Hydrological forecasting is key for water resources allocation and flood risk management.
Although a number of advanced hydrological forecasting methods have been developed in …

Rainfall forecast is critical to the management and allocation of water resources. Deep
learning is used to predict rainfall time series with high temporal and spatial variability …

The performance of the bio-inspired adaptive neuro-fuzzy inference system (ANFIS) models
are proposed for forecasting highly non-linear streamflow of Pahang River, located in a …

This paper presents a dynamical neural network framework to understand how catchment
systems respond to daily rainfall-runoff processes over time. We developed an interactive …

Stream flow prediction is studied by Artificial Intelligence (AI) in this paper using Artificial
Neural Network (ANN) as a hybrid of Multi-Layer Perceptron (MLP) with the Levenberg …

Forecasting reservoir inflow is one of the most important components of water resources and
hydroelectric systems operation management. Seasonal autoregressive integrated moving …

This paper aims to introduce an artificial intelligence approach to enhance short-term daily
streamflow forecasts through the integration of machine learning (ML) techniques with meta …

There has been a variety of techniques employed in the forecasting hydrological events.
These techniques have shown efficacies in some circumstances and also shown poor …

Revealing the dynamic link between rainfall and runoff, which are the main components of
the hydrological cycle, is significant for the planning and managing water resources, disaster …

Numerous data-driven models have been introduced to establish reliable predictions in the
rainfall-runoff relationship. The majority of these models are trained using a supervised …