Groundwater quality assessment using machine learning models: a comprehensive study on the industrial corridor of a semi-arid region

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: unknown

Published: July 4, 2024

Language: Английский

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An Experimental Investigation to Predict the Compressive Strength of Lightweight Ceramsite Aggregate UHPC Using Boosting and Bagging Techniques

Materials Today Communications, Journal Year: 2024, Volume and Issue: unknown, P. 110759 - 110759

Published: Oct. 1, 2024

Language: Английский

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Advanced machine learning techniques for predicting concrete mechanical properties: a comprehensive review of models and methodologies

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2024, Volume and Issue: 8(1)

Published: Dec. 18, 2024

Language: Английский

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Optimizing Rain Prediction Model Using Random Forest and Grid Search Cross-Validation for Agriculture Sector

Matrik Jurnal Manajemen Teknik Informatika dan Rekayasa Komputer, Journal Year: 2024, Volume and Issue: 23(3), P. 519 - 530

Published: July 19, 2024

Agriculture, as a sector that is highly influenced by weather conditions, faces challenges due to increasingly unpredictable changes in patterns. The aim of this research create an optimal rainfall prediction model help farmers irrigation schedules, use fertilizer, and planting protect plants from extreme events. method used obtain the best rain random forest algorithm grid search cross-validation algorithm. Random Forest, known for its robustness accuracy, emerged suitable predicting rain. utilizing substantial dataset West Nusa Tenggara Meteorology, Climatology, Geophysics Agency covering period 2000 2023. data then processed first ensure readiness use. This process involves removing outlier points, empty entries, unused features. After preprocessing stage, underwent training using Forest algorithm, resulting R-squared value 0.1334. To model, Grid Search Cross Validation used. results obtained with 0.0268. will be predict agricultural sector. concludes we can get combining Gird Cross-Validation. For further research, compare other methods, add features, combine datasets wider area.

Language: Английский

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Optimizing Adaptive Boosting Model for Breast Cancer Prediction Using Principal Component Analysis and Random Oversampling Techniques

Published: Aug. 29, 2024

Language: Английский

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