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Spatial Prediction of Rainfall Induced Shallow Landslides Using Adaptive-Network-Based Fuzzy Inference System and Particle Swarm Optimization: A Case Study at the Uttarakhand Area, India

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Advances and Applications in Geospatial Technology and Earth Resources (GTER 2017)

Abstract

Landslides generally occur during rainy season in Himalayas. Most of the landslides observed in the Uttarakhand part of Himalaya, India are of shallow nature. In the present study, we proposed a hybrid model Particle Swarm Optimization based Adaptive-Network-Based Fuzzy Inference System (PSOANFIS), which is a hybrid intelligent approach of Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO), for spatial prediction of shallow landslides in part of Uttarakhand State. Firstly, a total of 1295 historical landslide events occurred in the area were identified and mapped from satellite images in conjunction with available historical data from reports to construct a landslide inventory map. In addition, 16 affecting factors (slope angle, slope aspect, elevation, curvature, plan curvature, profile curvature, lithology, soil, distance to lineaments, lineament density, land cover, rainfall, road networks, distance to roads, road density, river networks, distance to river, and river density) were taken into account for landslide spatial modeling. Datasets (training and testing) were then generated from the analysis of the collected data using GIS application. Thereafter, landslide model PSOANFIS was constructed using training dataset for spatial prediction of landslides. Performance of the proposed hybrid model has been compared with another benchmark landslide model namely Support Vector Machines (SVM). Lastly, the predictive capability of the hybrid model was validated using Receiver Operating Characteristic (ROC) curve and Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) indexes. The results of the present study show that the PSOANFIS model performed well for spatial prediction of rainfall induced shallow landslides, thus the PSOANFIS method can also be applied for the development of better landslide predictive models in other landslide prone areas of the world.

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Acknowledgement

Authors are thankful to the Director, Bhaskarcharya Institute for Space Applications and Geo-Informatics, Gujarat, India for providing facilities to carry out this research work.

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Authors and Affiliations

  1. Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Viet Nam

    Binh Thai Pham

  2. Department of Science and Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar, India

    Indra Prakash

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  1. Binh Thai Pham
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  2. Indra Prakash
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Correspondence to Binh Thai Pham .

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Editors and Affiliations

  1. GIS group, Department of Business and IT, University College of Southeast Norway , Bo i Telemark, Norway

    Dieu Tien Bui

  2. Department of Underground and Mining Construction, Hanoi University of Mining and Geology , Hanoi, Vietnam

    Anh Ngoc Do

  3. Department of Exploration Geology, Hanoi University of Mining and Geology , Hanoi, Vietnam

    Hoang-Bac Bui

  4. Faculty of Civil Engineering, Duy Tan University , Da Nang, Vietnam

    Nhat-Duc Hoang

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Pham, B.T., Prakash, I. (2018). Spatial Prediction of Rainfall Induced Shallow Landslides Using Adaptive-Network-Based Fuzzy Inference System and Particle Swarm Optimization: A Case Study at the Uttarakhand Area, India. In: Tien Bui, D., Ngoc Do, A., Bui, HB., Hoang, ND. (eds) Advances and Applications in Geospatial Technology and Earth Resources. GTER 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-68240-2_14

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