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Papers
61,005 resultsShowing papers similar to Maximum Entropy Method for Wind Farm Site Selection: Implications for River Basin Ecosystems Under Climate Change
ClearUsing 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.
Enhanced Entropy-Fuzzy Integration Decision Support System for Risk Assessment and Management of Hydraulic Engineering
Researchers developed an Enhanced Entropy-Fuzzy Integration Decision Support System for risk assessment and management of hydraulic engineering projects in the context of climate change and increasingly complex water resource management. The system addresses limitations of traditional probability-based risk methods by incorporating fuzzy logic to handle ambiguous and uncertain risks.
Identification and Prediction of Crop Waterlogging Risk Areas under the Impact of Climate Change
Researchers developed a crop waterlogging risk identification model to predict areas vulnerable to agricultural flooding under climate change scenarios, aiming to support disaster prevention planning in affected farming regions.
Can Offshore Wind Power Nigeria?
This study evaluates the potential for offshore wind power generation in Nigeria using climate model data, finding sufficient wind resources to support large-scale renewable energy development.
What Determines the Future Ecological Risks of Wastewater Discharges in River Networks: Load, Location or Climate Change?
Researchers developed a systematic framework for assessing future ecological risks from wastewater treatment plant (WWTP) effluents in river networks by combining plant size class as a proxy for pollutant load with stream order as a proxy for discharge location, applying it under climate change scenarios to show that streamflow reduction in receiving rivers will significantly worsen ecological risk even without increases in pollutant loads.
Comparative Analysis of Habitat Expansion Mechanisms for Four Invasive Amaranthaceae Plants Under Current and Future Climates Using MaxEnt
Despite its title referencing invasive plants and climate modeling, this paper uses MaxEnt modeling to predict how four invasive plant species from the Amaranthaceae family will expand their range across China under future climate scenarios — not microplastic pollution. It examines habitat suitability projections based on climate, soil, and topography and is not relevant to microplastics or human health.
An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework
Researchers developed an electromagnetic field optimization-based neural network to predict solar irradiance from environmental conditions, demonstrating improved accuracy over standard neural network approaches for solar energy management applications.
Genomic 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.
The concept, approach, and future research of hydrological connectivity and its assessment at multiscales
Researchers reviewed the concept of hydrological connectivity — the water-mediated transfer of matter and energy across landscapes — examining how dam construction, land management, and climate factors alter it, and identifying numerical modeling and connectivity indices as the most useful tools for its assessment across spatial scales.
Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria
Researchers compared four machine learning models for mapping soil erosion susceptibility in northern Algeria, finding that categorical boosting (CatBoost) outperformed other approaches in predicting erosion risk in Mediterranean agricultural landscapes.
The Road Map to Classify the Potential Risk of Wind Erosion
This study developed a methodological framework for classifying the risk of wind erosion across different soil and climate conditions. Wind erosion can transport soil particles as well as light microplastic particles across large distances, making erosion risk assessment relevant to understanding microplastic dispersal.
Assessment of Uncertainties in Ecological Risk Based on the Prediction of Land Use Change and Ecosystem Service Evolution
Using the PLUS land use change model, researchers simulated future land use scenarios in southern China and evaluated how projected changes would alter ecosystem services and associated ecological risk under uncertainty.
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.
A Multicriteria Decision Analysis Model for Optimal Land Uses: Guiding Farmers under the New European Union’s Common Agricultural Policy (2023–2027)
Researchers applied a multicriteria decision analysis model to optimize land use decisions for Greek farmer groups under the new EU Common Agricultural Policy, balancing water use, profit, labor, and cost objectives. The model demonstrated practical value for guiding sustainable irrigation decisions in both irrigated and dryland farming systems.
Analysis 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.
Hydrological modelling: Insights into hydrological signals and contaminant transport
Researchers modeled how future climate-driven changes in hydrological extremes — including floods and droughts — affect contaminant transport in a heavily polluted Scottish catchment, finding that traditional models calibrated on historical data perform poorly when projecting under novel climatic conditions.
Simulation of nutrient management and hydroclimatic effects on coastal water quality and ecological status—The Baltic Himmerfjärden Bay case
Researchers used computer modeling to simulate how different nutrient management scenarios and climate conditions would affect water quality and ecological status in the Baltic Sea's Himmerfjarden Bay. The study provides a tool for coastal managers to evaluate strategies for reducing eutrophication under future climate scenarios.
An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework
Researchers used an optimization algorithm to tune a neural network for predicting solar energy availability from environmental conditions. This renewable energy modeling paper is unrelated to microplastic research.
An Analytical Framework for Determining the Ecological Risks of Wastewater Discharges in River Networks Under Climate Change
Researchers developed an analytical framework to assess ecological risks from wastewater treatment plant discharges into river networks under climate change scenarios, finding that reduced river flows from climate change will amplify ecological risks from effluent contaminants including microplastics.
Potential pollution risks of historic landfills in England: Further analysis of climate change impacts
Researchers expanded upon earlier analysis of historic landfill pollution risks in England by examining how climate change could affect inland landfill sites, not just coastal ones. They found that increased flooding, drought, and shifting groundwater patterns could all accelerate pollutant release from thousands of unregulated legacy landfills. The study warns that many of these sites sit in groundwater protection zones where modern regulations would never allow their construction.
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.
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.
Unleashing the Potential of a Hybrid 3D Hydrodynamic Monte Carlo Risk Model for Maritime Structures’ Design in the Imminent Climate Change Era
Not relevant to microplastics — this is a marine engineering study developing a hybrid hydrodynamic Monte Carlo risk model for designing submarine pipelines under climate change uncertainty.
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.