Abstract
This study proposes a novel hybrid machine learning approach for modeling of rainfall-induced shallow landslides. The proposed approach is a combination of an instance-based learning algorithm (k-NN) and Rotation Forest (RF), state of the art machine techniques that have seldom explored for landslide modeling. The Lang Son city area (Vietnam) is selected as a case study. For this purpose, a spatial database for the study area was constructed, and then was used to build and evaluate the hybrid model. Performance of the model was assessed using Receiver Operating Characteristic (ROC), area under the ROC curve (AUC), success rate and prediction rate, and several statistical evaluation metrics. The results showed that the model has high performance with both the training data (AUC = 0.948) and the validation data (AUC = 0.848). The results were compared with those obtained from soft computing techniques, i.e. Random Forest, J48 Decision Trees, and Multilayer Perceptron Neural Networks. Overall, the performance of the proposed model is better than those obtained from the above methods. Therefore, the proposed model is a promising tool for landslide modeling. The research result can be highly useful for land use planning and management in landslide prone areas.
Highlights
Development of landslide mitigation strategy is considered to be the most effective and economical way to reduce landslide losses and minimize landslide risks [1]
T2.h1.eSsttuuddyyAareraea is located in the Lang Son city area, near the Vietnam-China border, which belongs to the norTtheassttuedrny paraerat oisf lVoiceattnedamin(FthigeuLraen1g).SIotncocviteyrsaraena,anrearotfhaebVoiuetn1a6m8‐kCmhi2n,abebtowrdeeern, wlohnigchitudes 106◦4b1e’l3o4n”gsEtoanthde1n0o6r◦th4e8a’3st2e”rnEp, aarntdoflaVtiietutndaems (2F1i◦g4u9re’413)”
The operation of the rotation forest for new data XN is as follows: (i) Build the transformed data YN = XN Ria run it through the L classifiers to get degree of support for the landslide and the non-landslide classes, di,j with i = 1, . . . ,L; j = 1, 2 for the landslide and the non-landslide classes, respectively. (ii) Landslide susceptibility index (LSI) is estimated for each pixel of XN using the average combination method as follows:
Summary
Development of landslide mitigation strategy is considered to be the most effective and economical way to reduce landslide losses and minimize landslide risks [1]. The recent developments of Remote Sensing and GIS (Geographic Information Systems) have provided powerful tools for acquisition and processing of high quality data for landslide studies, the prediction power of landslide models is still a debated subject because the quality of susceptibility maps is clearly dependent on the method used [3,4,5,6]. Starting in the early 1990s, ensemble-based systems have become an important research area in machine learning with various techniques have been proposed These systems can be established through combinations of two or more methods and techniques [36,37,38,39,40,41,42] or ensemble frameworks such as Stacking, Bagging, AdaBoost, Random Subspace, MultiBoost, Random Forests, Diverse DECORATE (Ensemble Creation by Oppositional Relabeling of Artificial Training Examples), and Rotation Forest [43,44]. Decision Trees, and Multilayer Perceptron Neural Networks, and conclusions are given
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