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Environmental Monitoring

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Ensemble intelligence prediction algorithms and land use scenarios to measure carbon emissions of the Yangtze River Delta: A machine learning model based on Long Short-Term Memory.

PloS one
Land use in urban agglomerations is the main source of carbon emissions, and reducing them and improving land use efficiency are the keys to achieving sustainable development goals (SDGs). To advance the literature on densely populated cities and hig...

Natural factor-based spatial prediction and source apportionment of typical heavy metals in Chinese surface soil: Application of machine learning models.

Environmental pollution (Barking, Essex : 1987)
Predicting the natural distribution of heavy metals (HMs) in soil is important to understand the potential risk of pollution. However, suitable technologies are still lacking for wide scale due to the large spatial heterogeneity. In this study, we de...

Unsupervised learning for lake underwater vegetation classification: Constructing high-precision, large-scale aquatic ecological datasets.

The Science of the total environment
Monitoring underwater vegetation is vital for evaluating lake ecosystem health. Automated data collection and analysis play key roles in achieving large-scale, high-precision, and high-frequency monitoring. While technologies such as unmanned vessels...

Effective carbon footprint assessment strategy in fly ash geopolymer concrete based on adaptive boosting learning techniques.

Environmental research
In light of the growing need to mitigate climate change impacts, this study presents an innovative methodology combining ensemble machine learning with experimental data to accurately predict the carbon dioxide footprint (CO-FP) of fly ash geopolymer...

Selectively Quantify Toxic Pollutants in Water by Machine Learning Empowered Electrochemical Biosensors.

Environmental science & technology
Electroactive biofilm (EAB) sensors have become pivotal in water quality detection and early ecological risk warnings due to their remarkable sensitivity. However, it is challenging to identify multiple toxicants in complex water bodies concurrently....

Dongting Lake algal bloom forecasting: Robustness and accuracy analysis of deep learning models.

Journal of hazardous materials
Harmful algal blooms (HABs) pose a significant threat to aquatic ecosystems, prompting efforts to predict their occurrence for swift action by water management agencies. Despite the potential for high-precision forecasting through machine learning, t...

Comparing statistical and deep learning approaches for simultaneous prediction of stand-level above- and belowground biomass in tropical forests.

The Science of the total environment
Accurate and cost-effective prediction of aboveground biomass (AGB), belowground biomass (BGB), and the total (ABGB) at stand-level within tropical forests is crucial for effective forest ecological management and the provision of forest ecosystem se...

Modeling and predicting caffeine contamination in surface waters using artificial intelligence and standard statistical methods.

Environmental monitoring and assessment
Caffeine, considered an emerging contaminant, serves as an indicator of anthropic influence on water resources. This research employs various modeling techniques, including Artificial Neural Networks (ANN), Random Forest (RF), and more, along with hy...

Assessment of urban flood susceptibility based on a novel integrated machine learning method.

Environmental monitoring and assessment
Flood susceptibility assessment is the premise and foundation to prevent flood disaster events effectively. To accurately assess urban flood susceptibility (UFS), this study first analyzes the advantages and disadvantages of multi-layer perceptron (M...

Integrating Sentinel-1 data and machine learning for effective paddy field monitoring in Cauvery Delta Zone, Tamil Nadu, India.

Environmental monitoring and assessment
Paddy crop mapping is essential for agricultural monitoring, ensuring food security, and enhancing resource allocation. This study observes the Cauvery Delta Zone (CDZ), recognized as the rice bowl of Tamil Nadu and a crucial area for paddy farming i...