In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …

Deep learning (DL), a new generation of artificial neural network research, has transformed
industries, daily lives, and various scientific disciplines in recent years. DL represents …

Precipitation in any form—such as rain, snow, and hail—can affect day-to-day outdoor
activities. Rainfall prediction is one of the challenging tasks in weather forecasting process …

This article explores the suitability of a long short-term memory recurrent neural network
(LSTM-RNN) and artificial intelligence (AI) method for low-flow time series forecasting. The …

Abstract Multi-Model Ensembles (MMEs) are often employed to reduce the uncertainties
related to GCM simulations/projections. The objective of this study was to evaluate the …

Although machine learning (ML) techniques are increasingly used in rainfall-runoff models,
most of them are based on one-dimensional datasets. In this study, a rainfall-runoff model …

In the recent few decades there has been very significant developments in the theoretical
understanding of Support vector machines (SVMs) as well as algorithmic strategies for …

Rainfall–runoff modelling is complicated due to numerous complex interactions and
feedback in the water cycle among precipitation and evapotranspiration processes, and also …

Rainfall and runoff are considered the main components in the hydrological cycle.
Develo** an accurate model to capture the dynamic connection between rainfall and …

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 …