A generalized machine learning approach for dissolved oxygen estimation at multiple spatiotemporal scales using remote sensing

Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning

Oceanic dissolved oxygen (DO) is crucial for oceanic material cycles and marine biological activities. However, obtaining subsurface DO values directly from satellite observations is limited due to the restricted observed depth. Therefore, it is essential to develop a connection between surface oceanic parameters and subsurface DO values. Machine learning (ML) methods can effectively grasp the complex relationship between input attributes and target variables, making them a valuable approach for estimating subsurface DO values based on surface oceanic parameters. In this study, the potential of ML methods for subsurface DO retrieval is analyzed. Among the selected ML methods, namely support vector regression (SVR), random forest (RF) regression, and extreme gradient boosting (XGBoosting) regression, the RF method generally demonstrates superior performance. As the depth increases, the accuracy of DO estimates tends to initially decrease, then gradually improve, with the poorest performance occurring at the depth of 600 dbar. The range of determination coefficients (R2) and root mean square error (RMSE) values based on the test dataset at different depths lies between 0.53 and 47.59 μmol/kg to 0.99 and 4.01 μmol/kg. In addition, compared to sea surface salinity (SSS) and sea surface chlorophyll-a (SCHL), sea surface temperature (SST) plays a more significant role in DO retrieval. Finally, compared to the pelagic interactions scheme for carbon and ecosystem studies (PISCES) model, the RF method achieves higher retrieval accuracies at depths above 700 dbar. In the deep ocean, the primary differences in DO values obtained from the RF method and the PISCES model-based method are noticeable in the vicinity of the equatorial region.

Estimation of water quality variables based on machine learning model and cluster analysis-based empirical model using multi-source remote sensing data in inland reservoirs, South China

Reservoirs play important roles in the drinking water supply for urban residents, agricultural water provision, and the maintenance of ecosystem health. Satellite optical remote sensing of water quality variables in medium and micro-sized inland waters under oligotrophic and mesotrophic status is challenging in terms of the spatio-temporal resolution, weather conditions and frequent nutrient status changes in reservoirs, etc., especially when quantifying non-optically active components (non-OACs). This study was based on the surface reflectance products of unmanned aerial vehicle (UAV) multispectral images, Sentinel-2B Multispectral instrument (MSI) images and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) by utilizing fuzzy C-means (FCM) clustering algorithm was combined with band combination (BC) model to construct the FCM-BC empirical model, and used mixed density network (MDN), extreme gradient boosting (XGBoost), deep neural network (DNN) and support vector regression (SVR) machine learning (ML) models to invert 12 kinds of optically active components (OACs) and non-OACs. Compared with the unclustered BC (UC) model, the mean coefficient of determination (MR) of the FCM-BC models was improved by at least 46.9%. MDN model showed best accuracy (R2 in the range of 0.60-0.98) and stability (R2 decreased by up to 13.2%). The accuracy of UAV was relatively higher in both empirical methods and machine learning methods. Additionally, the spatio-temporal distribution maps of four water quality variables were mapped based on the MDN model and UAV images, all platforms showed good consistency. An inversion strategy of water quality variables in various monitoring frequencies and weather conditions were proposed finally. The purpose of introducing the UAV platform was to cooperate with the satellite to improve the monitoring response ability of OACs and non-OACs in small and micro-sized oligotrophic and mesotrophic water bodies.

Satellite-Based Global Sea Surface Oxygen Mapping and Interpretation with Spatiotemporal Machine Learning

The assessment of dissolved oxygen (DO) concentration at the sea surface is essential for comprehending the global ocean oxygen cycle and associated environmental and biochemical processes as it serves as the primary site for photosynthesis and sea-air exchange. However, limited comprehensive measurements and imprecise numerical simulations have impeded the study of global sea surface DO and its relationship with environmental challenges. This paper presents a novel spatiotemporal information embedding machine-learning framework that provides explanatory insights into the underlying driving mechanisms. By integrating extensive in situ data and high-resolution satellite data, the proposed framework successfully generated high-resolution (0.25° × 0.25°) estimates of DO concentration with exceptional accuracy (R2 = 0.95, RMSE = 11.95 μmol/kg, and test number = 2805) for near-global sea surface areas from 2010 to 2018, uncertainty estimated to be ±13.02 μmol/kg. The resulting sea surface DO data set exhibits precise spatial distribution and reveals compelling correlations with prominent marine phenomena and environmental stressors. Leveraging its interpretability, our model further revealed the key influence of marine factors on surface DO and their implications for environmental issues. The presented machine-learning framework offers an improved DO data set with higher resolution, facilitating the exploration of oceanic DO variability, deoxygenation phenomena, and their potential consequences for environments.

A method based on improved ant colony algorithm feature selection combined with GWO-SVR model for predicting chlorophyll-a concentration in Wuliangsu Lake

Chlorophyll-a (Chl-a) is an important parameter in water bodies. Due to the complexity of optics in water bodies, it is difficult to accurately predict Chl-a concentrations in water bodies by current traditional methods. In this paper, using Sentinel-2 remote sensing images as the data source combined with measured data, taking Wuliangsu Lake as the study area, a new intelligent algorithm is proposed for prediction of Chl-a concentration, which uses the adaptive ant colony exhaustive optimization algorithm (A-ACEO) for feature selection and the gray wolf optimization algorithm (GWO) to optimize support vector regression (SVR) to achieve Chl-a concentration prediction. The ant colony optimization algorithm is improved to select remote sensing feature bands for Chl-a concentration by introducing relevant optimization strategies. The GWO-SVR model is built by optimizing SVR using GWO with the selected feature bands as input and comparing it with the traditional SVR model. The results show that the usage of feature bands selected by the presented A-ACEO algorithm as inputs can effectively reduce complexity and improve the prediction performance of the model, under the condition of the same model, which can provide valuable references for monitoring the Chl-a concentration in Wuliangsu Lake.

Remote sensing retrieval of inland water quality parameters using Sentinel-2 and multiple machine learning algorithms

Remote sensing has long been an effective method for water quality monitoring because of its advantages such as high coverage and low consumption. For non-optically active parameters, traditional empirical and analytical methods cannot achieve quantitative retrieval. Machine learning has been gradually used for water quality retrieval due to its ability to capture the potential relationship between water quality parameters and satellite images. This study is based on Sentinel-2 images and compared the ability of four machine learning algorithms (eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Network (ANN)) to retrieve chlorophyll-a (Chl-a), dissolved oxygen (DO), and ammonia-nitrogen (NH³-N) for inland reservoirs. The results indicated that XGBoost outperformed the other three algorithms. We used XGBoost to reconstruct the spatial-temporal patterns of Chl-a, DO, and NH³-N for the period of 2018-2020 and further analyzed the interannual, seasonal, and spatial variation characteristics. This study provides an efficient and practical way for optically and non-optically active parameters monitoring and management at the regional scale.

An ensemble machine learning model for water quality estimation in coastal area based on remote sensing imagery

The accurate estimation of coastal water quality parameters (WQPs) is crucial for decision-makers to manage water resources. Although various machine learning (ML) models have been developed for coastal water quality estimation using remote sensing data, the performance of these models has significant uncertainties when applied to regional scales. To address this issue, an ensemble ML-based model was developed in this study. The ensemble ML model was applied to estimate chlorophyll-a (Chla), turbidity, and dissolved oxygen (DO) based on Sentinel-2 satellite images in Shenzhen Bay, China. The optimal input features for each WQP were selected from eight spectral bands and seven spectral indices. A local explanation strategy termed Shapley Additive Explanations (SHAP) was employed to quantify contributions of each feature to model outputs. In addition, the impacts of three climate factors on the variation of each WQP were analyzed. The results suggested that the ensemble ML models have satisfied performance for Chla (errors = 1.7%), turbidity (errors = 1.5%) and DO estimation (errors = 0.02%). Band 3 (B3) has the highest positive contribution to Chla estimation, while Band Ration Index2 (BR2) has the highest negative contribution to turbidity estimation, and Band 7 (B7) has the highest positive contribution to DO estimation. The spatial patterns of the three WQPs revealed that the water quality deterioration in Shenzhen Bay was mainly influenced by input of terrestrial pollutants from the estuary. Correlation analysis demonstrated that air temperature (Temp) and average air pressure (AAP) exhibited the closest relationship with Chla. DO showed the strongest negative correlation with Temp, while turbidity was not sensitive to Temp, average wind speed (AWS), and AAP. Overall, the ensemble ML model proposed in this study provides an accurate and practical method for long-term Chla, turbidity, and DO estimation in coastal waters.

Forward-Looking Roadmaps for Long-Term Continuous Water Quality Monitoring: Bottlenecks, Innovations, and Prospects in a Critical Review

Long-term continuous monitoring (LTCM) of water quality can bring far-reaching influences on water ecosystems by providing spatiotemporal data sets of diverse parameters and enabling operation of water and wastewater treatment processes in an energy-saving and cost-effective manner. However, current water monitoring technologies are deficient for long-term accuracy in data collection and processing capability. Inadequate LTCM data impedes water quality assessment and hinders the stakeholders and decision makers from foreseeing emerging problems and executing efficient control methodologies. To tackle this challenge, this review provides a forward-looking roadmap highlighting vital innovations toward LTCM, and elaborates on the impacts of LTCM through a three-hierarchy perspective: data, parameters, and systems. First, we demonstrate the critical needs and challenges of LTCM in natural resource water, drinking water, and wastewater systems, and differentiate LTCM from existing short-term and discrete monitoring techniques. We then elucidate three steps to achieve LTCM in water systems, consisting of data acquisition (water sensors), data processing (machine learning algorithms), and data application (with modeling and process control as two examples). Finally, we explore future opportunities of LTCM in four key domains, water, energy, sensing, and data, and underscore strategies to transfer scientific discoveries to general end-users.

The optimization of Low Impact Development placement considering life cycle cost using Genetic Algorithm

Low Impact Development (LID) is an effective measure in controlling the urban runoff and mitigating the non-point source pollution. The determination of LID facilities and layouts for a sub-catchment is important for designing stormwater management system. However, there are remain large uncertainty and challenge exist in determination of LID facilities when consider budget, land use, soil surface and groundwater as well as local climate etc. To address this issue, this study employed Genetic Algorithm (GA) for optimization of the selection and layout of LID. The urban runoff was simulated using Environmental Protection Agency (EPA) Storm Water Management Model (SWMM). The LID planning was encoded as 0 and 1 in GA algorithm. The multiple objectives which include runoff reduction, area of LID and life cycle cost were selected as optimization targets. To test the model performance, the Airport Economic Zone in Tianjin, China was chosen as the study area. The results demonstrate that the combination of LID approaches are most effective measures on runoff reduction through long-term simulation (10 years' rainfall events). The impact of different weights of land area and cost on LID selection were evaluated when considering life cycle cost. Bio-Retention is preferred when considering the area of LID and Green Roof is recommended when the cost is prioritized. The present research proved GA is feasible for LID planning in urban area. The proposed method can help the decision-makers to determine the LID plan more scientific based on SWMM model and GA.