چکیده مقاله
The present study aims to compare the efficiency of Machine learning algorithms including BaggingbasedRough Set BRS , Recurrent Neural Network NNETR , Boosted Regression Tree BRT , to evaluateflood susceptibility and identify vulnerable areas in Kal e Shur basin, so that the most critical factorsaffecting flood occurrences in the catchment area can be identified and investigated This study proposed ahybrid Flood Susceptibility Mapping FSM framework based on 3 learning models First, the flooddistribution map with 255 points was prepared, and then, the points were classified in a ratio of 70 to 30 fortraining and validation Among the 19 parameters effective in the occurrence of floods in the basin, nineparameters slope, land use/cover, lithology, distance to river, elevation, drainage density, and Slope LengthFactor SL Factor , precipitation, and soil are recognized as essential factors Among the natural parameters,loose and permeable formations, areas without vegetation, the concavity of the ground surface and upstreamrunoff are the most effective of them The integration of learning algorithms indicated the high efficiency ofthese algorithms in determining the flood susceptible zones with high flood risk Identifying flood proneareas and providing effective flood control and management strategies are essential measures to reduce flooddamage
کلیدواژهها
نویسندگان
Mohammadali Zangeneh Asadi
Leila Goli MokhtariRahman ZandiMahnaz Naemitabar
Leila Goli Mokhtari
Mohammadali Zangeneh AsadiRahman ZandiMahnaz Naemitabar
Rahman Zandi
Mohammadali Zangeneh AsadiLeila Goli MokhtariMahnaz Naemitabar
Mahnaz Naemitabar
Mohammadali Zangeneh AsadiLeila Goli MokhtariRahman Zandi
شیوه ارجاع
Zangeneh Asadi, Mohammadali and Goli Mokhtari, Leila and Zandi, Rahman and Naemitabar, Mahnaz,1402,Evaluation and modeling of flood risk in Kal-e Shur Sabzevarbasin using Machine learning algorithms,Second International Conference for Iranian Geography and Earth Sciences Students,Tehran
ارائهشده در
مجموعه مقالات دومین کنفرانس بین المللی دانشجویان جغرافیا و علوم زمین ایران30 بهمن 1402 · تهران