Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction

Applied Sciences

TranVan Phong¹  Himan Shahabi²  Tran Thanh³  Ebrahim Omidvar⁴  Ataollah Shirzadi⁵  Hersh Entezami⁶  TranThi Tuyen⁷  Indra Prakash⁸  DieuTien Bui⁹  ThaoBa Vu^1,10  BinhThai Pham^1,11  Ata Amini^1,12  Lee Saro^1,13  PhongTung Nguyen^1,14 

[1] Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382007, India;Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran;Department of Geotechnical Engineering, Hydraulic Construction Institute, Vietnam Academy for Water Resources, 3/95 Chua Boc Street, Ha Noi 100000, Viet Nam;Department of Rangeland and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan 87317-53153, Iran;Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran;Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, Iran;Department of Resource and Environment Management, School of Agriculture and Resources, Vinh University, Vinh 460000, Vietnam;;Department of Science &Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Korea;Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi 100000, Vietnam;Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam;Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj 66169-49688, Iran;NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam;Vietnam Academy for Water Resources, Hanoi 100000, Vietnam;

关键词: landslides;    ensemble techniques;    machine learning;    goodness-of-fit;    Vietnam;   

DOI  :  10.3390/app9142824

来源: DOAJ

【 摘 要 】

We proposed an innovative hybrid intelligent approach, namely, the multiboost based naïve bayes trees (MBNBT) method for the spatial prediction of landslides in the Mu Cang Chai District of Yen Bai Province, Vietnam. The MBNBT, which is an ensemble of the multiboost (MB) and naïve bayes trees (NBT) base classifier, has rarely been applied for landslide susceptibility mapping around the world. For the modeling, we selected 248 landslide locations in the hilly terrain of the study area. Fifteen landslide conditioning factors were selected for the construction of the database based on the one-R attribute evaluation (ORAE) technique. Model validation was done using statistical metrics, namely, sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and the area under the receiver operating characteristics curve (AUC). Performance of the hybrid model was evaluated and compared with popular soft computing benchmark models, namely, multiple perceptron neural network (MLPN), Support Vector Machines (SVM), and single NBT. Results indicated that the proposed MBNBT (AUC = 0.824) model outperformed the popular models, namely, the MLPN (AUC = 0.804), SVM (AUC = 0.804), and NBT (AUC = 0.800) models. Analysis of the model results also suggested that the MB meta classifier ensemble model could enhance the prediction power of the NBT model. Therefore, the MBNBT is a suitable method for the assessment of landslide susceptibility in landslide prone areas.

【 授权许可】

Unknown