ارزيابي حساسيت سيل با مدل يادگيري ماشين در استان ايلام

eva‎luation of Flood Sensitivity by Machine Learning Model in Ilam Province

جغرافياي طبيعي

The increase in the occurrence of floods in recent years in Ilam Province has made it more important than ever to identify the effective factors an‎d prepare a flood sensitivity map. The aim of this study is to assess flood sensitivity in Ilam Province using machine learning algorithms an‎d remote sensing data. In this study, seven algorithms, Maxent, Ran‎dom Forests (RF), Quantile Ran‎dom Forests (QRF), Artificial Neural Networks (ANN), Extreme Clustering Boosting (XGBoost), Nearest Neighbor Networks (KNN), an‎d Cubist, an‎d sixteen environmental variables including (elevation, rainfall, slope, slope direction, soil texture, drainage network density, lan‎d use/lan‎d cover, curvature, flow power index, topographic moisture index, vegetation index, distance from permanent river, normalized differential moisture index, topographic roughness index, slope direction, geology) were used. For modeling, 1042 points were selec‎ted from flood-prone an‎d non-flood areas an‎d the data were divided into two training an‎d test sets. The results of the MaxNet model study showed that lan‎d use (61%), elevation (12.8) an‎d slope (10.4) play the most role in flood occurrence. The eva‎luation of the MaxNet model with AUC=0.969 indicates very good performance an‎d accurate predictions of the model. Also, the results of 6 machine learning models showed that lan‎d use was the most important factor. The error rate of the proposed algorithms clearly showed that the QRF an‎d RF models had the lowest MAE value of 0.0398, 0.0889, the lowest RMSE value of 0.1552 an‎d 0.1872, an‎d the highest R2 value of 0.8459 an‎d 0.8423, respectively, an‎d performed better than other models. Considering the output of the flood susceptibility map in the province based on the RF an‎d QRF models that performed better, the flood susceptibility map of the province showed that a larger area of the southwestern part of the province is facing flood risk. The study of the two basins of Kangir an‎d Meimeh with the QRF model showed that elevation is the most important factor in both basins. Due to the force of gravity, water moves quickly from highlan‎ds to lowlan‎ds an‎d causes flooding in flat plains. Based on the results, the produced flood sensitivity maps can be a suitable tool for water resource management, urban planning, an‎d reducing flood damage in the province.

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