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Eutrophication

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Artificial intelligence to explain the variables that favor the cyanobacteria steady-state in tropical ecosystems: A Bayeasian network approach.

Anais da Academia Brasileira de Ciencias
The steady-state is a situation of little variability of species dominance and total biomass over time. Maintenance of cyanobacteria are often observed in tropical and eutrophic ecosystems and can cause imbalances in aquatic ecosystem. Bayeasian netw...

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...

Quantification of chlorophyll-a in typical lakes across China using Sentinel-2 MSI imagery with machine learning algorithm.

The Science of the total environment
Lake eutrophication has attracted the attention of the government and general public. Chlorophyll-a (Chl-a) is a key indicator of algal biomass and eutrophication. Many efforts have been devoted to establishing accurate algorithms for estimating Chl-...

Improvement of DBR routing protocol in underwater wireless sensor networks using fuzzy logic and bloom filter.

PloS one
Routing protocols for underwater wireless sensor networks (UWSN) and underwater Internet of Things (IoT_UWSN) networks have expanded significantly. DBR routing protocol is one of the most critical routing protocols in UWSNs. In this routing protocol,...

Early warning of algal blooms based on the optimization support vector machine regression in a typical tributary bay of the Three Gorges Reservoir, China.

Environmental geochemistry and health
Algal blooms caused by climate change and human activities have received considerable attention in recent years. Since chlorophyll a (Chl-a) can be used as an indicator of phytoplankton biomass, it has been selected as a direct indicator for monitori...

Algal bloom forecasting with time-frequency analysis: A hybrid deep learning approach.

Water research
The rapid emergence of deep learning long-short-term-memory (LSTM) technique presents a promising solution to algal bloom forecasting. However, the discontinuous and non-stationary processes within algal dynamics still largely limit the functions of ...

Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method.

Sensors (Basel, Switzerland)
Frequent outbreaks of cyanobacterial blooms have become one of the most challenging water ecosystem issues and a critical concern in environmental protection. To overcome the poor stability of traditional detection algorithms, this paper proposes a m...

Automated Secchi disk depth measurement based on artificial intelligence object recognition.

Marine pollution bulletin
Water transparency affects the degree of sunlight penetration in water, which is important to many water quality processes. It can be visually measured by lowering a Secchi disk (SD) into water and recording its disappearance depth - the Secchi disk ...

Prediction and sensitivity analysis of chlorophyll a based on a support vector machine regression algorithm.

Environmental monitoring and assessment
Outbreaks of planktonic algae seriously affect the water quality of rivers and are difficult to control. Based on the analysis of the temporal and spatial variation characteristics of environmental factors, this study uses a support vector machine re...

An optical mechanism-based deep learning approach for deriving water trophic state of China's lakes from Landsat images.

Water research
Widespread eutrophication has been considered as the most serious environment problems in the world. Given the critical roles of lakes in human society and serious negative effects of water eutrophication on lake ecosystems, it is thus fundamentally ...