Identification of precipitation trend and landslide susceptibility analysis in Miandoab County using MATLAB

• Published in: Environmental monitoring and assessment Vol. 194; no. 7; p. 472
• Main Authors: Rezapour Andabili, Nafiseh, Safaripour, Mahsa
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.07.2022; Springer Nature B.V
• Subjects: Algorithms; Annual precipitation; atmospheric precipitation; Atmospheric Protection/Air Quality Control/Air Pollution; Distance; Earth and Environmental Science; Earthquakes; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Environmental risk; Environmental science; Evaluation; Geology; Hazard identification; Hazard mitigation; Height; Hot spots (geology); Iran; Land use; Landslides; Matlab; Model accuracy; Monitoring/Environmental Analysis; Precipitation; Precipitation trends; Risk; rivers; Seismic activity; Seismic response; Trends; Iran; ROC-AUC model; Earthquake; Mann–Kendall trend test; Rainfall; Zoning; Landslide; Matlab
• ISSN: 0167-6369; 1573-2959; 1573-2959
• DOI: 10.1007/s10661-022-10069-w

Iranian plateau is a seismically active region. Within this region, northwestern Iran is of high importance. Before proper planning for mitigating the earthquake-induced hazards can be achieved, it is necessary to identify high-risk areas in terms of susceptibility to earthquakes. In this study, landslide susceptibility in Miandoab Country was modeled using the so-called random forest algorithm (RFA) in MATLAB based on records acquired at 67 earthquake hotspots considering 9 factors affecting the earthquake occurrence (i.e., height, slope, direction, distance from fault, distance from river, distance from road, land use, geology, and precipitation). Predictive power of the model and validity of its results were evaluated using relative operating characteristic (ROC) curve and area under the curve (AUC). The assessment results showed very good accuracy of the model (0.97). It was further found that digital height layer, geology, and distance from fault impose the largest contributions into earthquake potential. The results also showed that 53%, 8.3%, and 38.4% of the study area were classified as being at low risk, moderate risk, and high risk of earthquake. Among other climatic elements, the precipitation exhibits the largest fluctuations; we proceeded to evaluate precipitation trends in the study area for a statistical period of 30 years. This was practiced by implementing Mann–Kendall nonparametric test in MATLAB. This subject-matter is especially important in Iran where the annual precipitation level is as low as 250 mm. The results showed that the precipitation follows an increasing trend in the region.