Modeling Hydrological Responses of Watershed Under Climate Change Scenarios Using Machine Learning Techniques

Climate change is the most important problem of the earth in the current century. In this study, the effects of climate change on precipitation, temperature, wind speed, relative humidity and surface runoff in Saghez watershed in Iran investigated. The main methods were using the Coupled Model Inter...

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Veröffentlicht in:	Water resources management Jg. 37; H. 13; S. 5235 - 5254
Hauptverfasser:	Karimizadeh, Keivan, Yi, Jaeeung
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.10.2023 Springer Nature B.V
Schlagworte:	Artificial neural networks Atmospheric Sciences Civil Engineering Climate change Climate effects Earth and Environmental Science Earth Sciences Environment Environmental impact Geotechnical Engineering & Applied Earth Sciences Humidity Hydrogeology Hydrologic models Hydrology/Water Resources Intercomparison Iran Machine learning Neural networks Precipitation prediction Predictions Relative humidity Runoff Scaling Socioeconomic aspects socioeconomics Soil and Water Assessment Tool model Soil water Surface runoff temperature Water resources Watersheds Weather forecasting Wind speed Iran Climate change CMIP6 SSPs SWAT and machine learning Saghez watershed
ISSN:	0920-4741, 1573-1650
Online-Zugang:	Volltext
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