Summary The use of Artificial Intelligence (AI) has increased since the middle of the 20th
century as seen in its application in a wide range of engineering and science problems. The …

Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for
prediction and forecasting in water resources and environmental engineering. However …

Freshwater shortages have become much more common globally in recent years. Water
resources that are naturally available beneath the surface are capable of reversing this …

Accurate and reliable forecasting models for electricity demand (G) are critical in
engineering applications. They assist renewable and conventional energy engineers …

Physically distributed hydrological models are effective in hydrological simulations of large
river basins, but the complex characteristics of hydrological features limit their application …

This study aims to compare two machine learning techniques, random forests (RF) and
support vector machine (SVM), for real-time radar-derived rainfall forecasting. The real-time …

Flood forecasting is an important non-structural approach for flood mitigation. The flood
stage is chosen as the variable to be forecasted because it is practically useful in flood …

In this paper, several soft computing approaches were employed for rainfall prediction. Two
aspects were considered to improve the accuracy of rainfall prediction:(1) carrying out a data …

This study is an attempt to seek a relatively optimal data-driven model for rainfall forecasting
from three aspects: model inputs, modeling methods, and data-preprocessing techniques …

In this paper, the accuracy performance of monthly streamflow forecasts is discussed when
using data‐driven modeling techniques on the streamflow series. A crisp distributed support …