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

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 …

Intelligent Transportation System (ITS) is a significant part of smart city, and short-term traffic
flow prediction plays an important role in intelligent transportation management and route …

Ensemble forecasting applied to the field of hydrology is currently an established area of
research embracing a broad spectrum of operational situations. This work catalogs the …

The present study proposes a new hybrid evolutionary Adaptive Neuro-Fuzzy Inference
Systems (ANFIS) approach for monthly streamflow forecasting. The proposed method is a …

Develo** a hydrological forecasting model based on past records is crucial to effective
hydropower reservoir management and scheduling. Traditionally, time series analysis and …

Accurate prediction of the water level in a reservoir is crucial to optimizing the management
of water resources. A neuro-fuzzy hybrid approach was used to construct a water level …

This paper presents details of an open access web site that can be used by hydrologists and
other scientists to evaluate time series models. There is at present a general lack of …

This paper traces two decades of neural network rainfall-runoff and streamflow modelling,
collectively termed 'river forecasting'. The field is now firmly established and the research …

In this work, we develop and test two artificial neural networks (ANNs) to forecast streamflow
in ungauged basins. The model inputs include time-lagged records of precipitation and …