[1]
|
Comparison of the performance of SWAT and hybrid M5P tree models in rainfall–runoff simulation
Journal of Water and Health,
2024
DOI:10.2166/wh.2024.022
|
|
|
[2]
|
Comparison of the performance of SWAT and hybrid M5P tree models in rainfall–runoff simulation
Journal of Water and Health,
2024
DOI:10.2166/wh.2024.022
|
|
|
[3]
|
Monthly Streamflow Forecasting Using Decomposition-Based Hybridization with Two-step Verification Method Over the Mangla Watershed, Pakistan
Iranian Journal of Science and Technology, Transactions of Civil Engineering,
2023
DOI:10.1007/s40996-022-00947-1
|
|
|
[4]
|
Modeling optical gap of cupric oxide nanomaterial semiconductor using hybrid intelligent method
Cogent Engineering,
2023
DOI:10.1080/23311916.2023.2283287
|
|
|
[5]
|
Prediction of Flow Based on a CNN-LSTM Combined Deep Learning Approach
Water,
2022
DOI:10.3390/w14060993
|
|
|
[6]
|
Multi-step-ahead prediction of river flow using NARX neural networks and deep learning LSTM
H2Open Journal,
2022
DOI:10.2166/h2oj.2022.134
|
|
|
[7]
|
A deep multivariate time series multistep forecasting network
Applied Intelligence,
2022
DOI:10.1007/s10489-021-02899-x
|
|
|
[8]
|
Monthly Streamflow Forecasting Using Decomposition-Based Hybridization with Two-step Verification Method Over the Mangla Watershed, Pakistan
Iranian Journal of Science and Technology, Transactions of Civil Engineering,
2022
DOI:10.1007/s40996-022-00947-1
|
|
|
[9]
|
Improving Monthly Rainfall Forecast in a Watershed by Combining Neural Networks and Autoregressive Models
Environmental Processes,
2022
DOI:10.1007/s40710-022-00602-x
|
|
|
[10]
|
Prediction of Flow Based on a CNN-LSTM Combined Deep Learning Approach
Water,
2022
DOI:10.3390/w14060993
|
|
|
[11]
|
Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting
Water Resources Management,
2021
DOI:10.1007/s11269-020-02746-7
|
|
|
[12]
|
Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting
Water Resources Management,
2021
DOI:10.1007/s11269-020-02746-7
|
|
|
[13]
|
Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin, China
Natural Hazards,
2021
DOI:10.1007/s11069-020-04394-x
|
|
|
[14]
|
Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting
Applied Sciences,
2020
DOI:10.3390/app10093224
|
|
|
[15]
|
Contrasting features of hydroclimatic teleconnections and the predictability of seasonal rainfall over east and west Japan
Meteorological Applications,
2020
DOI:10.1002/met.1881
|
|
|
[16]
|
Contrasting features of hydroclimatic teleconnections and the predictability of seasonal rainfall over east and west Japan
Meteorological Applications,
2020
DOI:10.1002/met.1881
|
|
|
[17]
|
Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting
Applied Sciences,
2020
DOI:10.3390/app10093224
|
|
|
[18]
|
Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling
Water,
2019
DOI:10.3390/w11030451
|
|
|
[19]
|
Energy consumption forecasting in agriculture by artificial intelligence and mathematical models
Energy Sources, Part A: Recovery, Utilization, and Environmental Effects,
2019
DOI:10.1080/15567036.2019.1604872
|
|
|
[20]
|
Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China
Water,
2019
DOI:10.3390/w11010085
|
|
|
[21]
|
Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning
Transportmetrica A: Transport Science,
2019
DOI:10.1080/23249935.2019.1637966
|
|
|
[22]
|
Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling
Water,
2019
DOI:10.3390/w11030451
|
|
|
[23]
|
Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China
Water,
2019
DOI:10.3390/w11010085
|
|
|
[24]
|
Agricultural drought prediction using climate indices based on Support Vector Regression in Xiangjiang River basin
Science of The Total Environment,
2018
DOI:10.1016/j.scitotenv.2017.12.025
|
|
|
[25]
|
Climate Change Impacts
Water Science and Technology Library,
2018
DOI:10.1007/978-981-10-5714-4_6
|
|
|
[26]
|
Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models
Neural Computing and Applications,
2018
DOI:10.1007/s00521-018-3519-9
|
|
|
[27]
|
Space–time forecasting of groundwater level using a hybrid soft computing model
Hydrological Sciences Journal,
2017
DOI:10.1080/02626667.2016.1252986
|
|
|
[28]
|
Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction
International Journal of Environmental Science and Technology,
2016
DOI:10.1007/s13762-015-0856-4
|
|
|
[29]
|
Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques
Water Resources Management,
2016
DOI:10.1007/s11269-015-1188-3
|
|
|
[30]
|
Uncovering global climate fields causing local precipitation extremes
Hydrological Sciences Journal,
2016
DOI:10.1080/02626667.2015.1006232
|
|
|
[31]
|
A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction
Environmental Earth Sciences,
2016
DOI:10.1007/s12665-015-5096-x
|
|
|
[32]
|
Modeling river discharge time series using support vector machine and artificial neural networks
Environmental Earth Sciences,
2016
DOI:10.1007/s12665-016-5435-6
|
|
|
[33]
|
Regional Flood Frequency Analysis using Support Vector Regression under historical and future climate
Journal of Hydrology,
2016
DOI:10.1016/j.jhydrol.2016.04.041
|
|
|
[34]
|
Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach
Energy and Buildings,
2015
DOI:10.1016/j.enbuild.2014.12.029
|
|
|
[35]
|
Multi-step-ahead time series prediction using multiple-output support vector regression
Neurocomputing,
2014
DOI:10.1016/j.neucom.2013.09.010
|
|
|
[36]
|
Support vector machine applications in the field of hydrology: A review
Applied Soft Computing,
2014
DOI:10.1016/j.asoc.2014.02.002
|
|
|