Journal ArticleDOI
Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization
Dieu Tien Bui,Dieu Tien Bui,Tran Anh Tuan,Nhat-Duc Hoang,Nguyen Quoc Thanh,Duy Nguyen,Ngo Van Liem,Biswajeet Pradhan,Biswajeet Pradhan +8 more
TLDR
The proposed model, namely LSSVM-BC, is a promising tool for spatial prediction of landslides at the study area and is useful for landuse planning for the Lao Cai area.Abstract:
The main objective of this study is to produce a landslide susceptibility map for the Lao Cai area (Vietnam) using a new hybrid intelligent method based on least squares support vector machines (LSSVM) and artificial bee colony (ABC) optimization, namely LSSVM-BC. LSSVM and ABC are state-of-the-art soft computing techniques that have been rarely utilized in landslide susceptibility assessment. LSSVM is adopted to develop landslide prediction model whereas ABC was used to optimize the prediction model by identifying an appropriate set of the LSSVM hyper-parameters. To establish the hybrid intelligent method, a GIS database with ten landslide-influencing factors and 340 landslide locations that occurred mainly during the last 20-years was constructed. These historical landslide locations were collected from the existing inventories that sourced from (i) five landslide projects carried out in this study areas before and (ii) interpretations of SPOT satellite images with resolution of 2.5 m. The study area was geographically split into two different parts, with landslides located in the first part was used for building models whereas the other landslides in the second part was used for the model validation. Performance of the LSSVM-BC model was assessed using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Result shows that the prediction power of the model is good with the area under the curve (AUC) = 0.900. Experiments have pointed out the prediction power of the LSSVM-BC is better than that obtained from the popular support vector machines. Therefore, the proposed model is a promising tool for spatial prediction of landslides at the study area. The landslide susceptibility map is useful for landuse planning for the Lao Cai area.read more
Citations
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Journal ArticleDOI
Comparative Study in Fuzzy Controller Optimization Using Bee Colony, Differential Evolution, and Harmony Search Algorithms
Oscar Castillo,Fevrier Valdez,José Soria,Leticia Amador-Angulo,Patricia Ochoa,Cinthia Peraza +5 more
TL;DR: Simulation results provide evidence that the FDE algorithm outperforms the results of the FBCO and FHS algorithms in the optimization of fuzzy controllers and the better errors are found with the implementation of the fuzzy systems to enhance each proposed algorithm.
Journal ArticleDOI
Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran
Omid Rahmati,Saleh Yousefi,Zahra Kalantari,Evelyn Uuemaa,Evelyn Uuemaa,Teimur Teimurian,Saskia Keesstra,Tien Dat Pham,Dieu Tien Bui +8 more
TL;DR: Overall, multi-hazard exposure modeling revealed that valleys and areas close to the Chalous Road, one of the most important roads in Iran, were associated with high and very high levels of risk.
Journal ArticleDOI
Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm
Sedigheh Mohamadi,Saad Sh. Sammen,Fatemeh Panahi,Mohammad Ehteram,Ozgur Kisi,Amir Mosavi,Ali Najah Ahmed,Ahmed El-Shafie,Ahmed El-Shafie,Nadhir Al-Ansari +9 more
TL;DR: The general results indicated that the NPA and wavelet coherence analysis are useful tools for modelling drought indices and suggested that the hybrid models performed better than the standalone MLP, RBFNN, ANFIS, and SVM models.
Journal ArticleDOI
How can statistical and artificial intelligence approaches predict piping erosion susceptibility
Mohsen Hosseinalizadeh,Narges Kariminejad,Omid Rahmati,Saskia Keesstra,Mohammad Alinejad,Ali Mohammadian Behbahani +5 more
TL;DR: The main objective of this research is to develop a novel modeling approach by using three machine learning algorithms-mixture discriminant analysis (MDA), flexible discriminantAnalysis (FDA), and support vector machine (SVM) in addition to an unmanned aerial vehicle (UAV) images to map susceptibility to piping erosion in the loess-covered hilly region of Golestan Province, Northeast Iran.
Journal ArticleDOI
Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran
Viet-Ha Nhu,Danesh Zandi,Himan Shahabi,Kamran Chapi,Ataollah Shirzadi,Nadhir Al-Ansari,Sushant K. Singh,Jie Dou,Hoang Nguyen +8 more
TL;DR: This paper aims to apply and compare the performance of the three machine learning algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternatingdecision tree (ADTree)–to the real world.
References
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