[HTML][HTML] A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting
Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has
further generated immense interest in researching aspects for further improvements to …
further generated immense interest in researching aspects for further improvements to …
An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research …
Despite the massive diversity in the modeling requirements for practical hydrological
applications, there remains a need to develop more reliable and intelligent expert systems …
applications, there remains a need to develop more reliable and intelligent expert systems …
Advanced machine learning techniques to improve hydrological prediction: A comparative analysis of streamflow prediction models
The management of water resources depends heavily on hydrological prediction, and
advances in machine learning (ML) present prospects for improving predictive modelling …
advances in machine learning (ML) present prospects for improving predictive modelling …
Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model
Water quality monitoring is an important component of water resources management. In
order to predict two water quality variables, namely dissolved oxygen (DO; mg/L) and …
order to predict two water quality variables, namely dissolved oxygen (DO; mg/L) and …
Coupling a hybrid CNN-LSTM deep learning model with a boundary corrected maximal overlap discrete wavelet transform for multiscale lake water level forecasting
Developing accurate lake water level (WL) forecasting models is important for flood control,
shoreline maintenance and sustainable water resources planning and management. In this …
shoreline maintenance and sustainable water resources planning and management. In this …
Water quality prediction using machine learning methods
AH Haghiabi, AH Nasrolahi… - Water Quality Research …, 2018 - iwaponline.com
This study investigates the performance of artificial intelligence techniques including artificial
neural network (ANN), group method of data handling (GMDH) and support vector machine …
neural network (ANN), group method of data handling (GMDH) and support vector machine …
An integrated statistical-machine learning approach for runoff prediction
Nowadays, great attention has been attributed to the study of runoff and its fluctuation over
space and time. There is a crucial need for a good soil and water management system to …
space and time. There is a crucial need for a good soil and water management system to …
Artificial intelligence based models for stream-flow forecasting: 2000–2015
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 …
century as seen in its application in a wide range of engineering and science problems. The …
Uncertainty quantification of granular computing-neural network model for prediction of pollutant longitudinal dispersion coefficient in aquatic streams
Discharge of pollution loads into natural water systems remains a global challenge that
threatens water and food supply, as well as endangering ecosystem services. Natural …
threatens water and food supply, as well as endangering ecosystem services. Natural …
Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition
Hydrological time series forecasting is one of the most important applications in modern
hydrology, especially for effective reservoir management. In this research, the auto …
hydrology, especially for effective reservoir management. In this research, the auto …