Assessing and forecasting of groundwater level fluctuation in Joypurhat district, northwest Bangladesh, using wavelet analysis and ARIMA modeling

Abstract

Groundwater resource plays a crucial role for agricultural crop production and socio-economic development in some parts of the world including Bangladesh. Joypurhat district, the northwest part of Bangladesh, a crop production hub, is entirely dependent on groundwater irrigation. A precise assessment and prediction of groundwater level (GWL) can assist long-term GWR management, especially in drought-prone agricultural regions. Therefore, this study was carried out to identify trends and magnitude of GWL fluctuation (1980-2019) using the Modified Mann-Kendall test, Pettitt's Test, and Sen Slope estimators in the drought-prone Joypurhat district, northwest Bangladesh. Time-series data analysis was performed to forecast GWL from 2020 to 2050 using the Auto-Regressive Integrated Moving Average (ARIMA) model. The findings of the MMK test revealed a significant declining trend of GWL, and the trend turning points were identified in the years 1991, 1993, 1997, and 2004, respectively. Results also indicate that the declining rate of GWL varied from 0.104 m/yr to 0.159 m/yr and the average rate of GWL declination was 0.136 m/yr during 1980-2019. The outcomes of wavelet spectrum analysis depicted two significant periods of the declining trend in Khetlal and Akkelpur Upazilas. The results obtained from the optimal identified model ARIMA (2,1,0), indicating that GWL will decline at a depth of 13.76 m in 2050, and the average declination rate of GWL will be 0.143 m/yr in the study area. The predicted results showed a similar declining tendency of GWL from 2020 to 2050, suggesting a disquieting condition, particularly for Khetlal Upazila. This research would provide a practical approach for GWL assessment and prediction that could help decisionmakers implement long-term GWR management in the study area.

Machine Learning for Groundwater Storage Prediction: Leveraging Climatic Variables

“…An R 2 value of 0.80 for the period 2001-2022 underscores RF's impressive agreement between actual and predicted GWS values. This achievement outperforms conventional ARIMA models (R 2 = 0.75) in Joypurhat (Rashid et al, 2022) and Feedforward Neural Networks (R 2 < 0.80) employed in studies conducted around Bangladesh and China (J. Zhang et al, 2018), respectively.…”

Delineation of groundwater potential zones in Kotagarh block of Kandhamal District, Odisha through geospatial techniques

“…In an inaccessible terrain, integrated approach of remote sensing and GIS played a vital role in locating and managing the groundwater potential zone (Barik et al, 2016;Chatterjee et al, 2022Chatterjee et al, , 2023Chowdhury et al, 2010;Dar et al, 2010;Das et al, 1997;Harinarayana et al, 2000;Muralidhar et al, 2000;Nandi et al, 2017;Selvam et al, 2015;Singh et al, 2011;Tiwari & Rai, 1996). Hydro-geologists and remote sensing specialists can better analyze the remotely acquired data with the help of various GIS techniques (Rashid et al, 2022). Geomorphological, geological, and hydrogeological factors have been used to identify new groundwater potential zones as well as to address other issues relating to groundwater quality using a variety of remote sensing data and GIS approaches (Prasad et al, 2008).…”