AIMC Topic: Chlorophyll A
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Multiple remotely sensed datasets and machine learning models to predict chlorophyll-a concentration in the Nakdong River, South Korea.
Environmental science and pollution research international
Sep 24, 2024
The Nakdong River is a crucial water resource in South Korea, supplying water for various purposes such as potable water, irrigation, and recreation. However, the river is vulnerable to algal blooms due to the inflow of pollutants from multiple point...
Comparison of conventional and machine learning regression models for accurate prediction of selected optical active components - A case study: The Gulf of Izmit.
Marine pollution bulletin
Sep 14, 2024
This study hypothesizes that advanced machine learning (ML) models can more accurately predict certain critical water quality parameters in marine environments compared to conventional regression techniques. We specifically evaluated the spatio-tempo...
Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies.
PloS one
Aug 26, 2024
Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral refl...
Real-time chlorophyll-a forecasting using machine learning framework with dimension reduction and hyperspectral data.
Environmental research
Aug 22, 2024
Since water is an essential resource in various fields, it requires constant monitoring. Chlorophyll-a concentration is a crucial indicator of water quality and can be used to monitor water quality. In this study, we developed methods to forecast chl...
Long-term trend forecast of chlorophyll-a concentration over eutrophic lakes based on time series decomposition and deep learning algorithm.
The Science of the total environment
Aug 10, 2024
Long-term trend forecast of chlorophyll-a concentration (Chla) holds significant implications for eutrophication management and pollution control planning on lakes, especially under the background of climate change. However, it is a challenging task ...
Comparative analysis of machine learning methods for prediction of chlorophyll-a in a river with different hydrology characteristics: A case study in Fuchun River, China.
Journal of environmental management
Jun 11, 2024
Eutrophication is a serious threat to water quality and human health, and chlorophyll-a (Chla) is a key indicator to represent eutrophication in rivers or lakes. Understanding the spatial-temporal distribution of Chla and its accurate prediction are ...
Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning.
Marine environmental research
Jun 3, 2024
Oceanic dissolved oxygen (DO) is crucial for oceanic material cycles and marine biological activities. However, obtaining subsurface DO values directly from satellite observations is limited due to the restricted observed depth. Therefore, it is esse...
Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features.
Journal of environmental management
Apr 9, 2024
Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administra...
Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach.
The Science of the total environment
Dec 23, 2023
Recent advances in remote sensing techniques provide a new horizon for monitoring the spatiotemporal variations of harmful algal blooms (HABs) using hyperspectral data in inland water. In this study, a hierarchical concatenated variational autoencode...
A novel hybrid model based on two-stage data processing and machine learning for forecasting chlorophyll-a concentration in reservoirs.
Environmental science and pollution research international
Nov 28, 2023
The accurate and efficient prediction of chlorophyll-a (Chl-a) concentration is crucial for the early detection of algal blooms in reservoirs. Nevertheless, predicting Chl-a concentration in multivariate time series poses a significant challenge due ...
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