Hybrid Drought Forecasting Framework for Water‐Scarce Regions Based on Support Vector Machine and Precipitation Index

Hydrological Processes, Год журнала: 2024, Номер 38(12)

Опубликована: Дек. 1, 2024

ABSTRACT Drought is a natural event that slowly deteriorates water reserves. This study aims to develop machine learning–based computational framework for monitoring drought status in water‐scarce regions. The proposed integrates the precipitation index (PI) with support vector models forecast occurrences based on an autoregressive modelling scheme. Due suitability of PI analysis arid climates, developed hybrid model appropriate regions limited rainfall. used historical dataset from 1958 2020 at Kuwait International Airport, City. area characterised by scarce rainfall and vulnerable severe shortages owing resources. Initially, time‐series datasets were examined stationarity validate utility model. autocorrelation function test was significantly associated time series 12‐ 24‐month drought‐monitoring scales. Predictive forecasting constructed predict up 3 months advance. Statistical evaluation metrics assess performance results showed strong association between observed predicted events, coefficients determination ( R 2 ) ranging 0.865 0.925 provide managers efficient reliable tools assist preparing management plans. provides guidance improving resource resilience under shortage scenarios other climatic applying suitable indices conjunction robust data‐driven models. baseline policymakers worldwide establish sustainable conservation strategies crucial insights disaster preparation.

Язык: Английский

Key point of desert riparian forest development in the arid area: The response of phreatic water table depth to ecological water supply

The Science of The Total Environment, Год журнала: 2024, Номер 957, С. 177716 - 177716

Опубликована: Дек. 1, 2024

Язык: Английский