Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions
The development and application of evolutionary algorithms (EAs) and other metaheuristics
for the optimisation of water resources systems has been an active research field for over …
for the optimisation of water resources systems has been an active research field for over …
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 …
[HTML][HTML] Exploding the myths: An introduction to artificial neural networks for prediction and forecasting
Abstract Artificial Neural Networks (ANNs), sometimes also called models for deep learning,
are used extensively for the prediction of a range of environmental variables. While the …
are used extensively for the prediction of a range of environmental variables. While the …
Simulation and forecasting of streamflows using machine learning models coupled with base flow separation
Efficient simulation of rainfall-runoff relationships is one of the most complex problems owing
to the high number of interrelated hydrological processes. It is well-known that machine …
to the high number of interrelated hydrological processes. It is well-known that machine …
Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model
Drought forecasting using standardized metrics of rainfall is a core task in hydrology and
water resources management. Standardized Precipitation Index (SPI) is a rainfall-based …
water resources management. Standardized Precipitation Index (SPI) is a rainfall-based …
Data-driven input variable selection for rainfall–runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines
Selecting an adequate set of inputs is a critical step for successful data-driven streamflow
prediction. In this study, we present a novel approach for Input Variable Selection (IVS) that …
prediction. In this study, we present a novel approach for Input Variable Selection (IVS) that …
A comparative study of mutual information-based input variable selection strategies for the displacement prediction of seepage-driven landslides using optimized …
Artificial intelligence (AI) is becoming increasingly popular and useful for modeling landslide
movement processes due to its advantages of providing excellent generalization ability and …
movement processes due to its advantages of providing excellent generalization ability and …
Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique
HK Ghritlahre, RK Prasad - Journal of environmental management, 2018 - Elsevier
In the present study three different types of neural models: multi-layer perceptron (MLP),
generalized regression neural network (GRNN) and radial basis function (RBF) has been …
generalized regression neural network (GRNN) and radial basis function (RBF) has been …
Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms
Daily streamflow forecasting through data-driven approaches is traditionally performed
using a single machine learning algorithm. Existing applications are mostly restricted to …
using a single machine learning algorithm. Existing applications are mostly restricted to …
Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis
CL Wu, KW Chau - Journal of Hydrology, 2011 - Elsevier
Accurately modeling rainfall–runoff (R–R) transform remains a challenging task despite that
a wide range of modeling techniques, either knowledge-driven or data-driven, have been …
a wide range of modeling techniques, either knowledge-driven or data-driven, have been …