Prediction and mechanism analysis of octanol-air partition coefficient for persistent organic pollutants based on machine learning models

Journal of environmental chemical engineering, Год журнала: 2025, Номер 13(2), С. 115741 - 115741

Опубликована: Фев. 10, 2025

Язык: Английский

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Integrating machine learning and traditional methods for cadmium prediction and bioavailability assessment in Paeoniae Radix Alba: a case study from Bozhou, Anhui Province

Environmental Geochemistry and Health, Год журнала: 2025, Номер 47(6)

Опубликована: Май 21, 2025

Язык: Английский

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Analysis and Selection of Multiple Machine Learning Methodologies in PyCaret for Monthly Electricity Consumption Demand Forecasting

Опубликована: Авг. 28, 2024

This study investigates the application of several machine learning models using PyCaret to forecast monthly demand for electricity consumption; we analyze historical data consumption readings Cuajone Mining Unit company Minera Southern Peru Copper Corporation, recorded in yearbooks from decentralized office Ministry Energy and Mines Moquegua region between 2008 2018. We evaluated performance 27 available consumption, selecting three most effective models: Exponential Smoothing, AdaBoost with Conditional Deseasonalize Detrending ETS (Error-Trend-Seasonality). these eight metrics: MASE, RMSSE, MAE, RMSE, MAPE, SMAPE, R2, calculation time. Among analyzed models, Smoothing demonstrated best a MASE 0.8359, an MAE 4012.24 RMSE 5922.63; among 5922.63, followed by Detrending, while also provided competitive results. Forecasts 2018 were compared actual data, confirming high accuracy models. These findings provide robust energy management planning framework, highlighting potential methodologies optimize forecasting.

Язык: Английский

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PREDICTIVE MODEL OF DISINFECTION BYPRODUCTS FROM NATURAL ORGANIC MATTERS USING HIGH-RESOLUTION MASS SPECTROMETRY AND MACHINE LEARNING

Japanese Journal of JSCE, Год журнала: 2024, Номер 80(25), С. n/a - n/a

Опубликована: Янв. 1, 2024

Язык: Английский

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