干旱区生态环境知识资源中心(Arid): Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India

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DOI	10.1016/j.scitotenv.2021.147319
	Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India
	Mohapatra, Janaki B.; Jha, Piyush; Jha, Madan K.; Biswal, Sabinaya
通讯作者	Jha, MK (corresponding author), Indian Inst Technol Kharagpur, AgFE Dept, Kharagpur 721302, W Bengal, India.
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2021
卷号	785
英文摘要	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 groundwatermonitoring 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. (c) 2021 Elsevier B.V. All rights reserved.
英文关键词	Groundwater-level prediction Data-driven modeling ANFIS DNN SVM Agro-Ecological Zone Machine learning artificial intelligence techniques
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000659455300004
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; FUZZY INFERENCE SYSTEM ; SUPPORT VECTOR MACHINES ; LEVEL FLUCTUATIONS ; INTELLIGENCE MODELS ; ANFIS MODELS ; ANN ; SIMULATION ; REGIONS ; PLAIN
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368503
作者单位	[Mohapatra, Janaki B.; Jha, Madan K.; Biswal, Sabinaya] Indian Inst Technol Kharagpur, AgFE Dept, Kharagpur 721302, W Bengal, India; [Jha, Piyush] Univ Waterloo, 200 Univ Ave West, Waterloo, ON N2L 3G1, Canada
推荐引用方式 GB/T 7714	Mohapatra, Janaki B.,Jha, Piyush,Jha, Madan K.,et al. Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India[J],2021,785.
APA	Mohapatra, Janaki B.,Jha, Piyush,Jha, Madan K.,&Biswal, Sabinaya.(2021).Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India.SCIENCE OF THE TOTAL ENVIRONMENT,785.
MLA	Mohapatra, Janaki B.,et al."Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India".SCIENCE OF THE TOTAL ENVIRONMENT 785(2021).