Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile

Abstract

In this study, we combined machine learning and remote sensing techniques to estimate the value of chlorophyll-a concentration in a freshwater ecosystem in the South American continent (lake in Southern Chile). In a previous study, nine artificial intelligence (AI) algorithms were tested to predict water quality data from measurements during monitoring campaigns. In this study, in addition to field data (Case A), meteorological variables (Case B) and satellite data (Case C) were used to predict chlorophyll-a in Lake Llanquihue. The models used were SARIMAX, LSTM, and RNN, all of which showed generally good statistics for the prediction of the chlorophyll-a variable. Model validation metrics showed that all three models effectively predicted chlorophyll as an indicator of the presence of algae in water bodies. Coefficient of determination values ranging from 0.64 to 0.93 were obtained, with the LSTM model showing the best statistics in any of the cases tested. The LSTM model generally performed well across most stations, with lower values for MSE (<0.260 (μg/L)2), RMSE (<0.510 ug/L), MaxError (<0.730 μg/L), and MAE (<0.442 μg/L). This model, which combines machine learning and remote sensing techniques, is applicable to other Chilean and world lakes that have similar characteristics. In addition, it is a starting point for decision-makers in the protection and conservation of water resource quality.

Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated In Situ and Remote Sensing Analysis of an Urban Lake in Chile

“…When comparing the in situ Chl-a and turbidity data to the reflectance signals corrected by the different atmospheric methods, ACOLITE showed the highest coefficients of determination, reaching R 2 values of 0.88 and 0.79 for Chl-a and turbidity, respectively. These results are in line with a recent study conducted by Rodríguez-López et al [9], in which ACOLITE was used as an atmospheric distortion mitigation method on L-8 OLI scenes to estimate Chl-a concentrations in the Llanquihue Lake (Southern Chile). Other water quality research studies have found that ACOLITE effectively corrects for atmospheric distortions in water bodies, especially in the development of Chl-a estimation models from L-8 satellite OLI scenes (e.g., [56][57][58]).…”

“…As a result, Equation ( 9) is the final turbidity retrieval model. Turbidity = 0.51 × e (130.75×(Red+N IR)) (9) As in the Chl-a estimation model, the bootstrapping method was performed using 1000 iterations to optimize the selection of function parameters between mean and median (Figure 13). As a result, Equation ( 9) is the final turbidity retrieval model.…”

“…𝑇𝑢𝑟𝑏𝑖𝑑𝑖𝑡𝑦 0.51 𝑒 . (9) Figure 13. Calibration curve between the in situ turbidity (NTU) data and the red + NIR index with the application of the bootstrapping technique using the mean fit in the parameters with 1000 iterations, using 80% of the dataset in each iteration to compute the correlation and the remaining 20% for validation.…”

“…Thus, remote sensing offers a comprehensive and immediate perspective of aquatic, atmospheric, and terrestrial systems, and it has significant advantages over traditional monitoring methods, including large-scale coverage, real-time monitoring capabilities, and cost-effectiveness. It serves as an optimal tool for the continuous monitoring of the water quality in surface bodies [9][10][11]. When this type of monitoring is also combined with in situ measurements, it can deliver unparalleled, cost-efficient, and quantitative assessments of water quality variations.…”

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning

“…Eutrophication, an essential manifestation of water pollution in freshwater reservoirs, results from the excessive nutrient loads that cause algal blooms [1,2]. The excessive algae and aquatic plant growth result in oxygen depletion, ultimately causing severe impacts on aquatic ecosystems and considerably increasing the costs related to water treatment [3][4][5][6].…”