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Open AccessArticle

Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction

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Vietnam Academy for Water Resources, Hanoi 100000, Vietnam
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Department of Resource and Environment Management, School of Agriculture and Resources, Vinh University, Vinh 460000, Vietnam
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Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
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Department of Rangeland and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan 87317-53153, Iran
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Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj 66169-49688, Iran
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Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, Iran
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Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382007, India
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Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi 100000, Vietnam
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Department of Geotechnical Engineering, Hydraulic Construction Institute, Vietnam Academy for Water Resources, 3/95 Chua Boc Street, Ha Noi 100000, Viet Nam
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NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam
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Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Korea
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Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Korea
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Geographic Information System group, Department of Business and IT, University College of Southeast Norway, N-3800 Bø i Telemark, Norway
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Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(14), 2824; https://doi.org/10.3390/app9142824
Received: 24 June 2019 / Revised: 11 July 2019 / Accepted: 12 July 2019 / Published: 15 July 2019
(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)
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. View Full-Text
Keywords: landslides; ensemble techniques; machine learning; goodness-of-fit; Vietnam landslides; ensemble techniques; machine learning; goodness-of-fit; Vietnam
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Nguyen, P.T.; Tuyen, T.T.; Shirzadi, A.; Pham, B.T.; Shahabi, H.; Omidvar, E.; Amini, A.; Entezami, H.; Prakash, I.; Phong, T.V.; Vu, T.B.; Thanh, T.; Saro, L.; Bui, D.T. Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction. Appl. Sci. 2019, 9, 2824.

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