A novel implementation of pre-processing approaches and hybrid kernel-based model for short- and long-term groundwater drought forecasting

Journal of Hydrology, Год журнала: 2025, Номер 652, С. 132667 - 132667

Опубликована: Янв. 6, 2025

Язык: Английский

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Optimized prediction modeling of micropollutant removal efficiency in forward osmosis membrane systems using explainable machine learning algorithms

Journal of Water Process Engineering, Год журнала: 2024, Номер 66, С. 105937 - 105937

Опубликована: Авг. 19, 2024

Язык: Английский

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Urban flood hazard assessment using FLA-optimized boost algorithms in Ankara, Türkiye

Applied Water Science, Год журнала: 2025, Номер 15(4)

Опубликована: Март 19, 2025

Язык: Английский

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Modeling Short-Term Drought for SPEI in Mainland China Using the XGBoost Model

Atmosphere, Год журнала: 2025, Номер 16(4), С. 419 - 419

Опубликована: Апрель 4, 2025

Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework short-term forecasting using SPEI time series (1979–2020) evaluates three predictive models: (1) baseline XGBoost model (XGBoost1), (2) feature-optimized variant incorporating Pearson correlation analysis (XGBoost2), (3) an enhanced CPSO-XGBoost integrating hybrid particle swarm optimization with dual mechanisms of binary feature selection parameter tuning. Key findings reveal spatiotemporal patterns: temporal-scale dependencies show all models exhibit limited capability at SPEI-1 (R2: 0.32–0.41, RMSE: 0.68–0.79) but achieve progressive accuracy improvement, peaking SPEI-12 where attains optimal performance 0.85–0.90, 0.33–0.43) 18.7–23.4% error reduction versus baselines. Regionally, humid zones (South China/Central-Southern) demonstrate peak (R2 ≈ 0.90, RMSE < 0.35), while arid regions (Northwest Desert/Qinghai-Tibet Plateau) dramatic improvement from 0.35, > 1.0) to 0.85, 52%). Multivariate probability density confirms the model’s robustness through capture nonlinear atmospheric-land interactions reduced parameterization uncertainties via intelligence optimization. The CPSO-XGBoost’s superiority stems synergistic optimization: enhances input relevance adaptive tuning improves computational efficiency, collectively addressing climate variability challenges across diverse terrains. These establish advanced early warning systems, providing critical support climate-resilient management risk mitigation spatiotemporally predictions.

Язык: Английский

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Investigating the performance of random oversampling and genetic algorithm integration in meteorological drought forecasting with machine learning

Deleted Journal, Год журнала: 2025, Номер 7(6)

Опубликована: Май 29, 2025

Язык: Английский

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Enhancing the accuracy of metaheuristic neural networks in predicting underground water levels using meteorological data and remote sensing: A case study of Ardabil Plain, Iran

Ain Shams Engineering Journal, Год журнала: 2024, Номер unknown, С. 103061 - 103061

Опубликована: Окт. 1, 2024

Язык: Английский

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Development of deep learning approaches for drought forecasting: a comparative study in a cold and semi-arid region

Earth Science Informatics, Год журнала: 2024, Номер 18(1)

Опубликована: Дек. 19, 2024

Язык: Английский

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