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Papers
61,005 resultsShowing papers similar to Prediction of Oncomelania hupensis distribution in association with climate change using machine learning models
ClearGenomic Prediction of (Mal)Adaptation Across Current and Future Climatic Landscapes
This study developed genomic prediction models to forecast how organisms adapted to current climate conditions might (mal)adapt as climate change shifts selective pressures, offering a tool for conservation planning under future climate scenarios.
Predicting Potential Habitat of Aconitumcarmichaeli Debeaux in China Based onThree Species Distribution Models
Researchers applied three species distribution models (MaxEnt, GARP, and Bioclim) using 14 environmental variables and 449 specimen records to predict suitable habitat for the medicinal plant Aconitum carmichaelii Debeaux across China. All three models achieved AUC values above 0.85, identifying the highest-quality habitats in Sichuan, western Hubei, southern Shaanxi, and northern Guizhou provinces, with key climatic drivers identified through Jackknife analysis.
Species Distribution Model (SDM) Predicts the Spread of Invasive Nile Tilapia in the Sensitive Inland Water System of the Southeastern Arabian Peninsula Under Climate Change
Not relevant to microplastics — this study uses species distribution models with CMIP6 climate projections to predict the potential spread of invasive Nile tilapia in the freshwater systems of the southeastern Arabian Peninsula.
Suitable Area of Invasive Species Alexandriumunder Climate Change Scenariosin China Sea Areas
This study modeled how climate change will shift the habitat range of invasive Alexandrium algae in Chinese seas, providing guidance for early warning and prevention of harmful algal blooms that threaten coastal ecosystems.
Application of machine learning in assessing spatial distribution patterns of soil microplastics: a case study of the Bang Pakong Watershed, Thailand
Machine learning models were applied to predict spatial distribution patterns of microplastics in soils across a Thai watershed, identifying land use types and proximity to water bodies as key factors driving contamination levels.
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.
Machine learning models for forecasting microplastic dynamics in China’s coastal waters
Researchers used machine learning to analyze microplastic pollution patterns across China's four major coastal seas, drawing on over 1,100 data points from peer-reviewed studies. They found that urban centers and industrial activities are key drivers of contamination, with pollution levels varying significantly between marine, coastal, and estuary environments. The models project that economic development and education could reduce microplastic concentrations, while industrial expansion may increase them.
Using species distribution modelling to identify ‘coldspots’ for conservation of freshwater fishes under a changing climate
Species distribution modeling was used to project future habitat suitability for freshwater fish in southwestern Australia under climate change scenarios, finding that increased temperatures and drought would reduce suitable habitat for several native species. The study evaluates whether existing freshwater reserves will remain effective for conservation as climate conditions shift.
Using machine learning to reveal drivers of soil microplastics and assess their stock: A national-scale study
Using machine learning on data from 621 sites across China, researchers identified nine key factors driving microplastic distribution in soil, including population density, plastic production, and agricultural practices. The study estimated that Chinese topsoil contains a substantial stock of microplastics, with concentrations varying widely by region. This large-scale analysis helps predict where microplastic contamination is worst, which is important for understanding human exposure through food grown in contaminated soil.
Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction
Researchers used machine learning to predict microplastic levels in rivers across seven coastal regions of China, identifying population density, urbanization, and industrial activity as the strongest predictors of contamination. The models successfully captured how microplastics accumulate and move through river systems using 19 different environmental and human factors. This approach could reduce the need for costly field sampling while helping target pollution management efforts where they are needed most.
Microplastics migration mechanisms in high-erosion watersheds under climate warming
Scientists built a machine-learning model using 15 years of sediment data from three different-use watersheds on China's Qinghai-Tibet Plateau — grassland, cropland, and urban — to track how microplastics migrate and where they end up under changing climate conditions. The model achieved very high accuracy in tracing plastic sources and pathways, and found that wind direction and surface runoff are key drivers of transport, with cropland as a major source. The approach offers a practical tool for managing microplastic pollution in remote, high-altitude watersheds where warming is accelerating erosion.
An Analytical Review of Environmental and Machine Learning Approaches in Dengue Prediction
Despite its classification in this database, this systematic review examines machine learning approaches for dengue prediction using environmental factors — not microplastic research. Random Forest and Support Vector Machines outperformed traditional methods in identifying dengue risk areas, with temperature, humidity, and rainfall identified as key predictive variables.
Machine Learning Approaches for Microplastic Pollution Analysis in Mytilus galloprovincialis in the Western Black Sea
Machine learning models were applied to microplastic data from Mediterranean mussels (Mytilus galloprovincialis) in the western Black Sea, successfully predicting MP contamination levels and identifying pollution hotspots relevant to seafood safety and fisheries management.
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.
Identification of potentially contaminated areas of soil microplastic based on machine learning: A case study in Taihu Lake region, China
Researchers applied machine learning models — including random forest and support vector regression — to predict the spatial distribution of soil microplastic pollution in China's Taihu Lake region, finding that soil texture, population density, and proximity to known plastic sources were the dominant drivers, with nearly half of urban soils showing serious contamination.
Assessment of machine learning-based methods predictive suitability for migration pollutants from microplastics degradation
Researchers assessed the usefulness of machine learning methods for predicting the migration of chemical pollutants from microplastics. The study found that artificial neural networks and support vector methods showed strong potential for modeling and predicting the leaching of plasticizers and other contaminants, which could reduce the need for extensive laboratory analyses.
Machine Learning-Driven Prediction of Organic Compound Adsorption onto Microplastics in Freshwater
Seven machine learning algorithms were trained on 173 published measurements to predict how strongly organic contaminants adsorb onto different types of microplastics in freshwater. Accurate adsorption predictions are essential for assessing environmental risk, because microplastics that strongly bind pollutants become vectors that concentrate and transport toxic chemicals through aquatic food webs.
Evaluation of plateau wetland ecological security and its influencing factors in multi-climatic zones: A case study of Yunnan Province
Not a microplastics paper — this study assesses the ecological security of plateau wetlands across Yunnan Province, China using a pressure-state-response model based on remote sensing data, identifying climate and human activity as key threats to these fragile ecosystems.
From mapping to modelling: the evolving multidimensional microplastic risks in China's farmlands
Researchers combined a national-scale soil survey with machine learning models to map and project microplastic risks across China's farmlands through 2050, finding that agricultural film use, population density, and GDP are key drivers, and that regional risk rankings will shift counter-intuitively depending on which socioeconomic development pathway is followed.
Machine Learning Prediction of Adsorption Behavior of Xenobiotics on Microplastics under Different Environmental Conditions
Researchers developed a machine learning model to predict how different xenobiotic chemicals adsorb onto microplastics under varying environmental conditions, providing a computational tool to assess microplastics as vectors for pollutant transport without requiring extensive laboratory experiments.
Drinking water potability prediction using machine learning approaches: a case study of Indian rivers
Researchers applied machine learning techniques to predict drinking water quality in Indian rivers based on key parameters like pH, dissolved oxygen, and bacterial counts. Their models achieved high accuracy in classifying water as potable or non-potable. The study demonstrates how data-driven approaches could help developing countries monitor water safety more efficiently, especially in regions where traditional testing infrastructure is limited.
Decoding the transport thresholds of emerging contaminants in watersheds using explainable machine learning
Researchers collected 517 water samples from the Huangshui River over four years and used an explainable machine learning framework with SHAP analysis to model how land use, landscape metrics, and climate variables drive the transport of microplastics, antibiotics, and heavy metals through the watershed.
Predicting large-scale spatial patterns of marine meiofauna: implications for environmental monitoring
Researchers used machine learning to model the spatial distribution of marine meiofauna — small bottom-dwelling invertebrates — across the Santos Basin continental margin in Brazil, identifying six distinct benthic zones and providing a baseline for future environmental monitoring programs.
Population genetics and pedigree geography of Trionychia japonica in the four mountains of Henan Province and the Taihang Mountains
Researchers investigated the population genetics and phylogeography of the Japanese planarian in mountain systems of Henan Province and the Taihang Mountains, using DNA analysis to inform conservation strategies for this freshwater species threatened by habitat loss and climate change.