Water quality monitoring and assessment based on cruise monitoring, remote sensing, and deep learning: A case study of Qingcaosha Reservoir - Qian, Jing; Liu, Hongbo; Qian, Li; Bauer, Jonas; Xue, Xiaobai; Yu, Gongliang; He, Qiang; Zhou, Qi; Bi, Yonghong; Norra, Stefan (Frontiers in Environmental Science, 2022)
Accurate monitoring and assessment of the environmental state, as a prerequisite for improved action, is valuable and necessary because of the growing number of environmental problems that have harmful effects on natural systems and human society. This study developed an integrated novel framework containing three modules: remote sensing technology (RST), cruise monitoring technology (CMT), and deep learning, to achieve a robust performance for environmental monitoring and the subsequent assessment. The deep neural network (DNN), a type of deep learning, can adapt and take advantage of the big data platform effectively provided by RST and CMT to obtain more accurate and improved monitoring results. It was proved by our case study in the Qingcaosha reservoir (QCSR) that DNN showed a more robust performance (R^2=0.88 for pH, R^2=0.75 for DO, R^2=0.85 for conductivity, and R^2=0.94 for backscattered particles) compared to the traditional machine learning including multiple linear regression, support vector regression and random forest regression. Based on the monitoring results, the water quality assessment of QCSR was achieved by applying a deep learning algorithm called improved deep embedding clustering to classify QCSR into four categories of water bodies and identify the areas distributed in Class II and IV as water quality risk areas. This study presents a high value of the framework with a core of big data mining for environmental monitoring and follow-up assessment in a manner of high-frequency, multi-dimensionality, and deep-hierarchy.