Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India

Abstract

Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg-Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to 1846 S. Mohanty et al. that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well.

Impact of temperature changes on groundwater levels and irrigation costs in a groundwater-dependent agricultural region in Northwest Bangladesh

“…Therefore, empirical models are often suggested for hydrological modeling in data-poor regions (Shortridge et al, 2016). Recent studies have reported the impressive predictive accuracy of empirical models based on machine learning methods in groundwater level simulation (Behzad et al, 2010;Mohanty et al, 2010;Shortridge et al, 2016). Among the machine learning methods, SVM performs better in groundwater level simulations because it can model highly non-linear relationships (Behzad et al, 2010;Sudheer et al, 2011;Raghavendra and Deka, 2015;Tapak et al, 2014).…”

Probability Analysis of the Water Table and Driving Factors Using a Multidimensional Copula Function

“…Reliable prediction methods of the water table play significant roles in terms of groundwater planning and comprehensive management [2]. Over the years, scholars have applied different methods to study the water table, including the Linear Regression Method [3,4], Clustering Method [5], ARIMA Model [6], Genetic Programming Method, Neutral Network Method [7], Wavelet Approach [8], and SVM (Support Vector Machine) method [9], as well as the joint application of several methods [10,11].…”

“…Spring irrigation is divided into three stages: the jointing stage, heading stage, and the filling stage. In terms of the irrigation volume, the fixed irrigation volume of winter irrigation is 9 × 10 4 m 3 /km 2 to 10.5 × 10 4 m 3 /km 2 , and that of the spring irrigation is 6 × 10 4 m 3 /km 2 to 7.5 × 10 4 m 3 /km 2 . The total annual irrigation volume is less than 18 × 10 4 m 3 /km 2 to 22.5 × 10 4 m 3 /km 2 .…”

Comparative Evaluation of ANN- and SVM-Time Series Models for Predicting Freshwater-Saltwater Interface Fluctuations

“…Recently, in the field of hydrology and hydrogeology, research on the application of time series models-based on machine learning techniques such as an artificial neural network (ANN) and a support vector machine (SVM) to prediction of water resources variations-have been increased; Zealand et al [11] utilized the ANN for forecasting short term stream flow of the Winnipeg River system in Canada; Akhtar et al [12] applied ANN to river flow forecasting at Ganges river; Hu et al [13] explored new measures for improving the generalization ability of the ANN for the prediction of the rainfall-runoff; Coulibaly et al [14] and Mohanty et al [15] examined the performance of ANN for the prediction of groundwater level (GWL) fluctuations; Coppola et al [16] used the ANN for the prediction of GWL under variable pumping conditions; Liong and Sivapragasam [17], and Yu et al [18] employed the SVM for the prediction of the flood stage; Asefa et al [19] used the SVM for designing GWL monitoring networks; Gill et al [20] assessed the effect of missing data on the performance of ANN and SVM models for GWL prediction; Yoon et al [21] used ANN and SVM for long-term GWL forecast.…”