The response of groundwater to multiple concerning drivers and its future: a study on Birbhum District, West Bengal, India

• Main Authors: Niladri Das, Subhasish Sutradhar, Ranajit Ghosh, Prolay Mondal, Sadikul Islam
• Format: Article
• Language: English
• Published: SpringerOpen 2021-04-01
• Series: Applied Water Science
• Subjects: Multiple drivers; ANN model; Cluster analysis; Sustainability; Multilinear regression model
• Online Access: https://doi.org/10.1007/s13201-021-01410-8
• ISSN: 2190-5487; 2190-5495

Abstract Groundwater and its upcoming crisis are the present-day concern of the scientist. This research mainly focuses on responses of groundwater dynamicity to some important drivers, viz. agricultural yield, groundwater irrigated area, groundwater draft, landuse/landcover, and stage of development. The result of this study has been done under three sections. In the first section, the spatiality of groundwater has been discussed where it has been noticed that the western side of the district groundwater level is near the surface due to low drafting and low agricultural yield. Moreover, hard rock geology in the western part disappoints the drilling process. On the eastern part, rich alluvial soil influences high agricultural yield hence groundwater level lowering down rapidly. In the second section, the nature of groundwater levels has been analyzed through the boxplot, and cluster diagram, where boxplots have been drawn over different geological facies, which depicts groundwater is highly fluctuating in hard clay geology. For example, high agricultural intensity and high groundwater draft is the characteristic feature of hard clay geology. The dendrogram in cluster analysis represents a homogeneous groundwater level fluctuating station in three different time series. Last section deals with the future of groundwater level where an artificial neural network (ANN) model has been applied to extract the predicted groundwater level for 2030. This type of environmental analysis, such as groundwater fluctuations in relation to different sensitive parameters and the use of a machine learning model, would aid potential researchers and communities in making wise groundwater use decisions.