-based machine learning algorithm, to predict salinity at monitoring wells in an illustrative aquifer under variable groundwater pumping conditions. For evaluation purposes, the prediction results of SVMR are compared with well-established genetic programming (GP) based surrogate models. The prediction capabilities of the two learning machines are evaluated using several measures to ensure their practicality and generalisation ability. Also, a sensitivity analysis methodology is proposed for assessing the impact of pumping rates on salt concentrations at monitoring locations. The performance evaluations suggest that the predictive capability of SVMR is superior to that of GP models. The sensitivity analysis identifies a subset of the most influential pumping rates, which is used to construct new SVMR surrogate models with improved predictive capabilities. The improved predictive capability and generalisation ability of SVMR models, together with the ability to improve the accuracy of prediction by refining the dataset used for training, make the use of SVMR models more attractive. Coupled S/O models are efficient tools that are used for designing multi-objective coastal aquifer management strategies. This study applies a regional-scale coupled S/O methodology with a Pareto front clustering technique to prescribe optimal groundwater withdrawal patterns from the Bonriki aquifer in the Pacific Island of Kiribati. A numerical simulation model is developed, calibrated and validated using field data from the Bonriki aquifer. For computational feasibility, SVMR surrogate models are trained and tested utilizing input-output datasets generated using the flow and transport numerical simulation model. The developed surrogate models were externally coupled with a multi-objective genetic algorithm optimization (MOGA) model, as a substitute for the numerical model. The study area consisted of freshwater pumping wells for extracting groundwater. Pumping from barrier wells installed along the coastlines is also considered as a management option to hydraulically control saltwater intrusion. The objective of the multi-objective management model was to maximise pumping from production wells and minimize pumping from barrier wells (which provide a hydraulic barrier) to ensure that the water quality at different monitoring locations remains within pre-specified limits. The executed multi-objective coupled S/O model generated 700 Pareto-optimal solutions. Analysing a large set of Pareto-optimal solution is a challenging task for the decision-makers. Hence, the k-means clustering technique was utilized to reduce the large Pareto-optimal solution set and help solve the large-scale saltwater intrusion problem in the Bonriki aquifer. The S/O-based management models have delivered optimal saltwater intrusion management strategies. However, at times, uncertainties in the numerical simulation model due to uncertain aquifer parameters are not incorporated into the management models. The present study explicitly incorporates aquifer parameter uncertainty into a multi-objective management model for the optimal design of groundwater pumping strategies from the unconfined Bonriki aquifer. To achieve computational efficiency and feasibility of the management model, the calibrated numerical simulation model in the S/O model was is replaced with ensembles of SVMR surrogate models. Each SVMR standalone surrogate model in the ensemble is constructed using datasets from different numerical simulation models with different hydraulic conductivity and porosity values. These ensemble SVMR models were coupled to the MOGA model to solve the Bonriki aquifer management problem for ensuring sustainable withdrawal rates that maintain specified salinity limits. The executed optimization model presented a Pareto-front with 600 non-dominated optimal trade-off pumping solutions. The reliability of the management model, established after validation of the optimal solution results, suggests that the implemented constraints of the optimization problem were satisfied; i.e., the salinities at monitoring locations remained within the pre-specified limits. The correct implementation of a prescribed optimal management strategy based on the coupled S/O model is always a concern for decision-makers. The management strategy actually implemented in the field sometimes deviates from the recommended optimal strategy, resulting in field-level deviations. Monitoring such field-level deviations during actual implementation of the recommended optimal management strategy and sequentially updating the strategy using feedback information is an important step towards adaptive management of coastal groundwater resources. In this study, a three-phase adaptive management framework for a coastal aquifer subjected to saltwater intrusion is applied and evaluated for a regional-scale coastal aquifer study area. The methodology adopted includes three sequential components. First, an optimal management strategy (consisting of groundwater extraction from production and barrier wells) is derived and implemented for the optimal management of the aquifer. The implemented management strategy is obtained by solving a homogeneous ensemble-based coupled S/O model. Second, a regional-scale optimal monitoring network is designed for the aquifer system, which considers possible user noncompliance of a recommended management strategy and uncertainty in aquifer parameter estimates. A new monitoring network design is formulated to ensure that candidate monitoring wells are placed at high risk (highly contaminated) locations. In addition, a k-means clustering methodology is utilized to select candidate monitoring wells in areas representative of the entire model domain. Finally, feedback information in the form of salinity measurements at monitoring wells is used to sequentially modify pumping strategies for future time periods in the management horizon. The developed adaptive management framework is evaluated by applying it to the Bonriki aquifer system. Overall, the results of this study suggest that the implemented adaptive management strategy has the potential to address practical implementation issues arising due to user noncompliance, as well as deviations between predicted and actual consequences of implementing a management strategy, and uncertainty in aquifer parameters. The use of ensemble prediction models is known to be more accurate standalone prediction models. The present study develops and utilises homogeneous and heterogeneous ensemble models based on several standalone evolutionary algorithms, including artificial neural networks (ANN), GP, SVMR and Gaussian process regression (GPR). These models are used to predict groundwater salinity in the Bonriki aquifer. Standalone and ensemble prediction models are trained and validated using identical pumping and salinity concentration datasets generated by solving numerical 3D transient density-dependent coastal aquifer flow and transport numerical simulation models. After validation, the ensemble models are used to predict salinity concentration at selected monitoring wells in the modelled aquifer under variable groundwater pumping conditions. The predictive capabilities of the developed ensemble models are quantified using standard statistical procedures. The performance evaluation results suggest that the predictive capabilities of the standalone prediction models (ANN, GP, SVMR and GPR) are comparable to those of the groundwater variable-density flow and salt transport numerical simulation model. However, GPR standalone models had better predictive capabilities than the other standalone models. Also, SVMR and GPR standalone models were more efficient (in terms of computational training time) than other standalone models. In terms of ensemble models, the performance of the homogeneous GPR ensemble model was found to be superior to that of the other homogeneous and heterogeneous ensemble models. Employing data-driven predictive models as replacements for complex groundwater flow and transport models enables the prediction of future scenarios and also helps save computational time, effort and requirements when developing optimal coastal aquifer management strategies based on coupled S/O models. In this study, a new data-driven model, namely Group method for data handling (GMDH) approach is developed and utilized to predict salinity concentration in a coastal aquifer and, simultaneously, determine the most influential input predictor variables (pumping rates) that had the most impact onto the outcomes (salinity at monitoring locations). To confirm the importance of variables, three tests are conducted, in which new GMDH models are constructed using subsets of the original datasets. In TEST 1, new GMDH models are constructed using a set of most influential variables only. In TEST 2, a subset of 20 variables (10 most and 10 least influential variables) are used to develop new GMDH models. In TEST 3, a subset of the least influential variables is used to develop GMDH models. A performance evaluation demonstrates that the GMDH models developed using the entire dataset have reasonable predictive accuracy and efficiency. A comparison of the performance evaluations of the three tests highlights the importance of appropriately selecting input pumping rates when developing predictive models. These results suggest that incorporating the least influential variables decreases model accuracy; thus, only considering the most influential variables in salinity prediction models is beneficial and appropriate. This study also investigated the efficiency and viability of using artificial freshwater recharge (AFR) to increase fresh groundwater pumping rates from production wells. First, the effect of AFR on the inland encroachment of saline water is quantified for existing scenarios. Specifically, groundwater head and salinity differences at monitoring locations before and after artificial recharge are presented. Second, a multi-objective management model incorporating groundwater pumping and AFR is implemented to control groundwater salinization in an illustrative coastal aquifer system. A coupled SVMR-MOGA model is developed for prescribing optimal management strategies that incorporate AFR and groundwater pumping wells. The Pareto-optimal front obtained from the SVMR-MOGA optimization model presents a set of optimal solutions for the sustainable management of the coastal aquifer. The pumping strategies obtained as Pareto-optimal solutions with and without freshwater recharge shows that saltwater intrusion is sensitive to AFR. Also, the hydraulic head lenses created by AFR can be used as one practical option to control saltwater intrusion. The developed 3D saltwater intrusion model, the predictive capabilities of the developed SVMR models, and the feasibility of using the proposed coupled multi-objective SVMR-MOGA optimization model make the proposed methodology potentially suitable for solving large-scale regional saltwater intrusion management problems. Overall, the development and evaluation of various groundwater numerical simulation models, predictive models, multi-objective management strategies and adaptive methodologies will provide decision-makers with tools for the sustainable management of coastal aquifers. It is envisioned that the outcomes of this research will provide useful information to groundwater managers and stakeholders, and offer potential resolutions to policy-makers regarding the sustainable management of groundwater resources. The real-life case study of the Bonriki aquifer presented in this study provides the scientific community with a broader understanding of groundwater resource issues in coastal aquifers and establishes the practical utility of the developed management strategies

Elevated level of inhibin-α subunit is pro-tumourigenic and pro-metastatic and associated with extracapsular spread in advanced prostate cancer

The biological function of inhibin-α subunit (INHα) in prostate cancer (PCa) is currently unclear. A recent study associated elevated levels of INHα in PCa patients with a higher risk of recurrence. This prompted us to use clinical specimens and functional studies to investigate the pro-tumourigenic and pro-metastatic function of INHα. We conducted a cross-sectional study to determine a link between INHα expression and a number of clinicopathological parameters including Gleason score, surgical margin, extracapsular spread, lymph node status and vascular endothelial growth factor receptor-3 expression, which are well-established prognostic factors of PCa. In addition, using two human PCa cell lines (LNCaP and PC3) representing androgen-dependent and -independent PCa respectively, we investigated the biological function of elevated levels of INHα in advanced cancer. Elevated expression of INHα in primary PCa tissues showed a higher risk of PCa patients being positive for clinicopathological parameters outlined above. Over-expressing INHα in LNCaP and PC3 cells demonstrated two different and cell-type-specific responses. INHα-positive LNCaP demonstrated reduced tumour growth whereas INHα-positive PC3 cells demonstrated increased tumour growth and metastasis through the process of lymphangiogenesis. This study is the first to demonstrate a pro-tumourigenic and pro-metastatic function for INHα associated with androgen-independent stage of metastatic prostate disease. Our results also suggest that INHα expression in the primary prostate tumour can be used as a predictive factor for prognosis of PCa