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Ecosystem

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Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection.

Sensors (Basel, Switzerland)
During the past decades, the composition and distribution of marine species have changed due to multiple anthropogenic pressures. Monitoring these changes in a cost-effective manner is of high relevance to assess the environmental status and evaluate...

A water quality prediction method based on the multi-time scale bidirectional long short-term memory network.

Environmental science and pollution research international
As an important factor affecting the mangrove wetland ecosystem, water quality has become the focus of attention in recent years. Therefore, many studies have focused on the prediction of water quality to help establish a regulatory framework for the...

Attractor Ranked Radial Basis Function Network: A Nonparametric Forecasting Approach for Chaotic Dynamic Systems.

Scientific reports
The curse of dimensionality has long been a hurdle in the analysis of complex data in areas such as computational biology, ecology and econometrics. In this work, we present a forecasting algorithm that exploits the dimensionality of data in a nonpar...

Explore the relationship between fish community and environmental factors by machine learning techniques.

Environmental research
In the face of multiple habitat alterations originating from both natural and anthropogenic factors, the fast-changing environments pose significant challenges for maintaining ecosystem integrity. Machine learning is a powerful tool for modeling comp...

Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain).

International journal of environmental research and public health
The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt ch...

Towards a global understanding of the drivers of marine and terrestrial biodiversity.

PloS one
Understanding the distribution of life's variety has driven naturalists and scientists for centuries, yet this has been constrained both by the available data and the models needed for their analysis. Here we compiled data for over 67,000 marine and ...

Cloud-computing and machine learning in support of country-level land cover and ecosystem extent mapping in Liberia and Gabon.

PloS one
Liberia and Gabon joined the Gaborone Declaration for Sustainability in Africa (GDSA), established in 2012, with the goal of incorporating the value of nature into national decision making by estimating the multiple services obtained from ecosystems ...

An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection.

Scientific reports
Bees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of...

A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta, China.

International journal of environmental research and public health
The Yangtze River Delta (YRD) is one of the most developed regions in China. This is also a flood-prone area where flood disasters are frequently experienced; the situations between the people-land nexus and the people-water nexus are very complicate...

Application of deep learning in aquatic bioassessment: Towards automated identification of non-biting midges.

The Science of the total environment
Morphological species identification is often a difficult, expensive, and time-consuming process which hinders the ability for reliable biomonitoring of aquatic ecosystems. An alternative approach is to automate the whole process, accelerating the id...