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논문 기본 정보

자료유형
학술저널
저자정보
Siamak Boudaghpour (K.N.Toosi Technical University) Hajar Sadat Alizadeh Moghadam (K.N.Toosi Technical University) Mohammadreza Hajbabaie (K.N.Toosi Technical University) Seyed Hamidreza Toliati (University of Tehran)
저널정보
대한환경공학회 Environmental Engineering Research Environmental Engineering Research 제25권 제4호
발행연도
2020.8
수록면
515 - 521 (9page)

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초록· 키워드

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Nowadays, due to various pollution sources, it is essential for environmental scientists to monitor water quality. Phytoplanktons form the end of the food chain in water bodies and are one of the most important biological indicators in water pollution studies. Chlorophyll-A, a green pigment, is found in all phytoplankton. Chlorophyll-A concentration indicates phytoplankton biomass directly. Therefore, Chlorophyll-A is an indirect indicator of pollutants, including phosphorus and nitrogen, and their refinement and control are important. The present study, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were used to estimate the chlorophyll-A concentration in southern coastal waters in the Caspian Sea. For this purpose, Multi-layer perceptron neural networks (NNs) were applied which contained three and four feed-forward layers. The best three-layer NN has 15 neurons in its hidden layer and the best four-layer one has 5 in each. The three- and four- layer networks both resulted in similar root mean square errors (RMSE), 0.1(㎍/ℓ), however, the four-layer NNs proved superior in terms of R² and also required less training data. Accordingly, a four-layer feed-forward NN with 5 neurons in each hidden layer, is the best network structure for estimating Chlorophyll-A concentration in the southern coastal waters of the Caspian Sea.

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ABSTRACT
1. Introduction
2. Data
3. Method
4. Results and Discussion
5. Concluding Remarks
References

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