Intelligent Methods for flood forecasting in Wadi al Wala, Jordan

Abstract

Increasing water scarcity and rising demand throughout the Middle East and North Africa pose a major problem, and flood forecasting has been an open issue for a long time, attracting significant attention. Jordan seeks to use smart methods to solve the problem. Therefore, a real-world case study was conducted in Wadi al Wala for real-time rainfall forecasting and flood control, using 38 years of daily data from 13 rain gauge stations in the region. Different Machine Learning (ML) models were evaluated with various input information types to provide predictions in an almost real-time schedule. Preliminary tests showed that the decision tree (DT) and random forest (RF) techniques achieved the best generalized flood forecasting. In particular, the model was able to produce forecasts at any time, with the use of a mixture of meteorological parameters (relative humidity, air pressure, wet bulb temperature, and cloudiness), the precipitation at the forecasting point, and precipitation at the appropriate stations as input data, and the advanced ML model to be used with continuous data containing rainy and non-rainy cycles. Experiments showed the dominance of DT forecasts over those produced by the persistent model.