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Article

Micro-Climate Computed Machine and Deep Learning Models for Prediction of Surface Water Temperature Using Satellite Data in Mundan Water Reservoir

1
Department of Civil Engineering, National Pingtung University of Science & Technology, No. 1, Shuefu Road, Neipu 91201, Pingtung, Taiwan
2
Department of Soil and Water Conservation, National Pingtung University of Science & Technology, No. 1, Shuefu Road, Neipu 91201, Pingtung, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Jin Zhang, Yun Bai and Pei Hua
Water 2022, 14(18), 2935; https://doi.org/10.3390/w14182935
Received: 14 August 2022 / Revised: 5 September 2022 / Accepted: 15 September 2022 / Published: 19 September 2022
Water temperature is an important indicator of water quality for surface water resources because it impacts solubility of dissolved gases in water, affects metabolic rates of aquatic inhabitants, such as fish and harmful algal blooms (HABs), and determines the fate of water resident biogeochemical nutrients. Furthermore, global warming is causing a widespread rise in temperature levels in water sources on a global scale, threatening clean drinking water supplies. Therefore, it is key to increase the frequency of spatio-monitoring for surface water temperature (SWT). However, there is a lack of comprehensive SWT monitoring datasets because current methods for monitoring SWT are costly, time consuming, and not standardized. The research objective of this study was to estimate SWT using data from the Landsat-8 (L8) and Sentinel-3 (S3) satellites. To do this, we used machine learning techniques, such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), simple neural network (ANN), and deep learning techniques (Long Short Term Memory, LSTM, and Convolutional Long Short Term Memory, 1D ConvLSTM). Using deep and machine learning techniques to regress satellite data to estimate SWT presents a number of challenges, including prediction uncertainty, over- or under-estimation of measured values, and significant variation in the final estimated data. The performance of the L8 ConvLSTM model was superior to all other methods (R2 of 0.93 RMSE of 0.16 °C, and bias of 0.01 °C). The factors that had a significant effect on the model’s accuracy performance were identified and quantified using a two-factor analysis of variance (ANOVA) analysis. The results demonstrate that the main effects and interaction of the type of machine/deep learning (ML/DL) model and the type of satellite have statistically significant effects on the performances of the different models. The test statistics are as follows: (satellite type main effect p *** ≤ 0.05, Ftest = 15.4478), (type of ML/DL main effect p *** ≤ 0.05, Ftest = 17.4607) and (interaction, satellite type × type of ML/DL p ** ≤ 0.05, Ftest = 3.5325), respectively. The models were successfully deployed to enable satellite remote sensing monitoring of SWT for the reservoir, which will help to resolve the limitations of the conventional sampling and laboratory techniques. View Full-Text
Keywords: water quality; water temperature; machine and deep learning; uncertainties; Landsat-8; Sentinel-3 water quality; water temperature; machine and deep learning; uncertainties; Landsat-8; Sentinel-3
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MDPI and ACS Style

Mukonza, S.S.; Chiang, J.-L. Micro-Climate Computed Machine and Deep Learning Models for Prediction of Surface Water Temperature Using Satellite Data in Mundan Water Reservoir. Water 2022, 14, 2935. https://doi.org/10.3390/w14182935

AMA Style

Mukonza SS, Chiang J-L. Micro-Climate Computed Machine and Deep Learning Models for Prediction of Surface Water Temperature Using Satellite Data in Mundan Water Reservoir. Water. 2022; 14(18):2935. https://doi.org/10.3390/w14182935

Chicago/Turabian Style

Mukonza, Sabastian Simbarashe, and Jie-Lun Chiang. 2022. "Micro-Climate Computed Machine and Deep Learning Models for Prediction of Surface Water Temperature Using Satellite Data in Mundan Water Reservoir" Water 14, no. 18: 2935. https://doi.org/10.3390/w14182935

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