| Summary: | 碩士 === 國立成功大學 === 測量及空間資訊學系 === 107 === Healthy inland freshwater sources, such as lakes, reservoirs, rivers, and streams, play crucial roles in providing numerous benefits to surrounding societies. However, these inland water bodies, named case-II waters, have been severely polluted by human activities. Therefore, long-term monitoring and real-time measurements of water quality are essential to identify the changes of water quality for unexpected environmental incidents avoidance.
Over last 40 years, satellite-based remote sensing techniques, which have become powerful tools, enable researchers to efficiently monitor water quality of large-scale waterbodies. The success of satellite-based water quality studies relies on three key components: precise atmospheric correction method, optimization algorithm, and regression model. Previous studies integrated various algorithms and regression models, including (semi-) empirical or (semi-) analytical algorithms, and (non-) linear regression models, to obtain satisfactory results. Nevertheless, the selection of appropriate algorithm is complex and challenging because of the fact that the changes in chemical and physical properties of water can lead to different method determination. Ultimately, an accurate correction for atmospheric effects, especially in turbid productive case-II waters, which plays as important pre-processing step, is not always fully considered.
To alleviate the aforementioned difficulties, this study proposed a potential integration which comprises an optimization method for efficient water-quality model selection, ordinary least squares regression, and an accurately atmospheric corrected dataset. Prime focus of this study is water-quality model selection which optimizes an objective function that aims to maximize prediction accuracy of regression models.
According to the experiments, the performance of the selected water-quality model using proposed procedures, dominated that of the existing algorithms in terms of root-mean-squared-error (RMSE), normalized-mean-absolute-error (NMAE), the Pearson correlation coefficient (r), and slope of the regressed line (m) between measured and predicted chlorophyll-a.
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