THE APPLICATION OF ARTIFICIAL NEURAL NETWORK ON LANDSLIDE SUSCEPTIBILITY MAPPING DEVELOPED BY FREQUENCY RATIO AND AHP IN OLIYA'S PADENA IN SEMIROM

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ARABAMERI ALIREZA; REZAEEI KHALIL; RAMSHET MOHAMMADHOSSEIN; SHIRANI KOUROSH

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Journal: WATERSHED ENGINEERING AND MANAGEMENT Year:2018 | Volume:10 | Issue:3 Page(s): 332-349

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Title

THE APPLICATION OF ARTIFICIAL NEURAL NETWORK ON LANDSLIDE SUSCEPTIBILITY MAPPING DEVELOPED BY FREQUENCY RATIO AND AHP IN OLIYA'S PADENA IN SEMIROM

Author(s)

ARABAMERI ALIREZA | REZAEEI KHALIL | RAMSHET MOHAMMADHOSSEIN | SHIRANI KOUROSH | Issue Writer Certificate

Keywords

AHP METHODQ1

FREQUENCY RATIO METHODQ2

LANDSLIDE SUSCEPTIBILITYQ1

MULTILAYER PERCEPTRONQ1

OLIYA'S PADENA IN SEMIROMQ1

SEDIMENTQ1

Abstract

Landslide susceptibility and its risk assessment is the main part of landslide risk mapping. In this study, LANDSLIDE SUSCEPTIBILITY of OLIYA'S PADENA IN SEMIROM is mapped using artificial neural network. A total of 23 factors in relation to landslide in the region were initially characterized. The spatial location of landslide events was then determined by field study as well as aerial photo analysis. AHP analysis tends to 14 out of 23 parameters as the important factors for further steps. A total of 72 (70%) and 31 (30%) out of 103 detected landslide events in the study area were selected as training and validation data for neural network analysis, respectively. A MULTILAYER PERCEPTRON back propagation algorithm with sigmoid as activation function was developed. The best topology was determined by using conventional criteria including mean square error, root mean square error, maximum absolute error and correlation coefficient. Results show that a 14-4-1 array is the optimum topology for LANDSLIDE SUSCEPTIBILITY zoning in the region. The weight of each input layer was estimated by frequency ratio. In order to map landslide, ROC graph and area under curve indices were used and the accuracy of output map was computed. Results from validation shows that area under curve for the obtained model is about 0.938 (93.8%) that is considered as high resolution prediction group. According to this study, a total of 29.61 square kilometers (93.25%) of the landslide areas is categorized in very high and high susceptible groups.

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APA: Copy

ARABAMERI, ALIREZA, REZAEEI, KHALIL, RAMSHET, MOHAMMADHOSSEIN, & SHIRANI, KOUROSH. (2018). THE APPLICATION OF ARTIFICIAL NEURAL NETWORK ON LANDSLIDE SUSCEPTIBILITY MAPPING DEVELOPED BY FREQUENCY RATIO AND AHP IN OLIYA'S PADENA IN SEMIROM. WATERSHED ENGINEERING AND MANAGEMENT, 10(3 ), 332-349. SID. https://sid.ir/paper/234698/en

Vancouver: Copy

ARABAMERI ALIREZA, REZAEEI KHALIL, RAMSHET MOHAMMADHOSSEIN, SHIRANI KOUROSH. THE APPLICATION OF ARTIFICIAL NEURAL NETWORK ON LANDSLIDE SUSCEPTIBILITY MAPPING DEVELOPED BY FREQUENCY RATIO AND AHP IN OLIYA'S PADENA IN SEMIROM. WATERSHED ENGINEERING AND MANAGEMENT[Internet]. 2018;10(3 ):332-349. Available from: https://sid.ir/paper/234698/en

IEEE: Copy

ALIREZA ARABAMERI, KHALIL REZAEEI, MOHAMMADHOSSEIN RAMSHET, and KOUROSH SHIRANI, “THE APPLICATION OF ARTIFICIAL NEURAL NETWORK ON LANDSLIDE SUSCEPTIBILITY MAPPING DEVELOPED BY FREQUENCY RATIO AND AHP IN OLIYA'S PADENA IN SEMIROM,” WATERSHED ENGINEERING AND MANAGEMENT, vol. 10, no. 3 , pp. 332–349, 2018, [Online]. Available: https://sid.ir/paper/234698/en

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