Abstract
This study aimed to develop a reliable turbidity model to assess reservoir turbidity based on Landsat-8 satellite imagery. Models were established by multiple linear regression (MLR) and gene-expression programming (GEP) algorithms. Totally 55 and 18 measured turbidity data from Tseng-Wen and Nan-Hwa reservoir paired and screened with satellite imagery. Finally, MLR and GEP were applied to simulated 13 turbid water data for critical turbidity assessment. The coefficient of determination (R2), root mean squared error (RMSE), and relative RMSE (R-RMSE) calculated for model performance evaluation. The result show that, in model development, MLR and GEP shows a similar consequent. However, in model testing, the R2, RMSE, and R-RMSE of MLR and GEP are 0.7277 and 0.8278, 0.7248 NTU and 0.5815 NTU, 22.26% and 17.86%, respectively. Accuracy assessment result shows that GEP is more reasonable than MLR, even in critical turbidity situation, GEP is more convincible. In the model performance evaluation, MLR and GEP are normal and good level, in critical turbidity condition, GEP even belongs to outstanding level. These results exhibit GEP denotes rationality and with relatively good applicability for turbidity simulation. From this study, one can conclude that GEP is suitable for turbidity modeling and is accurate enough for reservoir turbidity estimation.
Highlights
In Taiwan, public water is primarily supplied by reservoirs
The reservoir turbidity quarterly measured by the Environmental Protection Administration (EPA)
The multiple linear regression (MLR) and gene-expression programming (GEP) turbidity simulation models could be developed by the data from Tseng-Wen reservoir and those models adopted in Nan-Hwa reservoir to test the model applicability
Summary
In Taiwan, public water is primarily supplied by reservoirs. The reservoir turbidity quarterly measured by the Environmental Protection Administration (EPA). Turbidity data was released after a strict quality control process [1]. The data are highly accurate, sampling and analysis are considerably time-consuming and are subject to several financial constraints [2]. The regional representation of samples from single-point sampling is observed to be insufficient; the test results are debatable if they are estimated based on the inspection results that are obtained from few stations [3]. Several scholars have recently used satellite imagery to establish the relationship between turbidity and multispectral data as a remote monitoring manner. Roeflsema et al, highlighted that the estimation performed based on a Landsat 7 satellite image exhibits an area coverage rate of
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