PREDICTIVE MODEL OF DISINFECTION BYPRODUCTS FROM NATURAL ORGANIC MATTERS USING HIGH-RESOLUTION MASS SPECTROMETRY AND MACHINE LEARNING DOI
Kenji Yoshida, Jibao Liu, Eunsang Kwon

et al.

Japanese Journal of JSCE, Journal Year: 2024, Volume and Issue: 80(25), P. n/a - n/a

Published: Jan. 1, 2024

Language: Английский

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

Zhenpeng Xu,

Hongxia Zhao, Jinyang Wang

et al.

Journal of environmental chemical engineering, Journal Year: 2025, Volume and Issue: 13(2), P. 115741 - 115741

Published: Feb. 10, 2025

Language: Английский

Citations

1
Integrating machine learning and traditional methods for cadmium prediction and bioavailability assessment in Paeoniae Radix Alba: a case study from Bozhou, Anhui Province DOI

Fang He,

Quan Tang, Dong Li

et al.

Environmental Geochemistry and Health, Journal Year: 2025, Volume and Issue: 47(6)

Published: May 21, 2025

Language: Английский

Citations

0
Analysis and Selection of Multiple Machine Learning Methodologies in PyCaret for Monthly Electricity Consumption Demand Forecasting DOI Creative Commons
José Quintana, Alberto Cristobal Flores Quispe, Nilton Cesar León-Calvo

et al.

Published: Aug. 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.

Language: Английский

Citations

1
PREDICTIVE MODEL OF DISINFECTION BYPRODUCTS FROM NATURAL ORGANIC MATTERS USING HIGH-RESOLUTION MASS SPECTROMETRY AND MACHINE LEARNING DOI
Kenji Yoshida, Jibao Liu, Eunsang Kwon

et al.

Japanese Journal of JSCE, Journal Year: 2024, Volume and Issue: 80(25), P. n/a - n/a

Published: Jan. 1, 2024

Language: Английский

Citations

0