Potential Application of Advanced Computational Techniques in Prediction of Groundwater Resource of India

Malakar, Pragnaditya; Mukherjee, Abhijit; Sarkar, Sudeshna

doi:10.1007/978-981-10-3889-1_37

Cited by 5 publications

(6 citation statements)

References 27 publications

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“…The variations in the GWL data set may cause adverse effects on the model learning ability. Thus, the GWL data needs to be standardized to remove the scale dependency and provide stable convergence in model development . The GWL data are standardized ( x stand ) using mean ( x μ ) and standard deviation ( x σ ): A detailed flowchart of the methodology adapted in this study is shown in Figure .…”

Section: Methodsmentioning

confidence: 99%

“…Thus, the GWL data needs to be standardized to remove the scale dependency and provide stable convergence in model development. 29 The GWL data are standardized (x stand ) using mean (x μ ) and standard deviation (x σ ):…”

Section: Methodsmentioning

confidence: 99%

“…The GWL variations in the wells provide a more direct measure of the impact of groundwater development and information on aquifer dynamics. Thus, over the years, several AI-based modeling techniques (feed-forward neural network (FNN), recurrent neural network (RNN), support vector machine (SVM), convolutional neural network (CNN), long short-term memory network (LSTM), and others) have been applied by the researchers for groundwater resource prediction (and simulations) across the world. − …”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Forecasting of Groundwater Level Trends in India: Implications for Crop Production and Drinking Water Supply

et al. 2021

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Despite numerous studies in recent times, there is no consensus on the primary drivers for groundwater storage (GWS) changes over India. Thus, predicting future groundwater level trends seems remote. In this context, using Gravity Recovery and Climate Experiment (GRACE)-derived GWS, WaterGap model-based groundwater recharge (GWR), and groundwater withdrawal (GWW), we show that GWW exhibits a stronger dominance than GWR on GWS change over India. Furthermore, we developed feed-forward neural network (FNN), recurrent neural network (RNN), and deep learning-based long short-term memory network (LSTM) models using multidepth in situ observations from a dense network of monitoring wells (n = 5367, 1996−2018), to simulate and forecast groundwater levels (GWL) in India. The result demonstrates the better performance of LSTM (>84% of observation wells showing r > 0.6, RMSE n < 0.7) across India, outperforming both FNN and RNN in the testing period of 5 years (2014−2018). Our estimates also reveal that besides the prevailing long-term (1996−2018) statistically significant (p < 0.1) declining GWL trends in northwest India and the Ganges river basin, higher declining trends will potentially be observed in parts of north-central and south India in the forecasting period of 5 years (2019−2023). We envisage that the forecasting approach presented in the study can contribute toward an improved urban−rural drinking water supply and sustainable crop production for 1.3 billion people in India.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Forecasting of Groundwater Level Trends in India: Implications for Crop Production and Drinking Water Supply

et al. 2021

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“…In environmental engineering, GWL forecasting results in a specific (short-, medium-, and long-term) time horizon are important for the rational management of groundwater resources. Long-term predictions of the groundwater resources' quantity and their trends' analysis allow to assess the availability of resources in changing climatic (and socio-economic) conditions (Malakar et al 2018), particularly in areas suffering from water scarcity (Ibrahim et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Correlation approach in predictor selection for groundwater level forecasting in areas threatened by water deficits

Kajewska-Szkudlarek

Kubicz

Kajewski

2021

Journal of Hydroinformatics

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Reliable long-term groundwater level (GWL) prediction is essential to assess the availability of resources and the risk to drinking water supply in changing climatic and socio-economic conditions, especially in areas with water deficits. The modern approach in this area involves the use of machine learning methods. However, the greatest challenge in these methods lies in the optimization of input selection. The presented research concerns the selection of the best combination of predictors using the Hellwig method. It served as a preprocessing technique before GWL prediction using support vector regression (SVR) and multilayer perceptron (MLP) for three wells in the Greater Poland Province, where the largest water deficits occur, in the period 1975–2014. The results of this method were compared with those of the regression method, general regression model. For the case study under investigation, the Hellwig method found GWL at lags of −1 and −2 months, all precipitation from the current month, and delayed by −1 to −6 months, and past temperature at months −1, −3, −4 and −6 as the most informative input set. Such input led to a model accuracy of 0.003–0.022 for a mean squared error and r2 of >0.8. The results obtained with SVR were slightly better than those with MLP. Moreover, every well required an individual set of predictors, and additional meteorological inputs improved the models’ performance.

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“…GWL modeling based on ML has the unique ability to find the likely relationships between GWL and controlling hydro-climatic-anthropogenic variables without constructing knowledge-driven conceptual or physically-based models. Therefore, researchers have studied the performance of ML methods for GWL modeling in India and Bangladesh (Nayak et al, 2006;Nury et al, 2017;Malakar et al, 2018;Mukherjee and Ramachandran, 2018;Bhanja et al, 2019b;Sun et al, 2019;Yadav et al, 2019 and the references therein) and other parts of the world (Coulibaly et al, 2001; 60 Feng et al, 2008;Sun, 2013;Nourani and Mousavi, 2016;Sun et al, 2016;Yoon et al, 2016;Barzegar et al, 2017;Ebrahimi and Rajaee, 2017;Wunsch et al, 2018;Chen et al, 2019;Lee et al, 2019 and the references therein). Most of these studies used popular methods like Artificial Neural Network (ANN), hybrid-ANN, Adaptive neuro-fuzzy inference system (ANFIS), Support Vector Machine (SVM) and few others using a wide range of frequency and temporal data on past GWLs, satellite observations derived groundwater storage (GWS), Normalized difference vegetation index (NDVI)), meteorological variables, river discharge, 65 variables on groundwater use and few dummy variables to simulate and/or predict GWLs.…”

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confidence: 99%

Importance of spatial and depth-dependent drivers in groundwater level modeling through machine learning

Malakar

Mukherjee

Bhanja

et al. 2020

Preprint

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Abstract. The water and food security of South Asia is embedded in the groundwater resources of the transboundary aquifer system of Indus-Ganges-Brahmaputra-Meghna (IGBM) rivers, which has been subjected to diverse natural and anthropogenic triggers. Thus, understanding the relative importance of such triggers in groundwater level change and developing a prediction framework is essential to sustain future stress. Although a number of studies on groundwater level prediction and simulation exist in the literature, characterization of predictive performances of groundwater level modeling using a large network of ground-based observations (n = 2303) is not yet reported. To identify the spatial and depth-wise predictors influence, here, we used linear regression based dominance analysis and machine learning methods (Support Vector Machine and Artificial Neural network) on long term (1985–2015) GWLs and/or climatic variables in the parts of IGBM basin aquifers. The results from the dominance analysis show that groundwater level change is primarily influenced by abstraction and population in most of the IGBM, whereas in the Brahmaputra basin, precipitation exhibits greater influence. Our results show a large proportion of the observation wells (n > 50 % for ANN and n > 65 % for SVM) demonstrate good correlation (r > 0.6, p 0.65), and normalized root mean square error (RMSEn

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Potential Application of Advanced Computational Techniques in Prediction of Groundwater Resource of India

Cited by 5 publications

References 27 publications

Deep Learning-Based Forecasting of Groundwater Level Trends in India: Implications for Crop Production and Drinking Water Supply

Deep Learning-Based Forecasting of Groundwater Level Trends in India: Implications for Crop Production and Drinking Water Supply

Correlation approach in predictor selection for groundwater level forecasting in areas threatened by water deficits

Importance of spatial and depth-dependent drivers in groundwater level modeling through machine learning

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