Feature selection for estimation of chlorophyll-a concentration in Kasumigaura Lake Japan

  • 阮 文孟

Student thesis: Master's Thesis

Abstract

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
Date of Award2019
LanguageEnglish
SupervisorChao-Hung Lin (Supervisor)

Cite this

Feature selection for estimation of chlorophyll-a concentration in Kasumigaura Lake Japan
文孟, 阮. (Author). 2019

Student thesis: Master's Thesis