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Chlorophyll A

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Effects of nitrogen and phosphorus addition on growth and leaf nitrogen metabolism of alfalfa in alkaline soil in Yinchuan Plain of Hetao Basin.

PeerJ
Alkaline soil is widely distributed in China. Its rational utilization is an effective measure to solve land shortage and improve the environment. Alfalfa is characterized by strong salt and alkali tolerance and high yield and protein content. Nitrog...

Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models.

The Science of the total environment
Remote sensing techniques have been applied to monitor the spatiotemporal variation of harmful algal blooms (HABs) in many inland waters. However, these studies have been limited to monitor the vertical distribution of HABs due to the optical complex...

Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery.

Environmental pollution (Barking, Essex : 1987)
Airborne hyperspectral remote sensing has the characteristics of high spatial and spectral resolutions, and provides an opportunity for accurate and efficient inland water qauality monitoring. Many studies have focused on evaluating and quantifying t...

LSTM Networks to Improve the Prediction of Harmful Algal Blooms in the West Coast of Sabah.

International journal of environmental research and public health
Harmful algal bloom (HAB) events have alarmed authorities of human health that have caused severe illness and fatalities, death of marine organisms, and massive fish killings. This work aimed to perform the long short-term memory (LSTM) method and co...

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...

Interpretation of ensemble learning to predict water quality using explainable artificial intelligence.

The Science of the total environment
Algal bloom is a significant issue when managing water quality in freshwater; specifically, predicting the concentration of algae is essential to maintaining the safety of the drinking water supply system. The chlorophyll-a (Chl-a) concentration is a...

Classifiers based on artificial intelligence in the prediction of recently planted coffee cultivars using a Remotely Piloted Aircraft System.

Anais da Academia Brasileira de Ciencias
The classification and prediction methods through artificial intelligence algorithms are applied in different sectors to assist and promote intelligent decision-making. In this sense, due to the great importance in the cultivation, consumption and ex...

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...

Deep learning based soft-sensor for continuous chlorophyll estimation on decentralized data.

Water research
Monitoring the concentration of pigments like chlorophyll (Chl) in water-bodies is a key task to contribute to their conservation. However, with the existing sensor technology, measurement in real-time and with enough frequency to ensure proper risk ...

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
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 ...