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

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Abstract

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.

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Data availability

The data used in the manuscript are publicly available. Some data on www.irimo.ir/far/wd.html are available under the Iran Meteorological Organization.

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Acknowledgements

We would like to appreciate Iran Meteorological Organization for their collaboration in this research.

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Authors and Affiliations

  1. Environmental Field (Trend: Assessment and Land Use Planning), Malayer University, Malayer, Islamic Republic of Iran

    Nafiseh Rezapour Andabili

  2. Department of Agriculture and Natural Resources Engineering, Payam Noor University, Tehran, Iran

    Mahsa Safaripour

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  1. Nafiseh Rezapour Andabili
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  2. Mahsa Safaripour
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Correspondence to Mahsa Safaripour.

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Rezapour Andabili, N., Safaripour, M. Identification of precipitation trend and landslide susceptibility analysis in Miandoab County using MATLAB. Environ Monit Assess 194, 472 (2022). https://doi.org/10.1007/s10661-022-10069-w

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