Optimization of Various Machine Learning Concepts to Evaluate Landslide Susceptibility - XGBoost, k-NN and MLP using PSO Algorithm

Aug 5, 2026

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Landslides pose major threats to natural and built environments, making accurate susceptibility prediction essential for effective hazard mitigation. This study improves machine learning performance by monitoring how optimization algorithms affect classifier results in landslide susceptibility mapping. Classifiers evaluated include k-Nearest Neighbors, MultiLayer Perceptron, and Extreme Gradient Boosting. Particle Swarm Optimization is applied to optimize hyperparameters and enhance robustness in complex spatial patterns. Results show XGBoost as the best traditional model, and PSO-XGBoost further improves predictive capability. SHAP feature importance identifies slope as dominant, followed by TRI, rainfall, aspect, TWI, and curvature, with lithology and vegetation indicators contributing moderately.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Introduction

Landslide risk and the need for predictive models

Machine learning approaches for susceptibility mapping

Optimization motivation and study novelty

Environmental Earth Sciences (2026) 85:101 [https://doi.org/10.1007/s12665-026-12822-7](https://doi.org/10.1007/s12665-026-12822-7) ORIGINAL ARTICLE Optimization of various machine learning concepts to evaluate landslide susceptibility: XGBoost, k-NN and MLP using PSO algorithm Hazem Ghassan Abdo1,2 · Sahar Mohammed Richi3 · Bilel Zerouali4 · Saeed Alqadhi5 · Okan Mert Katipoğlu6 · Pankaj Prasad7 · Hasan Arman8 · Jasem A Albanai9 · Javed Mallick5 Received: 20 May 2025 / Accepted: 17 January 2026 © The Author(s) 2026 Abstract Landslides significantly threaten natural and built environments, necessitating accurate prediction models for effective hazard mitigation. There is an urgent need to further improve the performance of machine learning algorithms in predicting landslide susceptibility by monitoring the impact of optimization algorithms on the performance of these models. This study evaluates the performance of various machine learning classifiers, including k-Nearest Neighbors (kNN), MultiLayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), for landslide susceptibility mapping. Additionally, Particle Swarm Optimization (PSO) is employed to enhance model performance by optimizing hyperparameters. Mountainous areas in the eastern Mediterranean (the northern Kabir River basin in western Syria) were identified as a result of the high frequency of landslide events over the past two decades. Nineteen factors causing landslides were identified, with no factor excluded, as a result of a multicollinearity test. The results indicate that XGBoost achieves the highest performance among traditional models. When integrated with PSO, the PSO-XGBoost model further improves classification performance, demonstrating its robustness in handling complex spatial patterns. Feature importance analysis using SHAP confirms slope as the dominant factor, followed by TRI, rainfall, Aspect, TWI, and curvature, highlighting the role of topography and hydrology in landslide occurrence. Moderate lithology, NDVI, and LULC contributions and lower importance of Flow Accumulation and Soil Depth suggest complex environmental interactions. Model predictions show varying susceptibility distributions. PSO-MLP assigns the highest very high susceptibility (44.09%), while PSO-XGBoost provides a balanced classification (31.13%) . The PSO-XGBoost model demonstrates superior predictive capability, offering reliable landslide susceptibility maps for disaster risk management and land-use planning. Keywords Optimization; landslide susceptibility; XGBoost · K-NN · MLP · PSO Introduction A landslide is classified as a morphodynamic activity of slope materials formed by weathering processes, consisting of rock, soil, and debris, under the influence of various driving factors, particularly gravity, seismic and volcanic activity, and rainstorms (Wei et al. 2021, 2024; Dey and Das 2025; Tanoumand et al. 2025; Hallal et al. 2024; Dai et al. 2025) . In addition to the acceleration of human activities, including uncontrolled urbanization, road construction, intensive agriculture, slope modification, and mining, landslides have devastating spatial consequences for human lives, infrastructure, economic activity, transportation, and Extended author information available on the last page of the article ecological diversity, while also significantly disrupting the quality of services provided (Susena et al. 2025; Liu et al. 2024a, b; Hou et al. 2025; Badreldin et al. 2025; Tebboucheet al. 2022) . The latest statistics indicate that approximately 3.8 km2of land worldwide is threatened by landslide events. This means that more than 290 million people are at risk of landslides (Aslam et al. 2022) . A study Froude and Petley (2018) indicates that more than 180,000 people have been killed by more than 37,000 landslide events worldwide. Landslides, particularly those of the rocky type, represent a persistent geomorphological threat to mountainous and p

FAQs

Which machine learning classifiers are compared for landslide susceptibility mapping?

The study evaluates k-Nearest Neighbors (kNN), MultiLayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost).

How does Particle Swarm Optimization (PSO) improve model performance?

PSO is used to optimize model hyperparameters, and the combined PSO-XGBoost approach increases classification performance and robustness for complex spatial patterns.

What factors are identified as most influential in landslide occurrence?

SHAP analysis shows slope is the dominant factor, followed by TRI, rainfall, aspect, TWI, and curvature; lithology, NDVI, and LULC contribute moderately while flow accumulation and soil depth are less important.

Aug 5, 2026

Upload by Ophelia