Evaluating performance of meta-heuristic algorithms and decision tree models in simulating water level variations of dams’ piezometers

Monitoring the seepage, particularly the piezometric water level in the dams, is of special importance in hydraulic engineering. In the present study, piezometric water levels in three observation piezometers at the left bank of Jiroft Dam structure (located in Kerman province, Iran) were simulated using soft computing techniques and then compared using the measured data. For this purpose, the input data, including inflow, evaporation, reservoir water level, sluice gate outflow, outflow, dam total outflow, and piezometric water level, were used. Modeling was performed using multiple linear regression method as well as soft computing methods including regression decision tree, classification decision tree, and three types of artificial neural networks (with Levenberg-Marquardt, particle swarm optimization, PSO, and harmony search learning algorithms, HS). The results of the present study indicated no absolute superiority for any of the methods over others. For the first piezometer the ANN-PSO indicates better performance (correlation coefficient, R=0.990). For the second piezometer ANN-PSO shows better results with R=0.945. For the third piezometers MLR with R=0.945 and ANN-HS with R=0.949 indicate better performance than other methods. Furthermore, Mann-Whitney statistical analysis at confidence levels of 95% and 99% indicated no significant difference in terms of the performance of the applied models used in this study

High Embankment Dam Stability Analysis Using Artificial Neural Networks

Regular surveillance, data acquisition, and visual observation of high embankment dams are extremely important for the stability analysis of these structures. The stability issues that could occur during a dam\u27s lifetime are mainly related to slope instability and internal erosion. The aim of continuous dam security monitoring and field measurement is to identify priority flow paths in the dam body, i.e. cracks and the erosion process. A key parameter for embankment dam stability assessment is the pore water pressure (PWP) response in the clay core. Increasing pore water pressure results in shear strength reduction and can cause dam instability. In this paper, four different models based on artificial neural networks will be developed for pore water pressure prediction in an embankment dam clay core, based on meteorological, hydrological, and geotechnical data. These models will be compared and the model that gives the smallest prediction error will be presented. In the light of climate change, the main objective of this paper is to find the model that can be used for embankment dam stability prediction in extreme weather events

Applying advanced data analytics and machine learning to enhance the safety control of dams

The protection of critical engineering infrastructures is vital to today’s so- ciety, not only to ensure the maintenance of their services (e.g., water supply, energy production, transport), but also to avoid large-scale disasters. Therefore, technical and financial efforts are being continuously made to improve the safety control of large civil engineering structures like dams, bridges and nuclear facilities. This con- trol is based on the measurement of physical quantities that characterize the struc- tural behavior, such as displacements, strains and stresses. The analysis of monitor- ing data and its evaluation against physical and mathematical models is the strongest tool to assess the safety of the structural behavior. Commonly, dam specialists use multiple linear regression models to analyze the dam response, which is a well- known approach among dam engineers since the 1950s decade. Nowadays, the data acquisition paradigm is changing from a manual process, where measurements were taken with low frequency (e.g., on a weekly basis), to a fully automated process that allows much higher frequencies. This new paradigm escalates the potential of data analytics on top of monitoring data, but, on the other hand, increases data quality issues related to anomalies in the acquisition process. This chapter presents the full data lifecycle in the safety control of large-scale civil engineering infrastructures (focused on dams), from the data acquisition process, data processing and storage, data quality and outlier detection, and data analysis. A strong focus is made on the use of machine learning techniques for data analysis, where the common multiple linear regression analysis is compared with deep learning strategies, namely recur- rent neural networks. Demonstration scenarios are presented based on data obtained from monitoring systems of concrete dams under operation in Portugal.info:eu-repo/semantics/acceptedVersio

Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models

The Applications of Soft Computing Methods for Seepage Modeling: A Review

In recent times, significant research has been carried out into developing and applying soft computing techniques for modeling hydro-climatic processes such as seepage modeling. It is necessary to properly model seepage, which creates groundwater sources, to ensure adequate management of scarce water resources. On the other hand, excessive seepage can threaten the stability of earthfill dams and infrastructures. Furthermore, it could result in severe soil erosion and consequently cause environmental damage. Considering the complex and nonlinear nature of the seepage process, employing soft computing techniques, especially applying pre-post processing techniques as hybrid methods, such as wavelet analysis, could be appropriate to enhance modeling efficiency. This review paper summarizes standard soft computing techniques and reviews their seepage modeling and simulation applications in the last two decades. Accordingly, 48 research papers from 2002 to 2021 were reviewed. According to the reviewed papers, it could be understood that regardless of some limitations, soft computing techniques could simulate the seepage successfully either through groundwater or earthfill dam and hydraulic structures. Moreover, some suggestions for future research are presented. This review was conducted employing preferred reporting items for systematic reviews and meta-analyses (PRISMA) method

Applications of machine learning to water resources management: A review of present status and future opportunities

Data availability: No data was used for the research described in the article.The corrected proof will be replaced by version of record in due course.Copyright © 2024 The Authors. Water is the most valuable natural resource on earth that plays a critical role in the socio-economic development of humans worldwide. Water is used for various purposes, including, but not limited to, drinking, recreation, irrigation, and hydropower production. The expected population growth at a global scale, coupled with the predicted climate change-induced impacts, warrants the need for proactive and effective management of water resources. Over the recent decades, machine learning tools have been widely applied to various water resources management-related fields and have often shown promising results. Despite the publication of several review articles on machine learning applications in water-related fields, this review paper presents for the first time a comprehensive review of machine learning techniques applied to water resources management, focusing on the most recent achievements. The study examines the potential for advanced machine learning techniques to improve decision support systems in the various sectors within the realm of water resources management, which includes groundwater management, streamflow forecasting, water distribution systems, water quality and wastewater treatment, water demand and consumption, hydropower and marine energy, water drainage systems, and flood management and defence. This study provides an overview of the state-of-the-art machine learning approaches to the water industry and how they can be used to ensure water supply sustainability, quality, and flood and drought mitigation. This review covers the most recent related studies to provide the most recent snapshot of machine learning applications in the water industry. Overall, LSTM networks have been proven to exhibit reliable performance, often outperforming ANN models, traditional machine learning models, and established physics-based models. Hybrid ML techniques have exhibited great forecasting accuracy across all water-related fields, often showing superior computational power over traditional ANNs architectures. In addition to purely data-driven models, physical-based hybrid models have also been developed to improve prediction performance. These efforts further demonstrate that Machine learning can be a powerful practical tool for water resources management. It provides insights, predictions, and optimisation capabilities to help enhance sustainable water use and management and improve socio-economic development, healthy ecosystems and human existence.EPSRC project reference 2339403 to S. Sayed and A. Ahmed

Neural network and analytical modeling of slope stability.

A semi-analytical method is developed for analysis of slope stability involving cohesive and non-cohesive soils. For sandy slopes, a planar slip surface is employed. For clayey slopes, circular slip surfaces are employed including Toe Failure, Face Failure and Base Failure resulting from different locations of a hard stratum. Earthquake effects are considered in an approximate manner in terms of seismic coefficient-dependent forces. The proposed method can be viewed as an extension of the method of slices, but it provides a more accurate treatment of the forces because they are represented in an integral form. Also, the minimum factor of safety is obtained by using the Powell's optimization technique rather than by a trial and error approach used commonly. The results (factor of safety) from the proposed semi-analytical method developed in this study are compared with the solutions by the Bishop method (1952) and the finite element method, and satisfactory agreements are obtained. The proposed method is simpler and more straightforward than the Bishop method and the finite element method. Also, it is found to be as good as or better than traditional slope stability analysis methods.An artificial neural network is also introduced in this study, as an alternate approach, for modeling slope stability. The proposed neural network model is a two-layer recurrent neural network (RNN) with a sigmoid hidden layer and a linear output layer. The model is developed by using data from 124 slopes collected for this study. The input variables include the parameters that contribute to the failure of a slope and include the height of a slope, the inclination of slope, the height of water level, the height of tension cracks at crest of slope, the depth of firm base, horizontal and vertical seismic coefficients, the unit weight of soil, the cohesion of soil, the friction angle of soil, the thickness of each layer, and the pore water pressure ratio which is defined as the ratio of the pore water pressure to the overburden pressure for a given layer. The output layer is a single linear neuron---the factor of safety of a slope. Training is performed on the 104 slope data randomly selected from the 124 slopes and prediction or evaluation is based on the remaining 20 slopes. Statistical analyses performed show that the results from the proposed RNN model are closer to the finite element method than to the Bishop method and the proposed semi-analytical method. A separate RNN model is developed to determine circular slip surfaces by retraining the proposed neural network model with three neurons in the output layer, namely the coordinates of the center and the radius of the circular slip surface. In comparison with the proposed semi-analytical method, the proposed RNN model is found to be more effective in representing relatively complex slopes with layered soils and/or pore water pressures

Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction

The main aim of structural safety control is the multiple assessments of the expected dam behaviour based on models and the measurements and parameters that characterise the dam’s response and condition. In recent years, there is an increase in the use of data-based models for the analysis and interpretation of the structural behaviour of dams. Multiple Linear Regression is the conventional, widely used approach in dam engineering, although interesting results have been published based on machine learning algorithms such as artificial neural networks, support vector machines, random forest, and boosted regression trees. However, these models need to be carefully developed and properly assessed before their application in practice. This is even more relevant when an increase in users of machine learning models is expected. For this reason, this paper presents extensive work regarding the verification and validation of data-based models for the analysis and interpretation of observed dam’s behaviour. This is presented by means of the development of several machine learning models to interpret horizontal displacements in an arch dam in operation. Several validation techniques are applied, including historical data validation, sensitivity analysis, and predictive validation. The results are discussed and conclusions are drawn regarding the practical application of data-based models

Prediction of River Discharge by Using Gaussian Basis Function

For design of water resources engineering related project such as hydraulic structures like dam, barrage and weirs river discharge data is vital. However, prediction of river discharge is complicated by variations in geometry and boundary roughness. The conventional method of estimation of river discharge tends to be inaccurate because river discharge is nonlinear but the method is linear. Therefore, an alternative method to overcome problem to predict river discharge is required. Soft computing technique such as artificial neural network (ANN) was able to predict nonlinear parameter such as river discharge. In this study, prediction of river discharge in Pari River is predicted using soft computing technique, specifically gaussian basis function. Water level raw data from year 2011 to 2012 is used as input. The data divided into two section, training dataset and testing dataset. From 314 data, 200 are allocated as training data and the remaining 100 are used as testing data. After that, the data will be run by using Matlab software. Three input variables used in this study were current water level, 1-antecendent water level, and 2-antecendent water level. 19 numbers of hidden neurons with spread value of 0.69106 was the best choice which creates the best result for model architecture after numbers of trial. The output variable was river discharge. Performance evaluation measures such as root mean square error, mean absolute error, correlation of efficiency (CE) and coefficient of determination (R2) was used to indicate the overall performance of the selected network. R2 for training dataset was 0.983 which showed predicted discharge is highly correlated with observed discharge value. However, testing stage performance is decline from training stage as R2 obtained was 0.775 consequently presence of outliers have affect scattering of whole data of testing and resulted in less accuracy as the R2 obtained much lower compared to training dataset. This happened because less number of input loaded into testing than training. RMSE and MSE recorded for training much lower than testing indicated that the better the performance of the model since the error is lesser. The comparison of with other types of neural network showed that Gaussian basis function is recommended to be used for river discharge prediction in Pari river