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- W3215713739 abstract "AbstractThis study explores the potential of Bayesian Network (BN), which is a class of Graphical Modeling (GM) as a feature selection technique for examining the association of monthly rainfall and probable meteorological drivers and subsequent drought assessment. One of the homogeneous meteorological subdivisions in Western India, namely, Vidarbha in Maharashtra is chosen as the study region. The relevant meteorological drivers (such as air temperature, total precipitable water, relative humidity, zonal wind, meridional wind, omega, and geo-potential height at different pressure levels) are considered up to a lead time of several months. The BN structure is developed to obtain the most influential drivers of rainfall and these are subsequently used as inputs to develop a prediction model for monthly rainfall using Artificial Neural Network (ANN). The developed hybrid BN-ANN model is contrasted against the prediction feature from the BN model. An evaluation of the hybrid (BN-ANN) model performance, in terms of suitable statistical measures, indicates good accuracy, affirming that BN is a promising tool for feature selection in multivariate hydrologic systems. Also, the predicted monthly rainfall is further used to achieve a reasonably good categorization of the drought scenarios in the study area in terms of dry, wet, and intermediate months using the Standard Precipitation Anomaly Index (SPAI) as the drought index.KeywordsBayesian networkGraphical modelingANNMeteorological driversRainfallSPAI" @default.
- W3215713739 created "2021-12-06" @default.
- W3215713739 creator A5017171449 @default.
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- W3215713739 date "2021-11-19" @default.
- W3215713739 modified "2023-09-27" @default.
- W3215713739 title "Feature Selection for Rainfall Prediction and Drought Assessment Using Bayesian Network Technique" @default.
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- W3215713739 doi "https://doi.org/10.1007/978-981-16-5501-2_10" @default.
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