Prediction of Ground Water Level using SVM-WOA Approach: A Case Study

Reliable and accurate estimation of Groundwater Level (GWL) fluctuations is essential and vital for sustainable water resources management. Due to uncertainties and interdependencies in hydro-geological processes, GWL prediction is complex by the fact that fluctuation of GWL is extremely nonlinear and non-stationary. Utilising novel methods for accurately predicting GWL is of vital significance in arid regions. In present work, Support Vector Machine (SVM), in combination with Whale Optimisation Algorithm (SVM-WOA), is applied to forecast GWL in Bhubaneswar region (Odisha University of Agricultural Technology). Three quantitative statistical performance assessment indices, coefficient of determination (R2), Mean Squared Error (MSE), and Wilmott Index (WI), is used to assess model performances. Based on the assessment with conventional SVM and RBFN models, the performance of hybrid SVM-WOA model is preeminent. SVM-WOA is capable of predicting nonlinear behavior of GWLs. Proposed modelling technique can be applied in different regions for proper management of groundwater resources and provides significant information, at a short time scale, to estimate variability in groundwater at local level

Methodology to Predict Daily Groundwater Levels by the Implementation of Machine Learning and Crop Models

The continuous decline of groundwater levels caused by variations in climatic conditions and crop water demands is an increased concern for the agricultural community. It is necessary to understand the factors that control these changes in groundwater levels so that we can better address declines and develop improved conservation practices that will lead to a more sustainable use of water. In this study, two machine learning techniques namely support vector regression (SVR) and the nonlinear autoregressive with exogenous inputs (NARX) neural network were implemented to predict daily groundwater levels in a well located in the Mississippi Delta Region (MDR). Results of the NARX model indicate that a Bayesian regularization algorithm with two hidden nodes and 100 time delays was the best architecture to forecast groundwater levels. In another study, the SVR and the NARX model were compared for the prediction of groundwater withdrawal and recharge periods separately. Results from this study showed that input data classified by seasons lead to incremental improvements in the model accuracy, and that the SVR was the most efficient machine learning model with a Mean Squared Error (MSE) of 0.00123 m for the withdrawal season. Analysis of input variables such as previous daily groundwater levels (Gw), precipitation (Pr), and evapotranspiration (ET) showed that the combination of Gw+Pr provides the optimal set for groundwater prediction and that ET degraded the modeling performance, especially during recharge seasons. Finally, the CROPGRO-Soybean crop model was used to simulate the impacts of different volumes of irrigation on the crop height and yield, and to generate the daily irrigation requirements for soybean crops in the MDR. Four irrigation threshold scenarios (20%, 40%, 50% and 60%) were obtained from the CROGRO-Soybean model and used as inputs in the SVR to evaluate the predicted response of daily groundwater levels to different irrigation demands. This study demonstrated that conservative irrigation management, by selecting a low irrigation threshold, can provide good yields comparable to what is produced by a high volume irrigation management practice. Thus, lower irrigation volumes can have a big impact on decreasing the amount of groundwater withdrawals, while still maintaining comparable yields

Methodology to Predict Daily Groundwater Levels by the Implementation of Machine Learning and Crop Models

The continuous decline of groundwater levels caused by variations in climatic conditions and crop water demands is an increased concern for the agricultural community. It is necessary to understand the factors that control these changes in groundwater levels so that we can better address declines and develop improved conservation practices that will lead to a more sustainable use of water. In this study, two machine learning techniques namely support vector regression (SVR) and the nonlinear autoregressive with exogenous inputs (NARX) neural network were implemented to predict daily groundwater levels in a well located in the Mississippi Delta Region (MDR). Results of the NARX model indicate that a Bayesian regularization algorithm with two hidden nodes and 100 time delays was the best architecture to forecast groundwater levels. In another study, the SVR and the NARX model were compared for the prediction of groundwater withdrawal and recharge periods separately. Results from this study showed that input data classified by seasons lead to incremental improvements in the model accuracy, and that the SVR was the most efficient machine learning model with a Mean Squared Error (MSE) of 0.00123 m for the withdrawal season. Analysis of input variables such as previous daily groundwater levels (Gw), precipitation (Pr), and evapotranspiration (ET) showed that the combination of Gw+Pr provides the optimal set for groundwater prediction and that ET degraded the modeling performance, especially during recharge seasons. Finally, the CROPGRO-Soybean crop model was used to simulate the impacts of different volumes of irrigation on the crop height and yield, and to generate the daily irrigation requirements for soybean crops in the MDR. Four irrigation threshold scenarios (20%, 40%, 50% and 60%) were obtained from the CROGRO-Soybean model and used as inputs in the SVR to evaluate the predicted response of daily groundwater levels to different irrigation demands. This study demonstrated that conservative irrigation management, by selecting a low irrigation threshold, can provide good yields comparable to what is produced by a high volume irrigation management practice. Thus, lower irrigation volumes can have a big impact on decreasing the amount of groundwater withdrawals, while still maintaining comparable yields

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

Strategic Environmental Assessment for Municipal Water Demand Based on Climate Change

Accurate urban water demand forecasting plays a key role in the planning and design of municipal water supply infrastructure. The reliable prediction of water demand is challenging for water companies, specifically when considering the implications of climate change (Zubaidi et al., 2018). Several studies have documented that weather variables drive water consumption in the short-term, and it enhances the accuracy of the prediction model when it is combined with socio-economic factors. However, the impact of climate change on the municipal water demand has yet to be challenged. To surmount this challenge, more research work is needed to accurately estimate the required quantity of water with increasing water demands. Recently, Artificial Neural Networks (ANNs) have been found to be an innovative approach to predict water demand. This PhD study aims to develop a novel methodology to forecast the impact of climate change on municipal water demands for a long-term time series based on the baseline period 1980-2010. It should be highlighted that, based on our knowledge, this is the first study of substantial duration, based on data collected from 1980-2010, which focuses on the associations between monthly climate change and municipal water consumption. A new approach is therefore proposed to quantifying municipal water demands through the assessment of climatic factors, using a combination of a Singular Spectrum Analysis (SSA) technique, three hybrid computational intelligence algorithms and an ANN model. These hybrid algorithms include a Lightning Search Algorithm (LSA-ANN), a Gravitational Search Algorithm (GSA-ANN) and Particle Swarm Optimisation (PSO-ANN). The SSA technique is adopted to decompose the time series of water consumption and climate variables to detect the stochastic signal for each time series. In the same context, the hybrid algorithms are used to find the best value of learning rate coefficient and the number of neurons in both hidden layers of the ANN model. Based on the performance of each hybrid algorithm, the most accurate and reliable water demand forecast model will be selected and used for estimating future water consumption. The considered environments of this study are applied in Australia and the United States from America for mitigating the uncertainty associated with the geographic location (the data of the United States of America was used to support the reliability of developing the municipal water demands prediction model). Furthermore, the Long Ashton Research Station Weather Generator (LARS-WG) model is utilised to simulate future climate factors over three periods (2011-2030, 2046-2065 and 2080-2099) based on the B1, A1B and A2 emission scenarios and seven General Circulation Models (GCMs). The future projection of these climate factors is applied directly to the impact model of water consumption to obtain the projected municipal water demand for different future periods and different greenhouse emission scenarios. The principal findings of this research are the following: from the model perspective, 1) the SSA is a powerful technique when used to remove the effect of socio-economic factors and noise, and detect the stochastic signal time series for water consumption. 2) The ANN model has better performance in term of optimising the correlation between observed and predicted water consumption when using the (LSA-ANN) algorithm. 3) The evaluation of the ANN model (using a validation data set) for Melbourne and Columbia Cities gives a correlation coefficient of 0.96 and 0.95, and the root mean square errors are 0.025 and 0.016 respectively. These findings indicate the capability of the proposed model to predict water demands with high accuracy in different continents. 4) The high performance of LARS-WG model results are found to be appropriate for the simulation of future climate variables. 5) The harmonisation between future monthly water demand (for the periods 2011-2030, 2046-2065 and 2080-2099) and stochastic signals of climate variables, relative to baseline period 1980-2010, emphasises the reliability of the present methodology. However, from the water demand perspective, the water percentage demand (WPD) are likely to rise in winter, drop in summer and fluctuate in both spring and autumn seasons for all periods and under all greenhouse emission scenarios. The results of WPD distribute between -3.5% and 3% for all periods and emission scenarios. The A2 scenario shows the highest and lowest values of WPDs compared to the A1B and B1 scenarios, in particular, in the 3rd period. The mean of seasonal WPD values shows that there is no dominant scenario as the best or the worst case of water demand over all future periods. The highest amount of seasonal demand happens in winter (A2 scenario, 3rd period), and the lowest amount of seasonal demand occurs in autumn (A1B scenario, 3rd period). In conclusion, this study facilitates the conception of the impact of climate change on municipal water demand from the baseline period 1980-2010

Appropriate Wisdom, Technology, and Management toward Environmental Sustainability for Development

The protection and maintenance of environmental resources for future generations require responsible interaction between humans and the environment in order to avoid wasting natural resources. According to an ancient Native American proverb, “We do not inherit the Earth from our ancestors; we borrow it from our children.” This indigenous wisdom has the potential to play a significant role in defining environmental sustainability. Recent technological advances could sustain humankind and allow for comfortable living. However, not all of these advancements have the potential to protect the environment for future generations. Developing societies and maintaining the sustainability of the ecosystem require appropriate wisdom, technology, and management collaboration. This book is a collection of 19 important articles (15 research articles, 3 review papers, and 1 editorial) that were published in the Special Issue of the journal Sustainability entitled “Appropriate Wisdom, Technology, and Management toward Environmental Sustainability for Development” during 2021-2022.addresses the policymakers and decision-makers who are willing to develop societies that practice environmental sustainability, by collecting the most recent contributions on the appropriate wisdom, technology, and management regarding the different aspects of a community that can retain environmental sustainability

Computational intelligence image processing for precision farming on-site nitrogen analysis in plants

PhD ThesisNitrogen is one of the macronutrients which is essentially required by plants. To support the precision farming, it is important to analyse nitrogen status in plants in order to prevent excessive fertilisation as well as to reduce production costs. Image-based analysis has been widely utilised to estimate nitrogen content in plants. Such research, however, is commonly conducted in a controlled environment with artificial lighting systems. This thesis proposes three novel computational intelligence systems to evaluate nitrogen status in wheat plants by analysing plant images captured on field and are subject to variation in lighting conditions. In the first proposed method, a fusion of regularised neural networks (NN) has been employed to normalise plant images based on the RGB colour of the 24-patch Macbeth colour checker. The colour normalisation results are then optimised using genetic algorithm (GA). The regularised neural network has also been effectively utilised to distinguish wheat leaves from other unwanted parts. This method gives improved results compared to the Otsu algorithm. Furthermore, several neural networks with different number of hidden layer nodes are combined using committee machines and optimised by GA to estimate nitrogen content. In the second proposed method, the utilisation of regularised NN has been replaced by deep sparse extreme learning machine (DSELM). In general the utilisation of DSELM in the three research steps is as effective as that of the developed regularised NN as proposed in the first method. However, the learning speed of DSELM is extremely faster than the regularised NN and the standard backpropagation multilayer perceptron (MLP). In the third proposed method, a novel approach has been developed to fine tune the colour normalisation based on the nutrient estimation errors and analyse the effect of genetic algorithm based global optimisation on the nitrogen estimation results. In this method, an ensemble of deep learning MLP (DL-MLP) has been employed in the three research steps, i.e. colour normalisation, image segmentation and nitrogen estimation. The performance of the three proposed methods has been compared with the intrusive SPAD meter and the results show that all the proposed methods are superior to the SPAD based estimation. The nutrient estimation errors of the proposed methods are less than 3%, while the error using the renowned SPAD meter method is 8.48%. As a comparison, nitrogen prediction using other methods, i.e. Kawashima greenness index () and PCA-based greenness index () are also calculated. The prediction errors by means of and methods are 9.84% and 9.20%, respectively.Indonesia Ministry of Research, Technology and Higher Education and Jenderal Soedirman Univerist

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc