A New Bayesian Network-Based Generalized Weighting Scheme for the Amalgamation of Multiple Drought Indices

Drought is one of the most multifaceted hydrologic phenomena, affecting several factors such as soil moisture, surface runoff, and significant water shortages. Therefore, monitoring and assessing drought occurrences based on a single drought index are inadequate. The current study develops a multiscalar weighted amalgamated drought index (MWADI) to amalgamate multiple drought indices. The MWADI is mainly based on the normalized average dependence posterior probabilities (ADPPs). These ADPPs are obtained from Bayesian networks (BNs)-based Markov Chain Monte Carlo (MCMC) simulations. Results have shown that the MWADI correlates more with the standardized precipitation index (SPI) and the standardized precipitation temperature index (SPTI). As proposed, the MWADI synthesizes drought characteristics of different multiscalar drought indices to reduce the uncertainty of individual drought indices and provide a comprehensive drought assessment.Validerad;2023;Nivå 2;2023-05-04 (hanlid);Funder: Deanship ofScientifc Research at King Khalid University (RGP.2/23/44); Prince Sattam bin Abdulaziz University (PSAU/2023/R/1444);Part of special issue: Technologies-Based Advanced Machine Learning Models: Applications in Civil Engineering 2021