Machine learning method for quick identification of water quality index (WQI) based on sentinel-2 MSI data: Ebinur lake case study

Abstract Surface water quality is an important factor affecting the ecological environment and human living environment. The monitoring of surface water quality by remote sensing monitoring technology can provide important research significance for water resources protection and water quality evaluation. Finding the optimal spectral index sensitive to water quality for remote sensing monitoring of water quality is extremely important for surface water quality analysis and treatment in the Ebinur Lake Basin in arid areas. This study used Sentinel-2MSI data at 10 m resolution to quickly monitor the water quality of the watershed. Through laboratory experiments and measurement data from the Ebinur Lake Basin, 22 WQPs were obtained. Through Z-score and redundancy analysis, 9 WQPs with significant contributions were extracted. Based on the remote sensing spectral band, 4 water indexes (NDWI, NWI, EWI, AWEI-nsh) and 2D modeling spectral index(DI, RI, NDI), the correlation analysis between WQPs and two kinds of spectral band indexes is carried out, and it is concluded that the overall correlation between WQP and 2D spectral modeling is more relevant. Calculate the evaluation and model the 2D spectrum of the Water Quality Index (WQI). The WQI is predicted and modeled through 4 machine learning algorithms (RF, SVM, PLSR, PLSR-SVM).The results show that the inversion effect of the two-dimensional spectral modeling index on water quality parameters (WQPs) is superior to that of the water index, and the correlation coefficient of the DI (R12-R1) SWIR-2 and BLUE band interpolation index reaches 0.787. On this basis, three kinds of two-dimensional spectral modeling indexes are used to inversely synthesize the WQI, and the correlation coefficient of the ratio index of the RI (R11/R8) SWIR-1 and NIR bands is preferably 0.69. In the WQI prediction, the partial least squares regression support vector machine (PLSR-SVM) model in machine learning algorithms has good modeling and prediction effects (R2c = 0.873, R2v = 0.87), which can provide a good basis. The research results provide references for remote monitoring of surface water in arid areas, and provide a basis for water quality prediction and safety evaluation.

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Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms

Remote Sensing ◽

10.3390/rs13224662 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4662

Author(s):

Zhi Qiao ◽

Siyang Sun ◽

Qun’ou Jiang ◽

Ling Xiao ◽

Yunqi Wang ◽

...

Keyword(s):

Water Pollution ◽

Machine Learning ◽

Remote Sensing ◽

Water Quality ◽

Surface Water ◽

Total Phosphorus ◽

Remote Sensing Data ◽

Machine Learning Algorithms ◽

Retrieval Model ◽

Miyun Reservoir

Some essential water conservation areas in China have continuously suffered from various serious problems such as water pollution and water quality deterioration in recent decades and thus called for real-time water pollution monitoring system underwater resources management. On the basis of the remote sensing data and ground monitoring data, this study firstly constructed a more accurate retrieval model for total phosphorus (TP) concentration by comparing 12 machine learning algorithms, including support vector machine (SVM), artificial neural network (ANN), Bayesian ridge regression (BRR), lasso regression (Lasso), elastic net (EN), linear regression (LR), decision tree regressor (DTR), K neighbor regressor (KNR), random forest regressor (RFR), extra trees regressor (ETR), AdaBoost regressor (ABR) and gradient boosting regressor (GBR). Then, this study applied the constructed retrieval model to explore the spatial-temporal evolution of the Miyun Reservoir and finally assessed the water quality. The results showed that the model of TP concentration built by the ETR algorithm had the best accuracy, with the coefficient R2 reaching over 85% and the mean absolute error lower than 0.000433. The TP concentration in Miyun Reservoir was between 0.0380 and 0.1298 mg/L, and there was relatively significant spatial and temporal heterogeneity. It changed remarkably during the periods of the flood season, winter tillage, planting, and regreening, and it was lower in summer than in other seasons. Moreover, the TP in the southwest part of the reservoir was generally lower than in the northeast, as there was less human activities interference. According to the Environmental Quality Standard for the surface water environment, the water quality of Miyun Reservoir was overall safe, except only for an over-standard case occurrence in the spring and September. These conclusions can provide a significant scientific reference for water quality monitoring and management in Miyun Reservoir.

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Environmental assessment based surface water quality prediction using hyper-parameter optimized machine learning models based on consistent big data

Process Safety and Environmental Protection ◽

10.1016/j.psep.2021.05.026 ◽

2021 ◽

Author(s):

Muhammad Izhar Shah ◽

Muhammad Faisal Javed ◽

Abdulaziz Alqahtani ◽

Ali Aldrees

Keyword(s):

Machine Learning ◽

Water Quality ◽

Big Data ◽

Surface Water ◽

Environmental Assessment ◽

Surface Water Quality ◽

Quality Prediction ◽

Learning Models ◽

Water Quality Prediction ◽

Machine Learning Models

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Remote sensing of surface water quality in relation to catchment condition in Zimbabwe

Physics and Chemistry of the Earth Parts A/B/C ◽

10.1016/j.pce.2017.02.013 ◽

2017 ◽

Vol 100 ◽

pp. 13-18 ◽

Cited By ~ 11

Author(s):

Mhosisi Masocha ◽

Amon Murwira ◽

Christopher H.D. Magadza ◽

Rafik Hirji ◽

Timothy Dube

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Surface Water ◽

Surface Water Quality

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Remote sensing inversion of water quality in coastal sea area based on machine learning: a case study of Shenzhen bay, China

10.5194/egusphere-egu21-1972 ◽

2021 ◽

Author(s):

Xiaotong Zhu ◽

Jinhui Jeanne Huang

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Water Quality ◽

Predictive Accuracy ◽

Water Environment ◽

Quality Parameters ◽

Machine Learning Algorithms ◽

Dynamic Monitoring ◽

Support Vector ◽

Seawater Quality

Remote sensing monitoring has the characteristics of wide monitoring range, celerity, low cost for long-term dynamic monitoring of water environment. With the flourish of artificial intelligence, machine learning has enabled remote sensing inversion of seawater quality to achieve higher prediction accuracy. However, due to the physicochemical property of the water quality parameters, the performance of algorithms differs a lot. In order to improve the predictive accuracy of seawater quality parameters, we proposed a technical framework to identify the optimal machine learning algorithms using Sentinel-2 satellite and in-situ seawater sample data. In the study, we select three algorithms, i.e. support vector regression (SVR), XGBoost and deep learning (DL), and four seawater quality parameters, i.e. dissolved oxygen (DO), total dissolved solids (TDS), turbidity(TUR) and chlorophyll-a (Chla). The results show that SVR is a more precise algorithm to inverse DO (R2 = 0.81). XGBoost has the best accuracy for Chla and Tur inversion (R2 = 0.75 and 0.78 respectively) while DL performs better in TDS (R2 =0.789). Overall, this research provides a theoretical support for high precision remote sensing inversion of offshore seawater quality parameters based on machine learning.

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Remote sensing and gis applications in determining shoreline and surface water quality changes in Bayelsa state, Nigeria

Ethiopian Journal of Environmental Studies and Management ◽

10.4314/ejesm.v10i5.2 ◽

1970 ◽

Vol 10 (5) ◽

pp. 572-587

Author(s):

A.O. Adebola ◽

T.H.T Ogunribido ◽

S.A. Adegboyega ◽

M.O. Ibitoye ◽

A.A Adeseko

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Surface Water ◽

Landsat Imagery ◽

Surface Water Quality ◽

Remote Sensing And Gis ◽

Shoreline Changes ◽

Gis Applications ◽

Gis Environment ◽

Coastal Landform

The study of shoreline changes is essential for updating the changes in shoreline maps and management of natural resources as the shoreline is one of the most important features on the earth’s surface. Shorelines are the key element in coastal GIS that provide information on coastal landform dynamics. The purpose of this paper is to investigate shoreline changes in the study area and how it affects surface water quality using Landsat imagery from 1987 to 2016. The image processing techniques adopted involves supervised classification, object-based image analysis, shoreline extraction and image enhancement. The data obtained was analyzed and maps were generated and then integrated in a GIS environment. The results indicate that LULC changes in wetland areas increases rapidly during the years (1987-2016) from 34.83 to 38.96%, vegetation cover reduces drastically through the year which range from 30% to 20%. Polluted surface water was observed to have decreased from 30% to 20% during 1984-2010 and reduced by about 3% in 2016. In addition, the result revealed the highest level of erosion from 1987 to 2016 which is -49.60% against the highest level of accretion of 13.39% EPR and NSM -1400 erosion against 350 accretions. It was also observed that variations in shoreline changes affect the quality of surface water possibly due to shoreline movement hinterland. This study has demonstrated that through satellite remote sensing and GIS techniques, the Nigerian coastline can adequately be monitored for various changes that have taken place over the years.Key Words: Shoreline, Remote Sensing, Erosion, Accretion, GIS

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