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Assessment, Categorisation and Prediction of the Landslide-Affected Regions Using Soft Computing and Clustering Techniques

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Abstract

Landslides are known natural hazard that significantly impacts human lives by damaging assets and infrastructures. It is tough to collect field data due to the non-access of various remote places. Collection of landslide images affected by those faraway places can be a way out for post-landslide assessment. Once a landslide impacts a region, the heavily affected places are more prone to getting affected again in the future. Post-landslide assessment by the proposed model can provide a survey. It contains region-wise, heavily affected, medium-affected landslide accounts and non-affected regions, which can help future decisions like road, building construction, and other socio-economic decisions for the specific regions. Even in the worst landslide again, the region will get minor damage to properties and human lives in the future. The model applies image processing, soft computing, and clustering tools on those collected images and finally produces a prediction, post-assessment and categorisation in terms of heavily affected, medium-affected landslide, and non-affected regions. One hundred fifty statistical features including ripplet coefficient, fractal dimension, autocorrelation coefficient, gray level co-occurrence matrix, gray level run length matrix, Gabor coefficient, moments, histogram, and directional contrast are used in the model. A linear support vector machine for selecting principal features uses seven feature selection methods: Fisher score, FSV, infFS, Laplacian, L0, MCFS, and MUTinfFS. Soft computing models like fuzzy systems, neural networks and genetic algorithms, and hybridisation are applied. Akaike’s information criteria and Bayesian information criteria are used for the best model selection. Classification based on clustering algorithm, viz. partitioning around medoids, K-means, hierarchical single, hierarchical complete, hierarchical centroid, and affinity propagation clustering algorithms, is used. Finally, the optimal number of the clusters is selected based on the values of the Silhouette index, Davies–Bouldin, Calinski–Harabasz, Dunn index, Hubert–Levin, Krzanowski–Lai index. The models’ accuracy (0.88), sensitivity (0.85), specificity (0.91), precision (0.88), G-mean (0.88), and balanced accuracy (0.88) depict a good behaviour. Additionally, the metrics like F1 score (0.86), FMI (0.86), CSI (0.76), kappa (0.76), and MCC (0.76) indicates well balance positive, negative class behaviour and good classification ability of the model. Furthermore, the discriminant power (2.23) also portrays fine positive and negative classes distinguishability and the unbiased models’ classification. The receiver operating characteristic curve (ROC) also indicates the evidence of a better-performing model. Therefore, the model can provide a ready reference for predicting, categorising and assessing landslide-affected regions, which can benefit future planning of the zone concern, which eventually improves the socio-economic condition of the zone.

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  1. Department of Information Technology, Kalyani Government Engineering College, Kalyani, Nadia, West Bengal, India

    Md. Iqbal Quraishi

  2. Department of Computer Science and Engineering, Narula Institute of Technology, Kolkata, West Bengal, India

    J. Paul Choudhury

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  1. Md. Iqbal Quraishi
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Quraishi, M.I., Choudhury, J.P. Assessment, Categorisation and Prediction of the Landslide-Affected Regions Using Soft Computing and Clustering Techniques. J. Inst. Eng. India Ser. B 104, 579–602 (2023). https://doi.org/10.1007/s40031-023-00876-1

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