Melika Haghparast; Mehdi Mokhtarzade
Volume 10, Issue 1 , June 2018, , Pages 91-108
Abstract
Due to the global scope of water resources, ground measurements of the quality parameters are not feasible, as well as traditional sampling of water and laboratory analysis is very costly and time-consuming. In studies, estimation of turbidity and chlorophyll a concentrations as the most important water ...
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Due to the global scope of water resources, ground measurements of the quality parameters are not feasible, as well as traditional sampling of water and laboratory analysis is very costly and time-consuming. In studies, estimation of turbidity and chlorophyll a concentrations as the most important water quality parameters using artificial neural networks have been done by researchers. Considering the difficulties in providing a high number of training data in aquatic environments, the use of more robust hybrid networks such as the wavelet neural network is suggested. In this research, various types of wavelet functions were used as a network activation function, and the best network was used to estimate chlorophyll a and turbidity respectively, wavelet neural networks with a Morelt and a Mexican hat activation function, the data used for the reflection of the ocean reflectance of the modis sensor, Due to the use of multi-time images, the radiometric normalization of data was done and the results were significantly improved compared to the time when the non-normalized images were used. in addition to increasing the number of training data, the network generalization capability is provided to other days, and the accuracy of the network in this case increased compared to the one-day condition. the RMSE for the best model to estimate turbidity And chlorophyll a concentration was 0.047 and 0.071, respectively, which is acceptable in comparison with field accuracy of 0.1, and can be a alternative method for field measurements.
