Monitoring and Assessment of Environmental Quality in Coastal Ecosystems

Coastal ecosystems are dynamic, complex, and often fragile transition environments between land and oceans. They are exclusive habitats for a broad range of living organisms, functioning as havens for biodiversity and providing several important ecological services that link terrestrial, freshwater, and marine environments. Humans living in coastal zones have been strongly dependent on these ecosystems as a source of food, physical protection against storms and advancing sea, and a range of human activities that generate economic income. Notwithstanding, the intensification of human activities in coastal areas of the recent decades, as well as the global climatic changes and coastal erosion processes of the present, have had detrimental impacts on these environments. Maintaining the structural and functional integrity of these environments and recovering an ecological balance or mitigating disturbances in systems under the influence of such stressors are complex tasks, only possible through the implementation of monitoring programs and by assessing their environmental quality. In this book, distinct approaches to environmental quality monitoring and assessment of coastal environments are presented, focused on abiotic and biotic compartments, and using tools that range from ecological levels of organization to the sub-organismal and the ecosystem levels

Modelling activated sludge wastewater treatment plants using artificial intelligence techniques (fuzzy logic and neural networks)

Activated sludge process (ASP) is the most commonly used biological wastewater treatment system. Mathematical modelling of this process is important for improving its treatment efficiency and thus the quality of the effluent released into the receiving water body. This is because the models can help the operator to predict the performance of the plant in order to take cost-effective and timely remedial actions that would ensure consistent treatment efficiency and meeting discharge consents. However, due to the highly complex and non-linear characteristics of this biological system, traditional mathematical modelling of this treatment process has remained a challenge. This thesis presents the applications of Artificial Intelligence (AI) techniques for modelling the ASP. These include the Kohonen Self Organising Map (KSOM), backpropagation artificial neural networks (BPANN), and adaptive network based fuzzy inference system (ANFIS). A comparison between these techniques has been made and the possibility of the hybrids between them was also investigated and tested. The study demonstrated that AI techniques offer viable, flexible and effective modelling methodology alternative for the activated sludge system. The KSOM was found to be an attractive tool for data preparation because it can easily accommodate missing data and outliers and because of its power in extracting salient features from raw data. As a consequence of the latter, the KSOM offers an excellent tool for the visualisation of high dimensional data. In addition, the KSOM was used to develop a software sensor to predict biological oxygen demand. This soft-sensor represents a significant advance in real-time BOD operational control by offering a very fast estimation of this important wastewater parameter when compared to the traditional 5-days bio-essay BOD test procedure. Furthermore, hybrids of KSOM-ANN and KSOM-ANFIS were shown to result much more improved model performance than using the respective modelling paradigms on their own.Damascus Universit

Contour ridge modelling using fuzzy logic and process based approaches for improved rainwater harvesting

A thesis submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements of the degree of Doctor of Philosophy. Johannesburg, February 2017Rainwater harvesting is used as a way of improving crop yields in rain fed agriculture by capturing excess rainfall and storing it in-situ or in reservoirs for use during dry spells. Contour ridges are one of the many rainwater harvesting technologies that are used although little is known about their effectiveness. Contour ridges harvest runoff generated in the cropped field upstream of the ridges. The traditional contour ridge type in Zimbabwe was introduced by the government in the 1950s to control soil erosion through safely draining away runoff from cropped fields and is commonly referred to as graded contour (GC) ridges. In the 1990s the country experienced severe and more frequent droughts leading stakeholders to experiment on contour ridges that retain the runoff instead of draining it away which are known as dead level contour (DLC) ridges. There was therefore the need to find out if there are benefits derived from this change and assess conditions under which benefits would be experienced. Previous studies have shown that rainwater harvested by contour ridges can improve water availability in downstream fields. However these studies did not investigate the conditions under which such benefits are realised. In addition no attempt to model water harvesting by contour ridges have been made in Zimbabwe while the contour ridges are widely being used for soil and water conservation. This research investigated the effect of contour ridges by comparing soil moisture between plots with DLC and GC ridges using plots with no contours as a control. Experimental work was carried out in Zhulube, in Matebeleland South Province of Zimbabwe. Matebeleland South Province falls within the semi-arid area in which rainfall is characterised by mid-season dry spells leading to frequent crop failure. In addition, the area often receives high rainfall intensities leading to soil erosion and sedimentation of rivers. DLC and GC ridges were constructed in farmers’ fields where maize crops were planted. Soil moisture measurements were done using a micro gopher soil moisture profiler while runoff plots were used to measure runoff generation. A fuzzy model was developed using data from this experiment and a previous study in Masvingo Province of Zimbabwe to simulate runoff generation at field scale while a process based water balance model was also developed to simulate soil moisture changes within the root zone of the cropped area. The results from this study indicate that DLC are effective in clay and loamy soils where runoff generation is significant and not in sandy soils due to insignificant generation of runoff under the rainfall regimes of semi-arid areas. Fuzzy logic was found to be a useful method of incorporating uncertainty in modelling runoff at field scale. A mass water balance model developed on process based principles was able to model soil moisture in the root zone reasonably well (NSE =0.55 to 0.66 and PBIAS=-1.3% to 6.1%) and could help to predict the water dynamics in contour ridged areas as would be required in determining the suitable dimensions and spacing of contour ridges. Further research is required to improve the fuzzy component of the model for estimation of runoff when more data becomes available. In addition experiments to validate methods of estimating macro pore fluxes and lateral transfer of water from the contour ridge channel to the downslope field are also recommended. The model structure can be improved by adopting the representative elementary watershed approaches to include momentum and energy balances in addition to mass balance that was used in this study.MT201

Analysis of FMRI Exams Through Unsupervised Learning and Evaluation Index

In the last few years, the clustering of time series has seen significant growth and has proven effective in providing useful information in various domains of use. This growing interest in time series clustering is the result of the effort made by the scientific community in the context of time data mining. For these reasons, the first phase of the thesis focused on the study of the data obtained from fMRI exams carried out in task-based and resting state mode, using and comparing different clustering algorithms: SelfOrganizing map (SOM), the Growing Neural Gas (GNG) and Neural Gas (NG) which are crisp-type algorithms, a fuzzy algorithm, the Fuzzy C algorithm, was also used (FCM). The evaluation of the results obtained by using clustering algorithms was carried out using the Davies Bouldin evaluation index (DBI or DB index). Clustering evaluation is the second topic of this thesis. To evaluate the validity of the clustering, there are specific techniques, but none of these is already consolidated for the study of fMRI exams. Furthermore, the evaluation of evaluation techniques is still an open research field. Eight clustering validation indexes (CVIs) applied to fMRI data clustering will be analysed. The validation indices that have been used are Pakhira Bandyopadhyay Maulik Index (crisp and fuzzy), Fukuyama Sugeno Index, Rezaee Lelieveldt Reider Index, Wang Sun Jiang Index, Xie Beni Index, Davies Bouldin Index, Soft Davies Bouldin Index. Furthermore, an evaluation of the evaluation indices will be carried out, which will take into account the sub-optimal performance obtained by the indices, through the introduction of new metrics. Finally, a new methodology for the evaluation of CVIs will be introduced, which will use an ANFIS model

A computationally efficient Kalman filter based estimator for updating look-up tables applied to NOx estimation in diesel engines

No-x estimation in diesel engines is an up-to-date problem but still some issues need to be solved. Raw sensor signals are not fast enough for real-time use while control-oriented models suffer from drift and aging. A control-oriented gray box model based on engine maps and calibrated off-line is used as benchmark model for No-x estimation. Calibration effort is important and engine data-dependent. This motivates the use of adaptive look-up tables. In addition to, look-up tables are often used in automotive control systems and there is a need for systematic methods that can estimate or update them on-line. For that purpose, Kalman filter (KF) based methods are explored as having the interesting property of tracking estimation error in a covariance matrix. Nevertheless, when coping with large systems, the computational burden is high, in terms of time and memory, compromising its implementation in commercial electronic control units. However look-up table estimation has a structure, that is here exploited to develop a memory and computationally efficient approximation to the KF, named Simplified Kalman filter (SKF). Convergence and robustness is evaluated in simulation and compared to both a full KF and a minimal steady-state version, that neglects the variance information. SKF is used for the online calibration of an adaptive model for No-x estimation in dynamic engine cycles. Prediction results are compared with the ones of the benchmark model and of the other methods. Furthermore, actual online estimation of No-x is solved by means of the proposed adaptive structure. Results on dynamic tests with a diesel engine and the computational study demonstrate the feasibility and capabilities of the method for an implementation in engine control units. (C) 2013 Elsevier Ltd. All rights reserved.Guardiola, C.; Pla Moreno, B.; Blanco-Rodriguez, D.; Eriksson, L. (2013). A computationally efficient Kalman filter based estimator for updating look-up tables applied to NOx estimation in diesel engines. Control Engineering Practice. 21(11):1455-1468. doi:10.1016/j.conengprac.2013.06.015S14551468211

Approximate Reasoning in Hydrogeological Modeling

The accurate determination of hydraulic conductivity is an important element of successful groundwater flow and transport modeling. However, the exhaustive measurement of this hydrogeological parameter is quite costly and, as a result, unrealistic. Alternatively, relationships between hydraulic conductivity and other hydrogeological variables less costly to measure have been used to estimate this crucial variable whenever needed. Until this point, however, the majority of these relationships have been assumed to be crisp and precise, contrary to what intuition dictates. The research presented herein addresses the imprecision inherent in hydraulic conductivity estimation, framing this process in a fuzzy logic framework. Because traditional hydrogeological practices are not suited to handle fuzzy data, various approaches to incorporating fuzzy data at different steps in the groundwater modeling process have been previously developed. Such approaches have been both redundant and contrary at times, including multiple approaches proposed for both fuzzy kriging and groundwater modeling. This research proposes a consistent rubric for the handling of fuzzy data throughout the entire groundwater modeling process. This entails the estimation of fuzzy data from alternative hydrogeological parameters, the sampling of realizations from fuzzy hydraulic conductivity data, including, most importantly, the appropriate aggregation of expert-provided fuzzy hydraulic conductivity estimates with traditionally-derived hydraulic conductivity measurements, and utilization of this information in the numerical simulation of groundwater flow and transport

Uncertainty Analysis of Multiple Hydrologic Models Using the Bayesian Model Averaging Method

Since Bayesian Model Averaging (BMA) method can combine the forecasts of different models together to generate a new one which is expected to be better than any individual model’s forecast, it has been widely used in hydrology for ensemble hydrologic prediction. Previous studies of the BMA mostly focused on the comparison of the BMA mean prediction with each individual model’s prediction. As BMA has the ability to provide a statistical distribution of the quantity to be forecasted, the research focus in this study is shifted onto the comparison of the prediction uncertainty interval generated by BMA with that of each individual model under two different BMA combination schemes. In the first BMA scheme, three models under the same Nash-Sutcliffe efficiency objective function are, respectively, calibrated, thus providing three-member predictions ensemble for the BMA combination. In the second BMA scheme, all three models are, respectively, calibrated under three different objective functions other than Nash-Sutcliffe efficiency to obtain nine-member predictions ensemble. Finally, the model efficiency and the uncertainty intervals of each individual model and two BMA combination schemes are assessed and compared