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A comparative study between popular statistical and machine learning methods for simulating volume of landslides

Shirzadi, A. and Shahabi, H. and Chapi, K. and Bui, D. T. and Pham, B. T. and Shahedi, K. and Ahmad, B. B. (2017) A comparative study between popular statistical and machine learning methods for simulating volume of landslides. Catena, 157 . pp. 213-226. ISSN 0341-8162

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Abstract

This study attempts to compare popular statistical methods (linear, logarithmic, quadratic, power and exponential functions) with machine learning methods (multi-layer perceptron (MLP), radial base function (RBF), adaptive neural-based fuzzy inference system (ANFIS) and support vector machine (SVM)) for simulating the volume of landslides based on their surface area (VL ~ AL) in the Kurdistan province, Iran. Performances of the models were validated using some commonly error functions including the Adjusted R2, F-test and AIC (Akaike Information Criteria). The results showed that the power model demonstrates the best performance compared to other statistical methods whereas the ANFIS model outperforms other machine learning approaches. Furthermore, the comparative results showed that machine learning methods indicate better performances than simple statistical methods for simulating the volume of landslides in the study area. In practice, the outputs of this research can help managers and investigators decrease the cost of field surveys and measurements of volumes of landslides in landslide hazard management projects.

Item Type:Article
Uncontrolled Keywords:ANFIS, Iran, Kurdistan province, Landslide, Machine learning algorithms, Simple statistical models
Subjects:H Social Sciences > HD Industries. Land use. Labor
Divisions:Geoinformation and Real Estate
ID Code:75476
Deposited By: Widya Wahid
Deposited On:28 Mar 2018 12:05
Last Modified:28 Mar 2018 12:05

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