Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for
prediction and forecasting in water resources and environmental engineering. However …
prediction and forecasting in water resources and environmental engineering. However …
[PDF][PDF] State of the art of artificial neural networks in geotechnical engineering
Over the last few years, artificial neural networks (ANNs) have been used successfully for
modeling almost all aspects of geotechnical engineering problems. Whilst ANNs provide a …
modeling almost all aspects of geotechnical engineering problems. Whilst ANNs provide a …
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear …
It is now well established to use shallow artificial neural networks (ANNs) to obtain accurate
and reliable groundwater level forecasts, which are an important tool for sustainable …
and reliable groundwater level forecasts, which are an important tool for sustainable …
Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network …
Daily water demand forecasts are an important component of cost‐effective and sustainable
management and optimization of urban water supply systems. In this study, a method based …
management and optimization of urban water supply systems. In this study, a method based …
A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data
Physically distributed hydrological models are effective in hydrological simulations of large
river basins, but the complex characteristics of hydrological features limit their application …
river basins, but the complex characteristics of hydrological features limit their application …
Real-time reservoir operation using recurrent neural networks and inflow forecast from a distributed hydrological model
Large-scale reservoirs play an essential role in water resources management for agriculture
irrigation, water supply and flood controls. However, we need robust reservoir operation …
irrigation, water supply and flood controls. However, we need robust reservoir operation …
Prediction of rainfall time series using modular soft computingmethods
CL Wu, KW Chau - Engineering applications of artificial intelligence, 2013 - Elsevier
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 …
aspects were considered to improve the accuracy of rainfall prediction:(1) carrying out a data …
HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts
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 …
other scientists to evaluate time series models. There is at present a general lack of …
Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques
CL Wu, KW Chau, YS Li - Water Resources Research, 2009 - Wiley Online Library
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 …
using data‐driven modeling techniques on the streamflow series. A crisp distributed support …
Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models
Streamflow forecasts are essential for water resources management. Although there are
many methods for forecasting streamflow, real-time forecasts remain challenging. This study …
many methods for forecasting streamflow, real-time forecasts remain challenging. This study …