Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks

Li, Xue; Sha, Jingeng; Wang, Zhongliang

doi:10.3390/w9070524

Cited by 47 publications

(32 citation statements)

References 57 publications

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“…The common methods for water quality prediction include Artificial Neural Networks (ANN), Regression Analyses (RA), Grey Systems (GS), and Support Vector Regressions (SVR). Li et al [1] applied the optimized back-propagation neural network to predict the concentration of chlorophyll in a lake. Grbić et al [2] proposed a method based on a Gaussian process regression to predict daily average water temperature.…”

Section: Introductionmentioning

confidence: 99%

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Water Quality Prediction Method Based on IGRA and LSTM

Zhou

Wang

Xiao

et al. 2018

Water

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Water quality prediction has great significance for water environment protection. A water quality prediction method based on the Improved Grey Relational Analysis (IGRA) algorithm and a Long-Short Term Memory (LSTM) neural network is proposed in this paper. Firstly, considering the multivariate correlation of water quality information, IGRA, in terms of similarity and proximity, is proposed to make feature selection for water quality information. Secondly, considering the time sequence of water quality information, the water quality prediction model based on LSTM, whose inputs are the features obtained by IGRA, is established. Finally, the proposed method is applied in two actual water quality datasets: Tai Lake and Victoria Bay. Experimental results demonstrate that the proposed method can take full advantage of the multivariate correlations and time sequence of water quality information to achieve better performance on water quality prediction compared with the single feature or non-sequential prediction methods.

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Section: Introductionmentioning

confidence: 99%

“…Candelieri et al [3] applied clustering and SVR in water demand forecasting and anomaly detection. Dai et al [4] established the Grey Model (1,1) with GS theory to predict major pollutants in a particular water environment.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Prediction Method Based on IGRA and LSTM

Zhou

Wang

Xiao

et al. 2018

Water

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“…By contrast, empirical-based methods address the link between spectral bands of satellite images and measured water parameters of interest [12,13,[18][19][20]. Recently, a neural network was also applied to define the various eutrophic levels and estimate the water quality parameters [21,22]. Statistical techniques are leveraged on empirical-based methods to relate water quality observations directly to remotely sensed spectral information [23].…”

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confidence: 99%

Spectral Feature Selection Optimization for Water Quality Estimation

Nguyen

Lin

Chu

et al. 2019

IJERPH

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The spatial heterogeneity and nonlinearity exhibited by bio-optical relationships in turbid inland waters complicate the retrieval of chlorophyll-a (Chl-a) concentration from multispectral satellite images. Most studies achieved satisfactory Chl-a estimation and focused solely on the spectral regions from near-infrared (NIR) to red spectral bands. However, the optical complexity of turbid waters may vary with locations and seasons, which renders the selection of spectral bands challenging. Accordingly, this study proposes an optimization process utilizing available spectral models to achieve optimal Chl-a retrieval. The method begins with the generation of a set of feature candidates, followed by candidate selection and optimization. Each candidate links to a Chl-a estimation model, including two-band, three-band, and normalized different chlorophyll index models. Moreover, a set of selected candidates using available spectral bands implies an optimal composition of estimation models, which results in an optimal Chl-a estimation. Remote sensing images and in situ Chl-a measurements in Lake Kasumigaura, Japan, are analyzed quantitatively and qualitatively to evaluate the proposed method. Results indicate that the model outperforms related Chl-a estimation models. The root-mean-squared errors of the Chl-a concentration obtained by the resulting model (OptiM-3) improve from 11.95 mg · m − 3 to 6.37 mg · m − 3 , and the Pearson’s correlation coefficients between the predicted and in situ Chl- a improve from 0.56 to 0.89.

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“…High levels of phosphates in waters will trigger dense plant growth which can result in the eutrophication of the water (Perlman, n.d.). chl-a, a type of chlorophyll present in algae, is used as an indicator of eutrophication (Li, Sha & Wang, 2017). Eutrophication may lead to deteriorating water quality, dissolved oxygen depletion, and toxic phytoplankton bloom, which produces toxins that can destroy aquatic life in affected areas (Smith, Tilman, & Nekola, 1999).…”

Section: Background Of the Studymentioning

confidence: 99%

Estimation of Chlorophyll-a Concentration in Laguna De Bay Using Sentinel-3 Satellite Data

Conopio

Japor

Blanco

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The mission of the European Space Agency’s Sentinel-3 satellite is to provide data for land and ocean monitoring purposes. Sentinel- 3’s Ocean and Land Colour Instrument (OLCI) data are being used largely for monitoring offshore and coastal waters but can also be used for inland waters including lakes. It has a spatial resolution of 300 meters, a temporal resolution of 2–3 days and contains 21 spectral bands. Laguna de Bay, with a surface area of around 930 km2, suffers from periodic algal blooms resulting from excessive nutrient inputs from surrounding watersheds. This study aims to assess the applicability of Sentinel-3 OLCI for estimating chlorophylla (chl-a) concentration in Laguna Lake. Several chl-a estimation algorithms (i.e., band ratios and indices) were tested for Sentinel 3 OLCI images and compared with in-situ data obtained using a chl-a sensor. A regression model comprising of individual spectral bands, band ratios, and band indices for chl-a estimation as independent variables was developed, yielding an adjusted R2 of 0.759 and RMSE of 1.19 ug/L. The model consists of R620, R674/R708, and RED/NIR. A map was produced showing the spatial distribution of chl-a in Laguna Lake, with most of the portion of the lake having a concentration ranging from 7.5 ug/L to 15 ug/L. This shows that Sentinel 3 OLCI images can be utilized for accurately estimating chl-a in Laguna Lake.

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Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks

Cited by 47 publications

References 57 publications

Water Quality Prediction Method Based on IGRA and LSTM

Water Quality Prediction Method Based on IGRA and LSTM

Spectral Feature Selection Optimization for Water Quality Estimation

Estimation of Chlorophyll-a Concentration in Laguna De Bay Using Sentinel-3 Satellite Data

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