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Papers
61,005 resultsShowing papers similar to Development of ecological management system for planted forest based on ELM deep learning algorithm
ClearAnalysis of Potential Supply of Ecosystem Services in Forest Remnants through Neural Networks
Researchers applied an artificial neural network to geospatial indicators to assess the potential supply of regulating ecosystem services from forest remnants in Campinas, Brazil. The study analyzed landscape configuration factors and evaluated how both the supply of and societal demand for ecosystem services influence the actual benefits provided by fragmented forest patches.
Machine learning based approaches for prompt diagnosis of aquatic plant ailments
Researchers applied Deep Belief Networks and Isolation Forests to diagnose aquatic plant diseases from observational data, achieving an average DBN accuracy of 86% and an Isolation Forest true positive rate of 91%. The study demonstrated that machine learning can improve early detection of aquatic plant ailments, which is relevant to monitoring aquatic ecosystem health in microplastic-contaminated environments.
Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models
Researchers applied ensemble machine learning techniques to model drought vulnerability across the state of Odisha, India, using 53 vulnerability indicators across 248 drought-prone villages. The M5P-Rotation Forest model achieved the highest accuracy with an AUC of 0.901, and the analysis found that nearly 38% of the study area showed high to very high drought vulnerability. The study demonstrates the potential of ensemble machine learning approaches for mapping regional drought risk to support better resource management.
A WebGIS-Based System for Supporting Saline–Alkali Soil Ecological Monitoring: A Case Study in Yellow River Delta, China
Researchers developed a web-based geographic information system for monitoring and predicting soil ecological conditions in the Yellow River Delta region of China, an area affected by saline-alkali soils. The system uses machine learning models to assess soil health indicators and provides online visualization and prediction tools. This platform could help land managers make more informed decisions about agricultural practices and environmental risk reduction in vulnerable soil ecosystems.
Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model
Researchers used Landsat satellite imagery and machine learning to estimate forest carbon storage in a region of China over two decades. The study demonstrates remote sensing as a practical tool for tracking carbon stocks and the effects of land-use change.
High-Precision Mapping of Soil Organic Matter Based on UAV Imagery Using Machine Learning Algorithms
UAV-based multispectral imaging combined with random forest machine learning achieved high-precision soil organic matter mapping with R2 of 0.91, outperforming SVM, elastic net, and other algorithms, with results showing a negative correlation between SOM content and elevation.
Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review
This review discusses the application of artificial intelligence as a predictive modelling tool for analysing environmental pollution impacts on mangrove ecosystems, examining how AI can support law enforcement and conservation decision-making in the face of data scarcity and complex pollution interactions.
Water Quality Monitoring And Ground Water Level Prediction Using Machine Learning
Researchers applied machine learning techniques to water quality monitoring and groundwater level prediction, demonstrating the potential of data-driven approaches for environmental sensing and resource management.
Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine
Researchers developed a hybrid machine learning framework to predict landfill leachate and groundwater quality, providing a robust monitoring tool to assess contamination risk to water resources near landfill sites.
Machine learning approaches for predicting microplastic pollution in peatland areas
Researchers used machine learning models to predict microplastic quantities in peatland sediments in Vietnam from easily measurable environmental parameters. The study found that pH, total organic carbon, and salinity were the most influential factors, and that Least-Square Support Vector Machines and Random Forest models could effectively predict microplastic contamination levels.
Soil Organic Carbon Estimation via Remote Sensing and Machine Learning Techniques: Global Topic Modeling and Research Trend Exploration
Researchers used advanced topic modeling and bibliometric analysis to map global research trends in estimating soil organic carbon using remote sensing and machine learning. They identified key research clusters including satellite imagery analysis, deep learning methods, and regional carbon mapping efforts. The study provides a roadmap for future research priorities in monitoring soil carbon stocks, which is critical for understanding climate change.
Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen
Researchers developed and compared neural network models for predicting dissolved oxygen concentrations in water using machine learning metaheuristic algorithms. Dissolved oxygen is a key indicator of aquatic ecosystem health, and accurate prediction tools support monitoring of water bodies affected by plastic and other pollutants.
Comparing the Performance of Machine Learning and Deep Learning Algorithms in Wastewater Treatment Process
This study compared machine learning and deep learning algorithms for predicting wastewater treatment plant performance, finding that modified ensemble and stacked models performed best. Machine learning approaches for optimizing wastewater treatment could improve the removal of microplastics alongside conventional pollutants.
New Graph-Based and Transformers Deep Learning Models for River Dissolved Oxygen Forecasting
Researchers developed new graph-based and transformer deep learning models to forecast dissolved oxygen levels in the Credit River Watershed, outperforming earlier approaches. Accurate dissolved oxygen prediction is important for detecting eutrophication and assessing water quality impacts from pollution.
Spatial prediction of physical and chemical properties of soil using optical satellite imagery: a state-of-the-art hybridization of deep learning algorithm
Not relevant to microplastics — this study uses deep learning models combining satellite imagery and topographic data to predict soil chemical properties (pH, organic carbon, phosphorus, potassium) across a region of Iran, with no connection to microplastic pollution.
An Efficient Data Driven-Based Model for Prediction of the Total Sediment Load in Rivers
A data-driven machine learning model was developed to predict total sediment load in rivers using readily available hydrological and morphological variables, outperforming conventional empirical sediment transport equations in accuracy. The model provides a practical tool for river management applications where comprehensive physical measurements are unavailable.
Machine learning-assisted assessment of key meteorological and crop factors affecting historical mulch pollution in China
Researchers used machine learning models (Elastic Net and Random Forest) to assess how meteorological and crop factors influenced plastic mulch contamination levels across China from 1993 to 2012, estimating mulch-derived microplastics and phthalic acid esters during the rapid expansion period of mulch use. The study identified key drivers of mulch pollution to inform more targeted management strategies for agricultural soils.
Development of a Classification Model for Physiological Parameters in Relation to Ecological Aspects Based on Cohort Data
Researchers developed a classification model linking physiological parameters to ecological and environmental factors using cohort data, aiming to understand how environmental variables, socioeconomic conditions, and demographic parameters influence human health outcomes in the context of ecosystem modelling.
Application of Machine learning techniques in environmental governance: A review
This paper is not relevant to microplastics research — it reviews the application of machine learning methods in environmental governance broadly, covering air and water quality monitoring and land use management.
Applicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors
Researchers applied machine learning models to predict how microplastics move through open water channels under different flow conditions, vegetation patterns, and particle densities. They found that tree-based algorithms like Random Forest and Extreme Gradient Boost significantly outperformed traditional statistical models in prediction accuracy. The study demonstrates that machine learning can be a valuable tool for understanding and forecasting microplastic transport in waterways.
Improving the Accuracy of Random Forest Classifier for Identifying Burned Areas in the Tangier-Tetouan-Al Hoceima Region Using Google Earth Engine
This study used remote sensing and machine learning to detect burned areas from forest fires in Morocco's Tangier-Tetouan-Al Hoceima region using Google Earth Engine. Researchers found that combining multiple spectral indices with a random forest classifier improved the accuracy of identifying fire-damaged areas compared to using individual indices alone.
Machine-Learning-Based Prediction of Plant Cuticle–Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective
Researchers developed machine-learning models using multiple linear regression, multi-layer perceptron, k-nearest neighbors, and gradient-boosting decision tree algorithms to predict plant cuticle-air partition coefficients for organic pollutants from molecular structure descriptors. They found that gradient-boosting models provided the best predictive accuracy, revealing key molecular structural features governing pollutant partitioning between plant surfaces and air.
The supporting role of Artificial Intelligence and Machine/Deep Learning in monitoring the marine environment: a bibliometric analysis
This review examines the supporting role of artificial intelligence and machine learning in monitoring and managing plastic pollution, covering applications in remote sensing, image-based plastic detection, and predictive modeling of plastic fate. The authors identify deep learning for image classification and satellite-based detection as the most rapidly advancing AI applications in plastic pollution science.
Tall Trees and Small Plastics. Using Random Forest Classification to Identify Microplastic Pollution in Surface Soil Samples
Researchers used machine learning (random forest classification) to identify and distinguish twenty types of plastic particles in soil samples from agricultural land. Developing accurate, automated detection methods for microplastics in soil is essential for large-scale environmental monitoring.