Assessment of groundwater level variation by ANN and ARIMA modeling coupled with Mann-Kendall trend analysis and GIS spatial interpolation
- 1Department of Civil Engineering, European University of Bangladesh, 2/4 Gabtoli, Mirpur, Dhaka-1216, BANGLADESH
- 2Sub-divisional engineer, Bangladesh Water Development Board
Abstract
Groundwater is the largest liquid freshwater reservoir and a critical resource for drinking water, agriculture, and ecosystem sustainability. In the Rajshahi Division of north-western Bangladesh, intensive groundwater use and recurring droughts necessitate an accurate assessment of groundwater level (GWL). This study presents an integrated framework combining statistical, machine learning, and spatial tools for a comprehensive assessment of GWL fluctuations. A total of 1008 artificial neural network (ANN) models and 768 autoregressive integrated moving average (ARIMA) models were developed and evaluated under univariate (UV) and multivariate (MV) settings to determine optimal model functions and lagged effects. Spatial GWL and trend variations were generated using ArcGIS. Results show that ANN models incorporating rainfall (RF) data improved predictive accuracy (e.g., Bogura, MSE 0.01, Correlation Coefficient R = 0.98, NSE = 0.97, KGE = 0.96, R 2 = 0.97) compared to UV models. ARIMA models slightly outperformed ANN due to linear patterns, while ANN captured nonlinear dynamics. Trend analysis indicated significant GWL increases at Nawabganj. This framework provides a robust, transferable approach for evaluating GWL, enabling sustainable irrigation planning, drought risk management, identification of vulnerable zones, and evidence-based groundwater management in other hydrologically stressed regions.
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BibTeX
@article{hoque2026assessment,
title = {{Assessment of groundwater level variation by ANN and ARIMA modeling coupled with Mann-Kendall trend analysis and GIS spatial interpolation}},
author = {Md Abrarul Hoque and Rubinoor Islam and Farhana Kabir and Abdullah All Noman and Md. Abul Kalam Azad and Md. Iftakhar-Ul Islam},
journal = {H2Open Journal},
year = {2026},
month = may,
doi = {10.1016/j.htopen.2026.100034},
url = {https://doi.org/10.1016/j.htopen.2026.100034},
note = {CC BY},
}
RIS
TY - JOUR
TI - Assessment of groundwater level variation by ANN and ARIMA modeling coupled with Mann-Kendall trend analysis and GIS spatial interpolation
AU - Md Abrarul Hoque
AU - Rubinoor Islam
AU - Farhana Kabir
AU - Abdullah All Noman
AU - Md. Abul Kalam Azad
AU - Md. Iftakhar-Ul Islam
PY - 2026
DA - 2026/05/17
JO - H2Open Journal
DO - 10.1016/j.htopen.2026.100034
UR - https://doi.org/10.1016/j.htopen.2026.100034
AB - Groundwater is the largest liquid freshwater reservoir and a critical resource for drinking water, agriculture, and ecosystem sustainability. In the Rajshahi Division of north-western Bangladesh, intensive groundwater use and recurring droughts necessitate an accurate assessment of groundwater level (GWL). This study presents an integrated framework combining statistical, machine learning, and spatial tools for a comprehensive assessment of GWL fluctuations. A total of 1008 artificial neural network (ANN) models and 768 autoregressive integrated moving average (ARIMA) models were developed and evaluated under univariate (UV) and multivariate (MV) settings to determine optimal model functions and lagged effects. Spatial GWL and trend variations were generated using ArcGIS. Results show that ANN models incorporating rainfall (RF) data improved predictive accuracy (e.g., Bogura, MSE 0.01, Correlation Coefficient R = 0.98, NSE = 0.97, KGE = 0.96, R 2 = 0.97) compared to UV models. ARIMA models slightly outperformed ANN due to linear patterns, while ANN captured nonlinear dynamics. Trend analysis indicated significant GWL increases at Nawabganj. This framework provides a robust, transferable approach for evaluating GWL, enabling sustainable irrigation planning, drought risk management, identification of vulnerable zones, and evidence-based groundwater management in other hydrologically stressed regions.
SN - 2616-6518
ER -