Machine Learning in Environmental Research: Common Pitfalls and Best Practices

Environmental Science & Technology, Год журнала: 2023, Номер 57(46), С. 17671 - 17689

Опубликована: Июнь 29, 2023

Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due the lack of familiarity methodological rigor, inadequate ML studies may lead spurious conclusions. In this study, we synthesized literature analysis with our own experience provided a tutorial-like compilation common pitfalls along best practice guidelines for research. We identified more than 30 key items evidence-based based on 148 highly cited articles exhibit misconceptions terminologies, proper sample size feature size, enrichment selection, randomness assessment, leakage management, splitting, method selection comparison, model optimization evaluation, explainability causality. By analyzing good examples supervised reference modeling paradigms, hope help researchers adopt rigorous preprocessing development standards accurate, robust, practicable uses applications.

Язык: Английский

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Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review

Sustainability, Год журнала: 2022, Номер 14(16), С. 9951 - 9951

Опубликована: Авг. 11, 2022

Air pollution is a major issue all over the world because of its impacts on environment and human beings. The present review discussed sources pollutants environmental health current research status forecasting techniques in detail; this study presents detailed discussion Artificial Intelligence methodologies Machine learning (ML) algorithms used early-warning systems; moreover, work emphasizes more (particularly Hybrid models) for various (e.g., PM2.5, PM10, O3, CO, SO2, NO2, CO2) focus given to AI ML predicting chronic airway diseases prediction climate changes heat waves. hybrid model has better performance than single models it greater accuracy warning systems. evaluation error indexes like R2, RMSE, MAE MAPE were highlighted based models.

Язык: Английский

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Coal resources under carbon peak: Segmentation of massive laser point clouds for coal mining in underground dusty environments using integrated graph deep learning model

Energy, Год журнала: 2023, Номер 285, С. 128771 - 128771

Опубликована: Авг. 15, 2023

Язык: Английский

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Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte

Advanced Energy Materials, Год журнала: 2024, Номер 14(20)

Опубликована: Фев. 14, 2024

Abstract Machine learning (ML) exhibits substantial potential for predicting the properties of solid‐state electrolytes (SSEs). By integrating experimental or/and simulation data within ML frameworks, discovery and development advanced SSEs can be accelerated, ultimately facilitating their application in high‐end energy storage systems. This review commences with an introduction to background SSEs, including explicit definition, comprehensive classification, intrinsic physical/chemical properties, underlying mechanisms governing conductivity, challenges, future developments. An in‐depth explanation methodology is also elucidated. Subsequently, key factors that influence performance are summarized, thermal expansion, modulus, diffusivity, ionic reaction energy, migration barrier, band gap, activation energy. Finally, it offered perspectives on design prerequisites upcoming generations focusing real‐time property prediction, multi‐property optimization, multiscale modeling, transfer learning, automation high‐throughput experimentation, synergistic optimization full battery, all which crucial accelerating progress SSEs. aims guide novel SSE materials practical realization efficient reliable technologies.

Язык: Английский

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Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions

Environmental Pollution, Год журнала: 2023, Номер 331, С. 121832 - 121832

Опубликована: Май 18, 2023

There is a growing need to apply geospatial artificial intelligence analysis disparate environmental datasets find solutions that benefit frontline communities. One such critically needed solution the prediction of health-relevant ambient ground-level air pollution concentrations. However, many challenges exist surrounding size and representativeness limited ground reference stations for model development, reconciling multi-source data, interpretability deep learning models. This research addresses these by leveraging strategically deployed, extensive low-cost sensor (LCS) network was rigorously calibrated through an optimized neural network. A set raster predictors with varying data quality spatial scales retrieved processed, including gap-filled satellite aerosol optical depth products airborne LiDAR-derived 3D urban form. We developed multi-scale, attention-enhanced convolutional reconcile LCS measurements estimating daily PM2.5 concentration at 30-m resolution. employs advanced approach using geostatistical kriging method generate baseline pattern multi-scale residual identify both regional patterns localized events high-frequency feature retention. further used permutation tests quantify importance, which has rarely been done in DL applications science. Finally, we demonstrated one application investigating inequality issue across within various urbanization levels block group scale. Overall, this demonstrates potential AI provide actionable addressing critical issues.

Язык: Английский

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Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature engineering based on variation sliding layer

Water Research, Год журнала: 2023, Номер 246, С. 120676 - 120676

Опубликована: Сен. 28, 2023

Язык: Английский

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Deciphering carbon emissions in urban sewer networks: Bridging urban sewer networks with city-wide environmental dynamics

Water Research, Год журнала: 2024, Номер 256, С. 121576 - 121576

Опубликована: Апрель 6, 2024

Язык: Английский

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Active Learning-Based Guided Synthesis of Engineered Biochar for CO₂ Capture

Environmental Science & Technology, Год журнала: 2024, Номер 58(15), С. 6628 - 6636

Опубликована: Март 18, 2024

Biomass waste-derived engineered biochar for CO2 capture presents a viable route climate change mitigation and sustainable waste management. However, optimally synthesizing them enhanced performance is time- labor-intensive. To address these issues, we devise an active learning strategy to guide expedite their synthesis with improved adsorption capacities. Our framework learns from experimental data recommends optimal parameters, aiming maximize the narrow micropore volume of biochar, which exhibits linear correlation its capacity. We experimentally validate predictions, are iteratively leveraged subsequent model training revalidation, thereby establishing closed loop. Over three cycles, synthesized 16 property-specific samples such that uptake nearly doubled by final round. demonstrate data-driven workflow accelerate development high-performance broader applications as functional material.

Язык: Английский

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Enhanced piezo-catalytic performance of BaTiO3 nanorods combining highly exposed active crystalline facets and superior deformation capability: Water purification and activation mechanism

Chemical Engineering Journal, Год журнала: 2024, Номер 488, С. 150768 - 150768

Опубликована: Март 27, 2024

Язык: Английский

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Photocatalytic degradation of drugs and dyes using a maching learning approach

RSC Advances, Год журнала: 2024, Номер 14(13), С. 9003 - 9019

Опубликована: Янв. 1, 2024

The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior organic pollutants during catalytic degradation.

Язык: Английский

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