Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China Karakoram Highway

Land, Journal Year: 2025, Volume and Issue: 14(1), P. 172 - 172

Published: Jan. 15, 2025

The effectiveness of data-driven landslide susceptibility mapping relies on data integrity and advanced geospatial analysis; however, selecting the most suitable method identifying key regional factors remains a challenging task. To address this, this study assessed performance six machine learning models, including Convolutional Neural Networks (CNNs), Random Forest (RF), Categorical Boosting (CatBoost), their CNN-based hybrid models (CNN+RF CNN+CatBoost), Stacking Ensemble (SE) combining CNN, RF, CatBoost in along Karakoram Highway northern Pakistan. Twelve were examined, categorized into Topography/Geomorphology, Land Cover/Vegetation, Geology, Hydrology, Anthropogenic Influence. A detailed inventory 272 occurrences was compiled to train models. proposed stacking ensemble improve modeling, with achieving an AUC 0.91. Hybrid modeling enhances accuracy, CNN–RF boosting RF’s from 0.85 0.89 CNN–CatBoost increasing CatBoost’s 0.87 0.90. Chi-square (χ2) values (9.8–21.2) p-values (<0.005) confirm statistical significance across This identifies approximately 20.70% area as high very risk, SE model excelling detecting high-risk zones. Key influencing showed slight variations while multicollinearity among variables remained minimal. approach reduces uncertainties, prediction supports decision-makers implementing effective mitigation strategies.

Language: Английский

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Performance evaluation of convolutional neural network and vision transformer models for groundwater potential mapping

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132840 - 132840

Published: Feb. 1, 2025

Language: Английский

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Enhancing PM2.5 Air Pollution Prediction Performance by Optimizing the Echo State Network (ESN) Deep Learning Model Using New Metaheuristic Algorithms

Urban Science, Journal Year: 2025, Volume and Issue: 9(5), P. 138 - 138

Published: April 23, 2025

Air pollution presents significant risks to both human health and the environment. This study uses air meteorological data develop an effective deep learning model for hourly PM2.5 concentration predictions in Tehran, Iran. evaluates efficient metaheuristic algorithms optimizing hyperparameters improve accuracy of predictions. The optimal feature set was selected using Variance Inflation Factor (VIF) Boruta-XGBoost methods, which indicated elimination NO, NO2, NOx. highlighted PM10 as most important feature. Wavelet transform then applied extract 40 features enhance prediction accuracy. Hyperparameters weights matrices Echo State Network (ESN) were determined algorithms, with Salp Swarm Algorithm (SSA) demonstrating superior performance. evaluation different criteria revealed that ESN-SSA outperformed other hybrids original ESN, LSTM, GRU models.

Language: Английский

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Spatial Prediction of Human Brucellosis Susceptibility Using an Explainable Optimized Adaptive Neuro Fuzzy Inference System

Acta Tropica, Journal Year: 2024, Volume and Issue: unknown, P. 107483 - 107483

Published: Nov. 1, 2024

Language: Английский

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