Explainable machine learning model for liquefaction potential assessment of soils using XGBoost-SHAP

Soil Dynamics and Earthquake Engineering, Год журнала: 2022, Номер 165, С. 107662 - 107662

Опубликована: Ноя. 30, 2022

Язык: Английский

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Optimized hybrid ensemble learning approaches applied to very short-term load forecasting

International Journal of Electrical Power & Energy Systems, Год журнала: 2023, Номер 155, С. 109579 - 109579

Опубликована: Окт. 26, 2023

The significance of accurate short-term load forecasting (STLF) for modern power systems' efficient and secure operation is paramount. This task intricate due to cyclicity, non-stationarity, seasonality, nonlinear consumption time series data characteristics. rise accessibility in the industry has paved way machine learning (ML) models, which show potential enhance STLF accuracy. paper presents a novel hybrid ML model combining Gradient Boosting Regressor (GBR), Extreme (XGBoost), k-Nearest Neighbors (kNN), Support Vector Regression (SVR), examining both standalone integrated, coupled with signal decomposition techniques like STL, EMD, EEMD, CEEMDAN, EWT. Through Automated Machine Learning (AutoML), these models are integrated their hyperparameters optimized, predicting each component using from two sources: National Operator Electric System (ONS) Independent Operators New England (ISO-NE), boosting prediction capacity. For 2019 ONS dataset, EWT XGBoost yielded best results very (VSTLF) an RMSE 1,931.8 MW, MAE 1,564.9 MAPE 2.54%. These findings highlight necessity diverse approaches VSTLF problem, emphasizing adaptability strength combined techniques.

Язык: Английский

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Predicting occurrence of liquefaction-induced lateral spreading using gradient boosting algorithms integrated with particle swarm optimization: PSO-XGBoost, PSO-LightGBM, and PSO-CatBoost

Acta Geotechnica, Год журнала: 2023, Номер 18(6), С. 3403 - 3419

Опубликована: Янв. 2, 2023

Язык: Английский

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Short-Term Rockburst Damage Assessment in Burst-Prone Mines: An Explainable XGBOOST Hybrid Model with SCSO Algorithm

Rock Mechanics and Rock Engineering, Год журнала: 2023, Номер 56(12), С. 8745 - 8770

Опубликована: Сен. 2, 2023

Язык: Английский

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Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development

Journal of Cleaner Production, Год журнала: 2024, Номер 444, С. 141035 - 141035

Опубликована: Фев. 8, 2024

Язык: Английский

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Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis

Environmental Technology & Innovation, Год журнала: 2024, Номер 35, С. 103655 - 103655

Опубликована: Май 5, 2024

Forest fires pose a significant threat to ecosystems and socio-economic activities, necessitating the development of accurate predictive models for effective management mitigation. In this study, we present novel machine learning approach combined with Explainable Artificial Intelligence (XAI) techniques predict forest fire susceptibility in Nainital district. Our innovative methodology integrates several robust — AdaBoost, Gradient Boosting Machine (GBM), XGBoost Random Deep Neural Network (DNN) as meta-model stacking framework. This not only utilises individual strengths these models, but also improves overall prediction performance reliability. By using XAI techniques, particular SHAP (SHapley Additive exPlanations) LIME (Local Interpretable Model-agnostic Explanations), improve interpretability provide insights into decision-making processes. results show effectiveness ensemble model categorising different zones: very low, moderate, high high. particular, identified extensive areas susceptibility, precision, recall F1 values underpinning their effectiveness. These achieved ROC AUC above 0.90, performing exceptionally well an 0.94. The are remarkably inclusion confidence intervals most important metrics all emphasises robustness reliability supports practical use management. Through summary plots, analyze global variable importance, revealing annual rainfall Evapotranspiration (ET) key factors influencing susceptibility. Local analysis consistently highlights importance rainfall, ET, distance from roads across models. study fills research gap by providing comprehensive interpretable modelling that our ability effectively manage risk is consistent environmental protection sustainable goals.

Язык: Английский

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Evaluation and analysis of liquefaction potential of gravelly soils using explainable probabilistic machine learning model

Computers and Geotechnics, Год журнала: 2024, Номер 167, С. 106051 - 106051

Опубликована: Янв. 8, 2024

Язык: Английский

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The Role of Utilizing Artificial Intelligence and Renewable Energy in Reaching Sustainable Development Goals

Renewable Energy, Год журнала: 2024, Номер 235, С. 121311 - 121311

Опубликована: Сен. 7, 2024

Язык: Английский

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Boosting-Based Machine Learning Applications in Polymer Science: A Review

Polymers, Год журнала: 2025, Номер 17(4), С. 499 - 499

Опубликована: Фев. 14, 2025

The increasing complexity of polymer systems in both experimental and computational studies has led to an expanding interest machine learning (ML) methods aid data analysis, material design, predictive modeling. Among the various ML approaches, boosting methods, including AdaBoost, Gradient Boosting, XGBoost, CatBoost LightGBM, have emerged as powerful tools for tackling high-dimensional complex problems science. This paper provides overview applications science, highlighting their contributions areas such structure-property relationships, synthesis, performance prediction, characterization. By examining recent case on techniques this review aims highlight potential advancing characterization, optimization materials.

Язык: Английский

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Predicting and Mapping of Soil Organic Matter with Machine Learning in the Black Soil Region of the Southern Northeast Plain of China

Agronomy, Год журнала: 2025, Номер 15(3), С. 533 - 533

Опубликована: Фев. 22, 2025

The estimation of soil organic matter (SOM) content is essential for understanding the chemical, physical, and biological functions soil. It also an important attribute reflecting quality black In this study, machine learning algorithms support vector (SVM), neural network (NN), decision tree (DT), random forest (RF), extreme gradient boosting (GBM), generalized linear model (GLM) were used to study accurate prediction SOM in Tieling County, City, Liaoning Province, China. models trained by using 1554 surface samples 19 auxiliary variables. Recursive feature elimination was as a selection method identify effective results showed that Normalized Difference Vegetation Index (NDVI) elevation key Based on 10-fold cross-validation, RF had highest accuracy. terms accuracy, coefficient determination 0.77, root mean square error 2.85. average 20.15 g/kg. spatial distribution shows higher concentrated east west, while lower found middle. cultivated land than land.

Язык: Английский

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