An AI-driven River Salinity Forecast and Advisory System for Climate Resilient Agriculture in Coastal Bangladesh
River salinity has been a critical factor influencing agricultural productivity in the polder systems of coastal Bangladesh, where sluice gates are used to regulate water inflows. Timely sluice gate operations during the early and late monsoon periods are essential for the successful rice cultivatio...
| Autores principales: | , , , , , |
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| Formato: | Resumen |
| Lenguaje: | Inglés |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/179448 |
| _version_ | 1855540390064553984 |
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| author | Matheswaran, Karthikeyan Behera, Abhijit Sena, Dipaka Ranjan Jampani, Mahesh Hasib, Md Raqubul Mondal, Manoranjan K. |
| author_browse | Behera, Abhijit Hasib, Md Raqubul Jampani, Mahesh Matheswaran, Karthikeyan Mondal, Manoranjan K. Sena, Dipaka Ranjan |
| author_facet | Matheswaran, Karthikeyan Behera, Abhijit Sena, Dipaka Ranjan Jampani, Mahesh Hasib, Md Raqubul Mondal, Manoranjan K. |
| author_sort | Matheswaran, Karthikeyan |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | River salinity has been a critical factor influencing agricultural productivity in the polder systems of coastal Bangladesh, where sluice gates are used to regulate water inflows. Timely sluice gate operations during the early and late monsoon periods are essential for the successful rice cultivation and subsequent dry season crops to get high yields. Farmers, agricultural extension agents, and water management groups (WMGs) require timely and location specific salinity information to make informed decisions on water management. Existing hydrological forecasts in the region primarily focus on climate extremes, floods and droughts, with limited operational tools for river salinity forecasting. To address this gap, an AI-based river salinity advisory system has been developed and piloted in two polders in the Khulna region. The system forecasts temporal windows during which river salinity remains within acceptable thresholds for agricultural water use, particularly at the start and end of the monsoon seasons. Given the limited availability of long-term salinity observations, the forecasting framework uses river discharge in the Rupsa River as a proxy. A multivariate Long Short-Term Memory (LSTM) and Random Forest models were trained using historical salinity and discharge data from the Rupsa River, CHIRPS rainfall estimates, and upstream discharge measurements at the Hardinge Bridge on the Ganges River to estimate salinity windows at key polder intake points. The salinity advisory for farmers issued for July 2025 forecasted the lowering of the salinity window between the 9th and 14th of July 2025. Validation of forecasts issued in July 2025 for Polders 34/2P and 30 showed strong performance, with Nash–Sutcliffe Efficiency (NSE) values exceeding 0.80 and per cent Bias (PBIAS) under 10% for the forecasted salinity windows. The system has been developed in collaboration with national research institutions, local government agencies, and WMGs, and is accessible through a user-friendly dashboard and mobile interface. This initiative demonstrates the potential of AI to enhance irrigation advisory services in climate vulnerable coastal regions. The approach offers a scalable model for integrating machine learning into water resource management in other data-scarce deltaic settings. |
| format | Abstract |
| id | CGSpace179448 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | CGSpace1794482026-01-07T04:55:29Z An AI-driven River Salinity Forecast and Advisory System for Climate Resilient Agriculture in Coastal Bangladesh Matheswaran, Karthikeyan Behera, Abhijit Sena, Dipaka Ranjan Jampani, Mahesh Hasib, Md Raqubul Mondal, Manoranjan K. artificial intelligence salinity management forecasting advisory services climate resilience agriculture coastal areas River salinity has been a critical factor influencing agricultural productivity in the polder systems of coastal Bangladesh, where sluice gates are used to regulate water inflows. Timely sluice gate operations during the early and late monsoon periods are essential for the successful rice cultivation and subsequent dry season crops to get high yields. Farmers, agricultural extension agents, and water management groups (WMGs) require timely and location specific salinity information to make informed decisions on water management. Existing hydrological forecasts in the region primarily focus on climate extremes, floods and droughts, with limited operational tools for river salinity forecasting. To address this gap, an AI-based river salinity advisory system has been developed and piloted in two polders in the Khulna region. The system forecasts temporal windows during which river salinity remains within acceptable thresholds for agricultural water use, particularly at the start and end of the monsoon seasons. Given the limited availability of long-term salinity observations, the forecasting framework uses river discharge in the Rupsa River as a proxy. A multivariate Long Short-Term Memory (LSTM) and Random Forest models were trained using historical salinity and discharge data from the Rupsa River, CHIRPS rainfall estimates, and upstream discharge measurements at the Hardinge Bridge on the Ganges River to estimate salinity windows at key polder intake points. The salinity advisory for farmers issued for July 2025 forecasted the lowering of the salinity window between the 9th and 14th of July 2025. Validation of forecasts issued in July 2025 for Polders 34/2P and 30 showed strong performance, with Nash–Sutcliffe Efficiency (NSE) values exceeding 0.80 and per cent Bias (PBIAS) under 10% for the forecasted salinity windows. The system has been developed in collaboration with national research institutions, local government agencies, and WMGs, and is accessible through a user-friendly dashboard and mobile interface. This initiative demonstrates the potential of AI to enhance irrigation advisory services in climate vulnerable coastal regions. The approach offers a scalable model for integrating machine learning into water resource management in other data-scarce deltaic settings. 2025-12-19 2026-01-07T04:54:57Z 2026-01-07T04:54:57Z Abstract https://hdl.handle.net/10568/179448 en Open Access Matheswaran, K.; Behera, A.; Sena, D. R.; Jampani, M.; Hasib, M. R.; Mondal, M. K. 2025. An AI-driven River Salinity Forecast and Advisory System for Climate Resilient Agriculture in Coastal Bangladesh [Abstract only]. Paper presented at the American Geophysical Union Fall (AGU25) Conference 2025: Where Science Connects Us. New Orleans, USA. 14-19 December 2025. |
| spellingShingle | artificial intelligence salinity management forecasting advisory services climate resilience agriculture coastal areas Matheswaran, Karthikeyan Behera, Abhijit Sena, Dipaka Ranjan Jampani, Mahesh Hasib, Md Raqubul Mondal, Manoranjan K. An AI-driven River Salinity Forecast and Advisory System for Climate Resilient Agriculture in Coastal Bangladesh |
| title | An AI-driven River Salinity Forecast and Advisory System for Climate Resilient Agriculture in Coastal Bangladesh |
| title_full | An AI-driven River Salinity Forecast and Advisory System for Climate Resilient Agriculture in Coastal Bangladesh |
| title_fullStr | An AI-driven River Salinity Forecast and Advisory System for Climate Resilient Agriculture in Coastal Bangladesh |
| title_full_unstemmed | An AI-driven River Salinity Forecast and Advisory System for Climate Resilient Agriculture in Coastal Bangladesh |
| title_short | An AI-driven River Salinity Forecast and Advisory System for Climate Resilient Agriculture in Coastal Bangladesh |
| title_sort | ai driven river salinity forecast and advisory system for climate resilient agriculture in coastal bangladesh |
| topic | artificial intelligence salinity management forecasting advisory services climate resilience agriculture coastal areas |
| url | https://hdl.handle.net/10568/179448 |
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