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

Abstract

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.

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

“…In our study, we conducted a comprehensive comparative analysis of various supervised machine learning techniques to assess their model performance (Figure 6a). The techniques examined include LR [29][30][31][32][33][34][35][36]45,46,51,65,75,, PLSR [22,27,[185][186][187][188][189], GPR [33,35,46,171,[190][191][192][193][194], GP [45,158,175,192,[195][196][197][198][199], SVM [22,25,26,[29][30][31][33][34][35]40,…”

“…DNNs, on the other hand, typically have more layers and can capture increasingly complex and abstract features, making them better suited for complex regression problems. This is because the first layers of a neural network detect simple features in the input data, such as edges or local patterns, whereas the deeper layers detect more complex and abstract features, such as global patterns or relationships between input features [28,30,194,246,251]. For complex regression problems, DNNs have proven to be more effective than simple ANNs [28,30,194,246,251].…”

“…This dominance is attributed to advanced algorithms, increased computational power, and unprecedented access to massive datasets. Deep neural networks commonly outperform shallow neural networks in terms of accuracy; however, their superior performance comes at the expense of higher computational costs and a greater susceptibility to overfitting due to their greater model complexity [28,30,194,246,251]. As a result, determining the best conditions and parameters for deep neural networks remains an ongoing area of research and whether to use a simple ANN or a DNN ultimately depends on the specifics of the problem at hand.…”

Air Temperature Monitoring over Low Latitude Rice Planting Areas: Combining Remote Sensing, Model Assimilation, and Machine Learning Techniques

“…For instance, Liu et al used the RF model to estimate near-surface Ta in an arid region of northwest China with satellite measurements [35]. As a recurrent neural network (RNN), the long short-term memory (LSTM) neural network model has exhibited exemplary performance in LST estimation [36][37][38][39][40]. Chung et al constructed a Ta estimation LSTM model using different LST data during cold and hot periods in one year and achieved acceptable performance during cold periods [41].…”

“…Yang et al also evaluated the performance of large-scale LSTM models for Ta [42]. The potential of the artificial neural network (ANN) model for temperature estimation has also been demonstrated [39,[43][44][45][46]. Runke et al applied the ANN model to achieve high-temperature estimation accuracy in complex mountainous areas [47].…”