Complex network analysis of groundwater level in Sina Basin, Maharashtra, India

Vikram Bharti (), Thendiyath Roshni (), Madan Kumar Jha (), Mohammad Ali Ghorbani () and Osama Ragab Abdelaziz Ibrahim ()
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Vikram Bharti: National Institute of Technology Patna
Thendiyath Roshni: National Institute of Technology Patna
Madan Kumar Jha: Indian Institute of Technology Kharagpur
Mohammad Ali Ghorbani: University of Tabriz
Osama Ragab Abdelaziz Ibrahim: Alexandria University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2024, vol. 26, issue 7, No 66, 18017-18032

Abstract: Abstract Monitoring groundwater level provides sufficient information on groundwater quantity and quality and is vital in effective management of water resources. This study applies transfer entropy coupled with directed-weighted complex network for the analysis of groundwater levels in the Sina river-basin, Maharashtra, India. All observation wells present in study area have been classified into five clusters using canopy clustering method. The direction and weight of the links of this complex network have been obtained by employing transfer entropy. Seasonal groundwater level data for pre-monsoon (May) and post-monsoon (October) were obtained from centre for groundwater board, Pune, Maharashtra for the period 1990–2009. Data analysis show that both pre-monsoon and post-monsoon groundwater level show significant decreasing trend. The proposed methodology determines the directional relationships between the selected observation wells of different clusters. It recognizes the most influenced well by using node strength and directed clustering coefficients. For each cluster, clustering coefficients and in-strength and out-strength have been calculated. Clustering coefficients for the selected wells of cluster 0 are 1, 1, 1, 0.6844 and 0.6342 which indicates Cluster 0 emerges as the strongest cluster. Similarly, clustering coefficients for cluster 4 are 0.6604, 0.6540, 0.3095, 0.2616 and 0 which means cluster 4 is the weakest among all the clusters formed. Clustering coefficients obtained for all clusters indicate that all wells within a cluster are forming clusters among themselves, however, some are strong whereas others are weak clusters. Therefore, transfer entropy can be effectively applied to groundwater and results obtained from it can be used for forecasting and water resources management.

Keywords: Complex network; Transfer entropy; Entropy; Clustering; Clustering coefficients; Node strength (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10668-023-03375-x

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