Addressing salinity intrusion in the polders of coastal Bangladesh: predictive machine-learning modeling for strategic sluice gate operations

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cg.contributor.affiliationInternational Water Management Instituteen_US
cg.contributor.affiliationInstitute of Water Modellingen_US
cg.contributor.affiliationInternational Rice Research Instituteen_US
cg.contributor.donorCGIAR Trust Funden_US
cg.contributor.initiativeAsian Mega-Deltasen_US
cg.coverage.countryBangladeshen_US
cg.coverage.iso3166-alpha2BDen_US
cg.creator.identifierAbhijit Behera: 0000-0002-0162-8508en_US
cg.creator.identifierD R SENA: 0000-0003-4683-4687en_US
cg.creator.identifierkarthikeyan matheswaran: 0000-0001-7377-0629en_US
cg.creator.identifierMahesh Jampani: 0000-0002-8925-719Xen_US
cg.creator.identifierSyed Mizan: 0000-0002-8707-9764en_US
cg.creator.identifierAlok Sikka: 0000-0001-9843-9617en_US
cg.identifier.iwmilibraryH053410en_US
cg.identifier.projectIWMI - C-0019en_US
cg.identifier.urlhttps://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1669003en_US
cg.reviewStatusPeer Reviewen_US
dc.contributor.authorBehera, Abhijiten_US
dc.contributor.authorSena, Dipaka Ranjanen_US
dc.contributor.authorHasib, Md. R.en_US
dc.contributor.authorMatheswaran, Karthikeyanen_US
dc.contributor.authorJampani, Maheshen_US
dc.contributor.authorMizan, Syed Adilen_US
dc.contributor.authorIslam, Md. J.en_US
dc.contributor.authorAlam, R.en_US
dc.contributor.authorMondal, M. K.en_US
dc.contributor.authorSikka, Alok Kumaren_US
dc.date.accessioned2025-01-14T07:01:41Zen_US
dc.date.available2025-01-14T07:01:41Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/168948en_US
dc.titleAddressing salinity intrusion in the polders of coastal Bangladesh: predictive machine-learning modeling for strategic sluice gate operationsen_US
dcterms.abstractThe coastal zone of Bangladesh comprises several polders, which are low-lying tracts of land surrounded by embankments to protect against tidal floods and saline water intrusion. They also enhance freshwater availability and aid in improving land productivity. These polders are equipped with sluice gates for water to drain out and intake into the polders. Each sluice has its own catchment area, defined by the elevation and connectivity with canal systems that carry fresh or saline water from surrounding rivers or streams. The sluice gates operation is influenced by in-polder water management for crop cultivation, diurnal tidal dynamics, and the seasonal variations of saline and fresh water in the peripheral river networks. During the dry season, limited flows in the lower Ganges River allow seawater to push inland, causing saltwater intrusion in the peripheral rivers until the rainy season. Community-coordinated sluice gate operations can improve water management, facilitating timely drainage and irrigation, which is essential for high-yielding rice and subsequent dry-season crops. To address these challenges, a multi-variate LSTM (Long Short-Term Memory) model was employed to forecast salinity levels in rivers near 29 sluice gates in a polder near Khulna City in southwest Bangladesh. Utilizing salinity data from July 2011 to December 2022, the models were trained (2011-18) and validated (2018-20) with covariates of discharge, water level, and an upstream reference station. A hierarchical variable additive approach was used to sequentially estimate salinity from upstream to downstream. The NSE was over 0.90 and PBIAS under 5% for all sluice gate locations, confirming accuracy in reconstructing the time series. For forecast testing, the 2020-22 dataset also showed significant confirmation with NSE values over 0.90 and PBIAS under 10%. With readily available input data, the developed salinity forecast model can effectively capture annual and seasonal salinity fluctuations along all sluice gate locations. These forecasting capabilities can potentially identify critical seasonal windows for sluice gate operations, giving the farmers in the polder a 30-day lead time for freshwater intake for irrigation and starting agricultural operations in the aman season.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.bibliographicCitationBehera, Abhijit; Sena, Dipaka Ranjan; Hasib, Md. R.; Matheswaran, Karthikeyan; Jampani, Mahesh; Mizan, Syed Adil; Islam, Md. J.; Alam, R.; Mondal, M. K.; Sikka, Alok Kumar. 2024. Addressing salinity intrusion in the polders of coastal Bangladesh: predictive machine-learning modeling for strategic sluice gate operations [Abstract only]. Paper presented at the American Geophysical Union Annual Meeting 2024 (AGU24) on What’s Next for Science, Washington, DC, USA, 9-13 December 2024. 1p.en_US
dcterms.extent1p.en_US
dcterms.issued2024-12-11en_US
dcterms.languageenen_US
dcterms.licenseCopyrighted; all rights reserveden_US
dcterms.subjectsaltwater intrusionen_US
dcterms.subjectsalinityen_US
dcterms.subjectpoldersen_US
dcterms.subjectcoastal areasen_US
dcterms.subjectmachine learningen_US
dcterms.subjectmodellingen_US
dcterms.subjectsluicesen_US
dcterms.typeAbstracten_US

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