Landslide Susceptibility Mapping: Analysis of Different Feature Selection Techniques with Artificial Neural Network Tuned by Bayesian and Metaheuristic Algorithms

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-09, Vol.15 (17), p.4330
• Main Authors: Abbas, Farkhanda, Zhang, Feng, Abbas, Fazila, Ismail, Muhammad, Iqbal, Javed, Hussain, Dostdar, Khan, Garee, Alrefaei, Abdulwahed Fahad, Albeshr, Mohammed Fahad
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; Artificial neural networks; Back propagation; Bayesian analysis; Bayesian techniques; Classification; Context; Early warning systems; Effectiveness; Entropy; Feature selection; feature selection techniques; Gaussian process; Genetic algorithms; Heuristic methods; hyperparameters; Infrastructure; land sliding; Landslides; Landslides & mudslides; Mapping; Mathematical models; Mathematical optimization; metaheuristic techniques; Methods; Natural disasters; Neural networks; Optimization algorithms; Pakistan; Particle swarm optimization; Performance evaluation; Planning; Remote sensing; Route optimization; Spatial analysis; Susceptibility; Variables; Vigilance
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15174330

The most frequent and noticeable natural calamity in the Karakoram region is landslides. Extreme landslides have occurred frequently along Karakoram Highway, particularly during monsoons, causing a major loss of life and property. Therefore, it is necessary to look for a solution to increase growth and vigilance in order to lessen losses related to landslides caused by natural disasters. By utilizing contemporary technologies, an early warning system might be developed. Artificial neural networks (ANNs) are widely used nowadays across many industries. This paper’s major goal is to provide new integrative models for assessing landslide susceptibility in a prone area in the north of Pakistan. To achieve this, the training of an artificial neural network (ANN) was supervised using metaheuristic and Bayesian techniques: Particle Swarm Optimization (PSO) algorithm, Genetic algorithm (GA), Bayesian Optimization Gaussian Process (BO_GP), and Bayesian Optimization Tree-structured Parzen Estimator (BO_TPE). In total, 304 previous landslides and the eight most prevalent conditioning elements were combined to form a geospatial database. The models were hyperparameter optimized, and the best ones were employed to generate susceptibility maps. The obtained area under the curve (AUC) accuracy index demonstrated that the maps produced by both Bayesian and metaheuristic algorithms are highly accurate. The effectiveness and efficiency of applying ANNs for landslide mapping, susceptibility analysis, and forecasting were studied in this research, and it was observed from experimentation that the performance differences for GA, BO_GP, and PSO compared to BO_TPE were relatively small, ranging from 0.32% to 1.84%. This suggests that these techniques achieved comparable performance to BO_TPE in terms of AUC. However, it is important to note that the significance of these differences can vary depending on the specific context and requirements of the ML task. Additionally, in this study, we explore eight feature selection algorithms to determine the geospatial variable importance for landslide susceptibility mapping along the Karakoram Highway (KKH). The algorithms considered include Information Gain, Variance Inflation Factor, OneR Classifier, Subset Evaluators, principal components, Relief Attribute Evaluator, correlation, and Symmetrical Uncertainty. These algorithms enable us to evaluate the relevance and significance of different geospatial variables in predicting landslide s