Assessing future hydrological and sediment transport response of an urban watershed using a machine learning–based land cover change model

Assessing the impacts of land cover change (LCC) on hydrology and sediment load is essential for the sustainable management of urban watersheds. Modeling LCC using machine learning techniques enhances the ability to generate realistic future scenarios, providing a robust basis for informed watershed...

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Bibliographic Details
Published in:	Environmental monitoring and assessment Vol. 197; no. 11; p. 1200
Main Authors:	Peker, İsmail Bilal, Cuceloglu, Gokhan, Sökmen, Eren Dağra, Gülbaz, Sezar
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 13.10.2025 Springer Nature B.V
Subjects:	Airport expansion Atmospheric Protection/Air Quality Control/Air Pollution Cities Dead storage Drinking water Earth and Environmental Science Ecology Ecotoxicology Environment Environmental Management Environmental Monitoring - methods Geographic information systems Geologic Sediments - analysis Hydrologic models Hydrologic processes Hydrology Land cover Land use Learning algorithms Machine Learning Markov analysis Markov chains Modelling Monitoring Monitoring/Environmental Analysis Multilayer perceptrons Population growth Precipitation Project management Rangelands Regression analysis Reservoir storage Sediment Sediment load Sediment transport Stream discharge Stream flow Surface runoff Sustainability management Turkey Urban sprawl Urban watersheds Urbanization Water Movements Water quality Water resources Water sources Watershed management Watersheds Turkey Istanbul Turkey Multilayer Perceptron–Markov chain Türkiye Field monitoring Urban watershed Land cover change Sediment transport
ISSN:	1573-2959, 0167-6369, 1573-2959
Online Access:	Get full text
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