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- W3088023672 endingPage "125563" @default.
- W3088023672 startingPage "125563" @default.
- W3088023672 abstract "This study aims to establish the conditional independence structure between regional monthly rainfall and several local meteorological drivers (probable predictors) to develop a parsimonious prediction model in the framework of Bayesian Networks (BN). The chosen study area is the Gangetic West Bengal (GWB) region, India and the chosen meteorological drivers at the surface and different pressure levels are the 12 probable predictors, viz. air temperature, total precipitable water, relative humidity, geo-potential height, zonal wind, meridional wind, omega and soil moisture, each up to a lead time of 6 months, making a total of 72 inputs. Within the BN architecture, two heuristic algorithms namely Hill-Climb (HC) and Max-Min Hill Climb (MMHC) were used to establish that a large part (89% for HC and 97% for MMHC) of the input information was redundant, given the potential predictors that are selected via these algorithms. The potential predictors revealed via these algorithms include geo-potential height at a pressure of 850 mb, total precipitable water, and soil moisture, each at a lead time of 2 months. The selected predictors were further used to develop a prediction model that results in good agreement with the observations. Additionally, two widely used machine learning techniques namely, Artificial Neural Networks (ANN) and Support Vector Regression (SVR) were also used to develop standalone models wherein all the 72 probable predictors are used for prediction, and also in conjunction with BN (i.e., BN-ANN and BN-SVR), wherein only the drivers selected via BN are used as predictors. The results indicate that the parsimonious models perform better or equally well as the higher dimensional models, with refined Index of agreement values: MDBN = 0.824, MDBN-ANN = 0.835 and MDBN-SVR = 0.841, MDANN = 0.813, MDSVR = 0.823, and Normalized Mean Square Error values: NMSEBN = 0.242, NMSEBN-ANN = 0.226 and NMSEBN-SVR = 0.234, NMSEANN = 0.226, NMSESVR = 0.238 for validation period (averaged values across three folds). The model predictions are further used to categorize monthly rainfall into dry/ intermediate/ wet in terms of Standardized Precipitation Anomaly Index (SPAI). All the approaches show consistent performance, with the ‘dry’ cases being captured slightly better than ‘wet’ cases." @default.
- W3088023672 created "2020-10-01" @default.
- W3088023672 creator A5017171449 @default.
- W3088023672 creator A5029479448 @default.
- W3088023672 date "2020-12-01" @default.
- W3088023672 modified "2023-10-04" @default.
- W3088023672 title "Bayesian Network based modeling of regional rainfall from multiple local meteorological drivers" @default.
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- W3088023672 doi "https://doi.org/10.1016/j.jhydrol.2020.125563" @default.
- W3088023672 hasPublicationYear "2020" @default.
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