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Papers
20 resultsShowing papers similar to Analysis of Potential Supply of Ecosystem Services in Forest Remnants through Neural Networks
ClearDevelopment of ecological management system for planted forest based on ELM deep learning algorithm
Researchers developed an ecological management system for planted forests using a combination of extreme learning machine (ELM) and deep learning algorithms on a J2EE platform. The system evaluates ecological function values through principal component analysis and demand prediction modules, with results showing that plant density significantly affects biomass, organic carbon storage, water content, and nutrient accumulation.
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.
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.
Applying Artificial Neural Networks to Oxidative Stress Biomarkers in Forager Honey Bees (Apis mellifera) for Ecological Assessment
Researchers applied artificial neural networks to analyze oxidative stress biomarkers in forager honey bees across urban, forested, and agricultural areas in Italy, identifying environmental matrix-specific patterns useful for ecological health assessment.
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.
Ecological Zoning Based on Value–Risk in the Wuling Mountains Area of Hunan Province
Researchers assessed ecological zoning in China's Wuling Mountains region based on ecosystem service value and ecological risk from 2000 to 2020. They found that both overall ecosystem value and ecological risk increased over the study period, with forests providing over 77% of the total ecosystem service value. The study provides a framework for ecological planning that accounts for environmental risks, including those from pollution and land use changes.
Heavy metal concentrations in the soil near illegal landfills in the vicinity of agricultural areas—artificial neural network approach
Researchers used artificial neural network models to predict heavy metal contamination in soils near illegal landfills close to agricultural areas. The study found that illegal landfilling significantly impacts surrounding soil quality and proposes these predictive models as effective tools for environmental risk management and decision-making.
Coastal Wetland Restoration Strategies Based on Ecosystem Service Changes: A Case Study of the South Bank of Hangzhou Bay
Researchers analyzed coastal wetland restoration strategies based on ecosystem service changes along Hangzhou Bay's south bank, identifying priority restoration areas where interventions would maximize ecological benefits including pollution filtration and biodiversity support.
Predicting microplastic accumulation zones and shoreline changes along the Kelantan coast, Malaysia, using integrated GIS and ANN models
Researchers combined GIS with an artificial neural network to predict microplastic accumulation zones along Malaysia's Kelantan coast, achieving R=0.972 predictive accuracy and identifying shoreline erosion-prone areas as the primary deposition hotspots for microplastic pollution.
An Optimization Analysis Model of Tourism Specialized Villages Based on Neural Network and System Dynamics
A tourism optimization model for rural villages was developed combining neural network forecasting with system dynamics simulation to better predict visitor demand and guide resource allocation. The model improved prediction accuracy over traditional methods. Better planning tools can help tourism villages manage visitor flow sustainably without exceeding environmental carrying capacity.
Assessing natural capital value in the network of Italian marine protected areas: a comparative approach
This study assessed the natural capital value of a network of Italian Marine Protected Areas using biophysical and economic approaches. It is a marine ecology and ecosystem services study rather than a microplastics research article.
Detection of Vegetation Spectral Signatures in Hyperspectral Images using Artificial Neural Networks
This study developed a computer program that can identify plants and vegetation in detailed satellite images by analyzing how they reflect different colors of light. The technology successfully detected about 42% of an area as vegetation in a test neighborhood, which was more accurate than older methods. This could help scientists better monitor environmental changes like deforestation or urban green spaces that affect air quality and human health.
Servicios ecosistémicos en áreas de montaña: beneficios y amenazas
This review examines the ecosystem services provided by mountain areas, including water supply, biodiversity, climate regulation, and cultural heritage, while identifying growing threats from climate change, land use intensification, and pollution. The authors argue that mountain ecosystem services are critically undervalued in national and global sustainability frameworks.
Importance of Mangroves to Human Well-being
This review synthesizes the role of mangrove ecosystems in supporting human well-being through provisioning, regulating, cultural, and supporting ecosystem services including climate regulation, food security, and poverty reduction, recommending science-based protection and restoration strategies to sustain these critical coastal habitats.
VIRS based detection in combination with machine learning for mapping soil pollution
Researchers reviewed visible and infrared reflectance spectroscopy combined with machine learning — including neural networks and random forest algorithms — as a low-cost, remote method for detecting and mapping soil contamination across proximal, airborne, and satellite sensor platforms.
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.
Mapping the plastic legacy: Geospatial predictions of a microplastic inventory in a complex estuarine system using machine learning
Researchers applied machine learning techniques to develop geospatial predictions of microplastic inventory in a complex estuarine system, overcoming the limitations of coarse ocean basin models by accounting for the intricate geomorphological and hydrodynamic conditions that govern sediment-associated microplastic distribution.
Regulatory Ecosystem Services of Forest Sacred Groves in comparison to Invasive vegetation in the urban and urban peripheries of a semi-arid region
This Indian ecology study compared carbon sequestration, soil erosion, and floral diversity between traditionally protected sacred forest groves and invasive Prosopis juliflora stands in a semi-arid region. Sacred groves supported higher biodiversity and comparable carbon sequestration, supporting their conservation as urban ecosystem service providers.
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.
Machine learning may accelerate the recognition and control of microplastic pollution: Future prospects
This review examines how machine learning techniques including neural networks and random forests are being applied to microplastic detection, classification, and ecological risk assessment, demonstrating faster and more accurate results than traditional analytical methods. The authors identify data standardization and model interpretability as key challenges for broader adoption.