AIMC Topic: Chlorophyll A
Clear Filters Showing 31 to 40 of 43 articles
Modelling of ecological status of Polish lakes using deep learning techniques.
Environmental science and pollution research international
Sep 22, 2020
Since 2000, after the Water Framework Directive came into force, aquatic ecosystems' bioassessment has acquired immense practical importance for water management. Currently, due to extensive scientific research and monitoring, we have gathered compre...
Responses to the letter on "Transfer learning for neural network model in Chlorophyll-a dynamics prediction".
Environmental science and pollution research international
Jul 4, 2020
A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning.
Sensors (Basel, Switzerland)
Apr 9, 2020
Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this paper presents a methodology to estimate this informat...
Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes.
Environmental science and pollution research international
Sep 3, 2019
Chlorophyll-a (CHLA) is a key indicator to represent eutrophication status in lakes. In this study, CHLA, total phosphorus (TP), total nitrogen (TN), turbidity (TB), and Secchi depth (SD) collected by the United States Environmental Protection Agency...
Transfer learning for neural network model in chlorophyll-a dynamics prediction.
Environmental science and pollution research international
Aug 13, 2019
Neural network models have been used to predict chlorophyll-a concentration dynamics. However, as model generalization ability decreases, (i) the performance of the models gradually decreases over time; (ii) the accuracy and performance of the models...
Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll , Diatoms, Green Algae and Turbidity.
International journal of environmental research and public health
Aug 30, 2018
Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters ar...
Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations.
Computational intelligence and neuroscience
Dec 27, 2015
A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived "ocean color" (OC) data are used in this study becau...
Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea.
The Science of the total environment
Sep 19, 2014
Chlorophyll-a (Chl-a) is a direct indicator used to evaluate the ecological state of a waterbody, such as algal blooms that degrade the water quality in lakes, reservoirs and estuaries. In this study, artificial neural network (ANN) and support vecto...
A novel hybrid machine learning approach for accurate retrieval of ocean surface chlorophyll-a across oligotrophic to eutrophic waters.
Environmental research
Aug 15, 2025
Accurate assessment of chlorophyll a (Chla) concentration distribution and variations is significant for environmental monitoring and ecological research. However, the inversion of Chla in different optical types of water bodies can only be achieved ...
Improved prediction of chlorophyll-a concentrations using advancing graph neural network variants.
The Science of the total environment
Jun 1, 2025
Accurate estimation of harmful algal blooms is essential for protecting surface water. Chlorophyll-a (Chl-a), commonly used as a proxy for estimating algal concentration, is influenced by a broad range of weather and physicochemical factors that oper...
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823