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 …
Genetic programming in water resources engineering: A state-of-the-art review
The state-of-the-art genetic programming (GP) method is an evolutionary algorithm for
automatic generation of computer programs. In recent decades, GP has been frequently …
automatic generation of computer programs. In recent decades, GP has been frequently …
A hybrid ETS–ANN model for time series forecasting
S Panigrahi, HS Behera - Engineering applications of artificial intelligence, 2017 - Elsevier
Over the past few decades, a large literature has evolved to forecast time series using
various linear, nonlinear and hybrid linear–nonlinear models. Recently, hybrid models by …
various linear, nonlinear and hybrid linear–nonlinear models. Recently, hybrid models by …
Ocean wave energy forecasting using optimised deep learning neural networks
PMR Bento, JAN Pombo, RPG Mendes, MRA Calado… - Ocean …, 2021 - Elsevier
Ocean renewable energy is a promising inexhaustible source of renewable energy, with an
estimated harnessing potential of approximately 337 GW worldwide, which could re-shape …
estimated harnessing potential of approximately 337 GW worldwide, which could re-shape …
Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes
G Papacharalampous, H Tyralis… - … research and risk …, 2019 - Springer
Research within the field of hydrology often focuses on the statistical problem of comparing
stochastic to machine learning (ML) forecasting methods. The performed comparisons are …
stochastic to machine learning (ML) forecasting methods. The performed comparisons are …
Applicability of ε-support vector machine and artificial neural network for flood forecasting in humid, semi-humid and semi-arid basins in China
TM Bafitlhile, Z Li - Water, 2019 - mdpi.com
The aim of this study was to develop hydrological models that can represent different geo-
climatic system, namely: humid, semi-humid and semi-arid systems, in China. Humid and …
climatic system, namely: humid, semi-humid and semi-arid systems, in China. Humid and …
A multiscale long short-term memory model with attention mechanism for improving monthly precipitation prediction
L Tao, X He, J Li, D Yang - Journal of Hydrology, 2021 - Elsevier
In this study, a multiscale long short-term memory model with attention mechanism (MLSTM-
AM) is proposed to improve the accuracy of monthly precipitation forecasting. In the MLSTM …
AM) is proposed to improve the accuracy of monthly precipitation forecasting. In the MLSTM …
Reconstructing daily discharge in a megadelta using machine learning techniques
In this study, six machine learning (ML) models, namely, random forest (RF), Gaussian
process regression (GPR), support vector regression (SVR), decision tree (DT), least …
process regression (GPR), support vector regression (SVR), decision tree (DT), least …
Hybrid load forecasting for mixed-use complex based on the characteristic load decomposition by pilot signals
K Park, S Yoon, E Hwang - IEEE Access, 2019 - ieeexplore.ieee.org
In this paper, a characteristic load decomposition (CLD)-based day-ahead load forecasting
scheme is proposed for a mixed-use complex. The aggregated load of the complex is …
scheme is proposed for a mixed-use complex. The aggregated load of the complex is …
Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling
The input variable selection problem has recently garnered much interest in the time series
modeling community, especially within water resources applications, demonstrating that …
modeling community, especially within water resources applications, demonstrating that …