Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India

Janaki B Mohapatra; Piyush Jha; Madan K Jha; Sabinaya Biswal

doi:10.1016/j.scitotenv.2021.147319

Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India

Sci Total Environ. 2021 Sep 1:785:147319. doi: 10.1016/j.scitotenv.2021.147319. Epub 2021 Apr 24.

Authors

Janaki B Mohapatra¹, Piyush Jha², Madan K Jha³, Sabinaya Biswal¹

Affiliations

¹ AgFE Department, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India.
² University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada.
³ AgFE Department, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India. Electronic address: madan@agfe.iitkgp.ac.in.

PMID: 33957597
DOI: 10.1016/j.scitotenv.2021.147319

Abstract

In the 21st century, groundwater depletion is posing a serious threat to humanity throughout the world, particularly in developing nations. India being the largest consumer of groundwater in the world, dwindling groundwater storage has emerged as a serious concern in recent years. Consequently, the judicious and efficient management of vital groundwater resources is one of the grand challenges in India. Groundwater modeling is a promising tool to develop sustainable management strategies for the efficient utilization of this treasured resource. This study demonstrates a pragmatic framework for predicting seasonal groundwater levels at a large scale using real-world data. Three relatively powerful Machine Learning (ML) techniques viz., ANFIS (Adaptive Neuro-Fuzzy Inference System), Deep Neural Network (DNN) and Support Vector Machine (SVM) were employed for predicting seasonal groundwater levels at the country scale using in situ groundwater-level and pertinent meteorological data of 1996-2016. ANFIS, DNN and SVM models were developed for 18 Agro-Ecological Zones (AEZs) of India and their efficacy was evaluated using suitable statistical and graphical indicators. The findings of this study revealed that the DNN model is the most proficient in predicting seasonal groundwater levels in most AEZs, followed by the ANFIS model. However, the prediction ability of the three models is 'moderate' to 'very poor' in 3 AEZs ['Western Plain and Kutch Peninsula' in Western India, and 'Deccan Plateau (Arid)' and 'Eastern Ghats and Deccan Plateau' in Southern India]. It is recommended that groundwater-monitoring network and data acquisition systems be strengthened in India in order to ensure efficient use of modeling techniques for the sustainable management of groundwater resources.

Keywords: ANFIS; Agro-Ecological Zone; DNN; Data-driven modeling; Groundwater-level prediction; Machine learning/artificial intelligence techniques; SVM.