Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks

2020 ◽  
pp. 213-238
Author(s):  
Taina Hakala ◽  
Ilkka Pölönen ◽  
Eija Honkavaara ◽  
Roope Näsi ◽  
Teemu Hakala ◽  
...  
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Hyperspectral Imaging ◽  
Deep Neural Networks ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Imaging Data ◽  
Aerial Platforms

scholarly journals Predição temporal de parâmetros da qualidade da água usando redes neurais profundas / Temporal prediction of water quality parameters using deep neural networks

2021 ◽  
Vol 7 (11) ◽  
pp. 107662-107678
Author(s):  
Anderson Francisco de Sousa Almeida ◽  
Adonney Allan de Oliveira Veras ◽  
Bruno Merlin ◽  
Adam Santos ◽  
Marcos Amaris
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Deep Neural Networks ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Temporal Prediction

scholarly journals Mapping Water Quality Parameters in Urban Rivers from Hyperspectral Images Using a New Self-Adapting Selection of Multiple Artificial Neural Networks

2020 ◽  
Vol 12 (2) ◽  
pp. 336 ◽  
Author(s):  
Yishan Zhang ◽  
Lun Wu ◽  
Huazhong Ren ◽  
Yu Liu ◽  
Yongqian Zheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Hyperspectral Remote Sensing ◽  
Water Quality Parameters ◽  
Proposed Model ◽  
Selection Of

Protection of water environments is an important part of overall environmental protection; hence, many people devote their efforts to monitoring and improving water quality. In this study, a self-adapting selection method of multiple artificial neural networks (ANNs) using hyperspectral remote sensing and ground-measured water quality data is proposed to quantitatively predict water quality parameters, including phosphorus, nitrogen, biochemical oxygen demand (BOD), chemical oxygen demand (COD), and chlorophyll a. Seventy-nine ground measured data samples are used as training data in the establishment of the proposed model, and 30 samples are used as testing data. The proposed method based on traditional ANNs of numerical prediction involves feature selection of bands, self-adapting selection based on multiple selection criteria, stepwise backtracking, and combined weighted correlation. Water quality parameters are estimated with coefficient of determination R 2 ranging from 0.93 (phosphorus) to 0.98 (nitrogen), which is higher than the value (0.7 to 0.8) obtained by traditional ANNs. MPAE (mean percent of absolute error) values ranging from 5% to 11% are used rather than root mean square error to evaluate the predicting precision of the proposed model because the magnitude of each water quality parameter considerably differs, thereby providing reasonable and interpretable results. Compared with other ANNs with backpropagation, this study proposes an auto-adapting method assisted by the above-mentioned methods to select the best model with all settings, such as the number of hidden layers, number of neurons in each hidden layer, choice of optimizer, and activation function. Different settings for ANNS with backpropagation are important to improve precision and compatibility for different data. Furthermore, the proposed method is applied to hyperspectral remote sensing images collected using an unmanned aerial vehicle for monitoring the water quality in the Shiqi River, Zhongshan City, Guangdong Province, China. Obtained results indicate the locations of pollution sources.

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