Open AccessCombination of machine learning model (LR-FR) for flash flood susceptibility assessment in Dawuan Sub watershed Mojokerto Regency, East Java
Author(s) -
Listyo Yudha Irawan,
Sumarmi Sumarmi,
Damar Panoto,
Nabila,
Irfan Helmi Pradana,
Arif Darmansyah
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/739/1/012017
Subject(s) - flash flood , watershed , hydrology (agriculture) , topographic wetness index , drainage , environmental science , flood myth , logistic regression , geography , geology , landslide , geomorphology , machine learning , mathematics , statistics , geotechnical engineering , archaeology , computer science , ecology , biology
The Dawuan Sub-watershed in Mojokerto Regency is a prone area to floods. There were flash floods in this area in 2002 and 2019, which caused casualties and property losses. As one of the mitigation efforts, this study aims to map a flash flood’s susceptibility using the LR-FR combination machine learning technique (logistic regression and frequency ratio). 11 conditioning factors are used to assess landslide susceptibility, namely: slope, aspect, TWI (Topographic Wetness Index), TPI (Topographic Position Index), SPI (Stream Power Index), profile curvature, distance to drainage, rainfall, geological unit, and land use. The results of the flash flood susceptibility mapping show that areas with very high levels of susceptibility have the following characteristics: slope 16; TPI <(-3,39)-(-0,06); SPI <50-200; profile curvature (-0,001)-0,0; distance to drainage <10-40; rainfall <2000; geological unit Qvwl, Qvlw3, Qvlp3, Qvlp4, Qvwl, Qvf3, Qvf4 and Qvf8; and agricultural land use. The validation results show that the quality of the LR-FR model used has very good quality, as indicated by the AUC value = 0.93.