Analysis and Predictive Modeling of River Water Level in Surma-Meghna River

Abstract: This study explores the hydrological dynamics of the Meghna-Surma basin in Kishoregonj, Bangladesh, with the objective of analyzing and forecasting river water levels. Using 30 years of daily data (1995–2024) on rainfall, discharge, and water levels, the study first applies statistical regression to examine correlations among the variables. Subsequently, the ARIMA (Autoregressive Integrated Moving Average) model is employed to predict future water level trends up to 2029. Findings reveal a strong correlation between rainfall and discharge, and the ARIMA (4,1,3) model demonstrated satisfactory short-term forecasting performance. The study highlights the practicality of using ARIMA for real-time water level prediction in data-constrained environments. These insights are crucial for flood risk mitigation, agricultural planning, and infrastructure development in river-prone regions of Bangladesh.

River water level forecasting, Meghna-Surma basin, ARIMA model, Hydrological correlation, Flood risk management

1. Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1–4), 28–40. https://doi.org/10.1016/j.jhydrol.2011.06.013 [Google Scholar] [Crossref]

2. Akter, S., Howladar, M. F., Ahmed, Z., & Chowdhury, T. R. (2019). The rainfall and discharge trends of Surma River area in North-eastern part of Bangladesh: an approach for understanding the impacts of climatic change. Environmental Systems Research, 8(1), 28. https://doi.org/10.1186/s40068-019-0156-y [Google Scholar] [Crossref]

3. Box, G. (2013). Box and Jenkins: Time Series Analysis, Forecasting and Control. In A Very British Affair (pp. 161–215). Palgrave Macmillan UK. https://doi.org/10.1057/9781137291264_6 [Google Scholar] [Crossref]

4. Gao, H., Bohn, T. J., Podest, E., McDonald, K. C., & Lettenmaier, D. P. (2011). On the causes of the shrinking of Lake Chad. Environmental Research Letters, 6(3), 034021. https://doi.org/10.1088/1748-9326/6/3/034021 [Google Scholar] [Crossref]

5. Getirana, A., Kumar, S. V., Konapala, G., & Ndehedehe, C. E. (2021). Impacts of Fully Coupling Land Surface and Flood Models on the Simulation of Large Wetlands’ Water Dynamics: The Case of the Inner Niger Delta. Journal of Advances in Modeling Earth Systems, 13(5). https://doi.org/10.1029/2021MS002463 [Google Scholar] [Crossref]

6. Kaburuan, E. R., Mutu Manikam, R., Kucharov Olimjon Ruzimurodovich, O. K., & Rianggi Boyo, A. (2024). Comparison of LSTM and ARIMA Algorithms in Predicting Water Levels in Jakarta. 2024 International Conference on Orange Technology (ICOT), 1–8. https://doi.org/10.1109/ICOT64290.2024.10936979 [Google Scholar] [Crossref]

7. Khodakhah, H., Aghelpour, P., & Hamedi, Z. (2022). Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH. Environmental Science and Pollution Research, 29(15), 21935–21954. https://doi.org/10.1007/s11356-021-17443-0 [Google Scholar] [Crossref]

8. Kumar, A., Mondal, S., & Lal, P. (2022). Analysing frequent extreme flood incidences in Brahmaputra basin, South Asia. PLOS ONE, 17(8), e0273384. https://doi.org/10.1371/journal.pone.0273384 [Google Scholar] [Crossref]

9. Mahamat Nour, A., Vallet‐Coulomb, C., Gonçalves, J., Sylvestre, F., & Deschamps, P. (2021). Rainfall-discharge relationship and water balance over the past 60 years within the Chari-Logone sub-basins, Lake Chad basin. Journal of Hydrology: Regional Studies, 35, 100824. https://doi.org/10.1016/j.ejrh.2021.100824 [Google Scholar] [Crossref]

10. Mojid, M. A. (2020). Climate change-induced challenges to sustainable development in Bangladesh. IOP Conference Series: Earth and Environmental Science, 423(1), 012001. https://doi.org/10.1088/1755-1315/423/1/012001 [Google Scholar] [Crossref]

11. Noor, F., Haq, S., Rakib, M., Ahmed, T., Jamal, Z., Siam, Z. S., Hasan, R. T., Adnan, M. S. G., Dewan, A., & Rahman, R. M. (2022). Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network. Water, 14(4), 612. https://doi.org/10.3390/w14040612 [Google Scholar] [Crossref]

12. Rahman, M. M., Chowdhury, M. M. I., Al Amran, M. I. U., Malik, K., Abubakar, I. R., Aina, Y. A., Hasan, M. A., Rahman, M. S., & Rahman, S. M. (2024). Impacts of climate change on food system security and sustainability in Bangladesh. Journal of Water and Climate Change, 15(5), 2162–2187. https://doi.org/10.2166/wcc.2024.631 [Google Scholar] [Crossref]

13. Rashid, A., Alamgir, M., Ahmed, M. T., Salam, R., Islam, A. R. Md. T., & Islam, A. (2022). Assessing and forecasting of groundwater level fluctuation in Joypurhat district, northwest Bangladesh, using wavelet analysis and ARIMA modeling. Theoretical and Applied Climatology, 150(1–2), 327–345. https://doi.org/10.1007/s00704-022-04160-y [Google Scholar] [Crossref]

14. Tran, T. T., Duy Nguyen, L., Bao Pham, Q., Thanh Tran, T., Ngoc Hoai, P., Thi Thanh Huyen, P., Phuong Dong, N., Hoang Hieu, H., & Thu Hien, N. (2022). Long Short-Term Memory (LSTM) neural networks for short-term water level prediction in Mekong river estuaries. Article in Songklanakarin Journal of Science and Technology, 44(4), 1057–1066. https://doi.org/10.14456/sjst-psu.2022.138 [Google Scholar] [Crossref]

15. Uddin, M., Akter, S., Uddin, M., & Diganta, M. (2018). Trend Analysis Variations and Relation Between Discharge and Rainfall: a Study on Kushiyara River. Journal of Environmental Science and Natural Resources, 10(2), 121–132. https://doi.org/10.3329/jesnr.v10i2.39025 [Google Scholar] [Crossref]

16. Wang, W.-C., Chau, K.-W., Cheng, C.-T., & Qiu, L. (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 374(3–4), 294–306. https://doi.org/10.1016/j.jhydrol.2009.06.019 [Google Scholar] [Crossref]

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