Mapping concentrations of surface water quality parameters using a novel remote sensing and artificial intelligence framework

2017 ◽  
Vol 38 (4) ◽  
pp. 1023-1042 ◽  
Author(s):  
Essam Sharaf El Din ◽  
Yun Zhang ◽  
Alaeldin Suliman
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters

Assessement of Water Quality Parameters in Some Potential Pollutant Areas from Romania

2018 ◽  
Vol 69 (8) ◽  
pp. 2045-2049
Author(s):  
Catalina Gabriela Gheorghe ◽  
Andreea Bondarev ◽  
Ion Onutu
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Chemical Parameters ◽  
Water Quality Data ◽  
Pollutant Sources ◽  
Water Quality Forecasting

Monitoring of environmental factors allows the achievement of some important objectives regarding water quality, forecasting, warning and intervention. The aim of this paper is to investigate water quality parameters in some potential pollutant sources from northern, southern and east-southern areas of Romania. Surface water quality data for some selected chemical parameters were collected and analyzed at different points from March to May 2017.

scholarly journals Assessment of coastal surface water quality for irrigation purpose

2020 ◽  
Vol 15 (4) ◽  
pp. 960-972
Author(s):  
M. F. Serder ◽  
M. S. Islam ◽  
M. R. Hasan ◽  
M. S. Yeasmin ◽  
M. G. Mostafa
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Type ◽  
Post Monsoon ◽  
Irrigation Purpose

Abstract The study aimed to assess the coastal surface water quality for irrigation purposes through the analysis of the water samples of some selected estuaries, rivers, and ponds. The analysis results showed that the mean value of typical water quality parameters like electrical conductivity (EC), total dissolved solids (TDS), sodium (Na+), and chloride (Cl−) ions exceeded the permissible limit of the Department of Environment (DoE), Bangladesh 2010, and FAO, 1985 for the pre- and post-monsoon seasons. The Piper diagram indicated a Na-Cl water type, especially during the pre- and post-monsoon seasons. The water quality parameters in the areas showed a higher amount than the standard permissible limits, indicating that the quality is deteriorating. The water quality index values for domestic uses showed very poorly to unsuitable in most of the surface waters except pond water, especially during the pre- and post-monsoon periods. The surface water quality index for irrigation purpose usages was found to be high and/ or severely restricted (score: 0–55) during the pre- and post-monsoon seasons. The study observed that due to saline water intrusion, the water quality deterioration started from post-monsoon and reached its highest level during the pre-monsoon season, which gradually depreciates the water quality in coastal watersheds of Bangladesh.

Assessment of Surface Water Quality Parameters of Panchganga River

2021 ◽  
pp. 781-797
Author(s):  
Chidanand Patil ◽  
Sneha S. Bandekar ◽  
Sateesh Hosamane ◽  
Sanjeev Sangami ◽  
Amrut Adavimath
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters

Determination of water quality parameters in the Tan Rai exploiting area (Lam Dong province) using Sentinel-2 msi and Landsat 8 data

2020 ◽  
Vol 61 (2) ◽  
pp. 126-134
Author(s):  
Nghia Viet Nguyen ◽  
Hung Le Trinh ◽  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Secchi Depth ◽  
Great Influence ◽  
Surface Water Quality ◽  
Mining Area ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Landsat 8 ◽  
Optical Remote Sensing

Despite high profits, the mining process often leads to negative effects on the quality of groundwater around the mining site. Due to the close relationship between the concentration of water quality parameters and spectral reflectance values of surface watẻ, optical remote sensing image has been used effectively in the world in assessing and monitoring surface water quality. This paper presents the results of determining some surface water quality parameters in the Tan Rai bauxite mining area (Lam Dong province) such as turbidity, water-transparency (Secchi depth), and surface temperature from Sentinel-2A and Landsat 8 images taken on January 29, 2019. The results obtained in this study show that the mining process has a great influence on the surface water quality in Tan Rai (Lam Dong), reflected in all three water quality parameters such as turbidity, Secchi depth, and water temperature.

scholarly journals Spatiotemporal Analysis of Surface Water Quality in Dong Thap Province, Vietnam Using Water Quality Index and Statistical Approaches

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 336
Author(s):  
Nguyen Thanh Giao ◽  
Phan Kim Anh ◽  
Huynh Thi Hong Nhien
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
E Coli

The study was conducted to spatiotemporally analyze the quality, location and critical water variables influencing water quality using water monitoring data from the Department of Environment and Natural Resources, Dong Thap province in 2019. The water quality parameters including turbidity, pH, temperature, dissolved oxygen (DO), total suspended solids (TSS), biological oxygen demand (BOD), chemical oxygen demand (COD), nitrite (N-NO2−), nitrate (N-NO3−), ammonium (N-NH4+), total nitrogen (TN), orthophosphate (P-PO43−), chloride (Cl−), oil and grease, sulfate (SO42−), coliforms, and Escherichia coli (E. coli) were collected at 58 locations with the frequency of four times per year (February, May, August, and November). These parameters were compared with national technical regulation on surface water quality—QCVN 08-MT: 2015/BTNMT. Water quality index (WQI) was calculated and spatially presented by geographical information system (GIS) tool. Pearson correlation analysis, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the correlation among water quality parameters, group and reduce the sampling sites, and identify key parameters and potential water pollution sources. The results showed that TSS, BOD, COD, N-NH4+, P-PO43−, coliforms, and E. coli were the significant concerns impairing the water quality. Water quality was assessed from poor to medium levels by WQI analysis. CA suggested that the current monitoring locations could be reduced from 58 sites to 43 sites which can be saved the total monitoring budget up to 25.85%. PCA showed that temperature, pH, TSS, DO, BOD, COD, N-NH4+, N-NO2−, TN, P-PO43−, coliforms, and E. coli were the key water parameters influencing water quality in Dong Thap province’s canals and rivers; thus, these parameters should be monitored annually. The water pollution sources were possibly hydrological conditions, water runoff, riverbank erosion, domestic and urban activities, and industrial and agricultural discharges. Significantly, the municipal and agricultural wastes could be decisive factors to the change of surface water quality in the study area. Further studies need to focus on identifying sources of water pollution for implementing appropriate water management strategies.

scholarly journals EVALUATING SURFACE WATER QUALITY AND WATER MONITORING PARAMETERS IN THE TIEN RIVER, VIETNAMESE MEKONG DELTA

2021 ◽  
Vol 83 (3) ◽  
pp. 29-36
Author(s):  
Thanh Giao Nguyen ◽  
Vo Quang Minh
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Monitoring Program ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Monitoring ◽  
Water Quality Parameters

The study aimed to evaluate the surface water quality of the Tien River and identify water quality parameters to be monitored using the water quality monitoring data in the period of 2011 - 2019. The water samples were collected at five locations from Tan Chau to Cho Moi districts, An Giang province for three times per year (i.e., in March, June, and September). Water quality parameters included temperature (oC), pH, dissolved oxygen (DO), total suspended solids (TSS), nitrate (NO3--N), orthophosphate (PO43--P), biological oxygen demand (BOD), and coliforms. These parameter results were compared with the national technical regulation on surface water quality QCVN 08-MT: 2015/BTNMT, column A1. Principal component analysis (PCA) was used to identify the sources of pollution and the main factors affecting water quality. The results of this study showed that DO concentration was lower and TSS, BOD, PO43--P, coliforms concentrations in the Tien river exceeded QCVN 08-MT: 2015/BTNMT, column A1. pH, temperature, and NO3--N values were in accordance with the permitted regulation. The water monitoring parameters were seasonally fluctuated. DO, BOD, TSS, and coliforms concentrations were higher in the rainy season whereas NO3--N and PO43--P were higher in the dry season. The PCA results illustrated that pH, TSS, DO, BOD, PO43--P and coliforms should be included in the monitoring program. Other indicators such as temperature and NO3--N could be considered excluded from the program to save costs. 

scholarly journals Surface Water Pollution Risk From Vietnam Water Quality Index (VN-WQI) in the Ca Mau City, Mekong Delta

2021 ◽  
Vol 20 (4) ◽  
Author(s):  
Nguyen Ngan Ha ◽  
Tran Thi Thu Huong ◽  
Pham The Vinh ◽  
Tran Thi Van
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Sentinel 2

This paper presents the study of integrating the remote sensing technology with in-situ ground observation for assessing the status of water quality in Ca Mau city through the Vietnam Water Quality Index (VN-WQI). The Sentinel-2 image and in-situ surface water samples were collected on 20 February 2020 for this study. The sample results were then specified by samples’ coordination. Besides, Sentinel-2 imaging was processed by radiometric and atmospheric correction, geometric registration, and extracted pixel spectral values from the sample locations. The multiple linear regressions of seven water quality parameters including BOD5, COD, NH4, PO4, TSS, pH, Coliform with surface water’s pixel spectral values from the satellite images were calculated and used to simulate water quality parameters on the satellite image. They were integrated into the VN-WQI to estimate, classify, and evaluate the general surface water quality of the Ca Mau city. The results show that there is a regressive correlation between measured data and image spectral values, and the simulation also well fits with the data with an acceptable error. The surface water quality of Ca Mau city is heavily polluted with almost all water quality parameters recognized at B1 to above B2 level according to the QCVN08-MT:2015/BTNMT. In terms of VN-WQI, the results also illustrate the low quality of surface water and heavy pollution only used for water transportation, not for domestic use. This approach can be a powerful method in spatially monitoring water quality and supporting environment management.

scholarly journals Surface Water Quality Assessment Using Remote Sensing, Gis and Artificial Intelligence

2019 ◽  
Vol 1 (1) ◽  
pp. 113-122
Author(s):  
A. KC ◽  
A. Chalise ◽  
D. Parajuli ◽  
N. Dhital ◽  
S. Shrestha ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Water Quality ◽  
Surface Water ◽  
Quality Assessment ◽  
Satellite Imagery ◽  
Surface Water Quality ◽  
Water Quality Assessment ◽  
The Relationship ◽  
The City

The deterioration of surface water quality occurs due to the presence of various types of pollutants from human activities such as agriculture, industry, construction, deforestation, etc. Thus, the presence of various pollutants in water bodies can lead to deterioration of both surface water quality and aquatic life. Conventional surface water quality assessment methods are widely performed using laboratory analysis, which are labour intensive, costly, and time consuming. Moreover, these methods can only provide individual concentration of surface water quality parameters (SWQPs), measured at monitoring stations and shown in a discrete point format, which are difficult for decision-makers to understand without providing the overall patterns of surface water quality. To such problem, Remote Sensing has been a blessing because of its low cost, spatial continuity and temporal consistency. The relationship between SWQPs and satellite data is complex to be modelled accurately by using regression-based methods. Therefore, our study attempts to develop an artificial intelligence modelling method for mapping concentrations of both optical and non-optical SWQPs. This study aims to develop techniques for estimating the concentration of both optical and non-optical SWQPs from Satellite Imagery (Landsat8) which supports coastal studies and mapping the complex relationship between satellite multi-spectral signature and concentration of SWQPs. It will also focus on classifying the most significant SWQPs that contribute to both spatial and temporal surface water quality. In contrast to traditionally performed surface water quality assessment methods, this research project will be focused on identifying such parameters incorporating the new and evolving machine intelligence that is Artificial Intelligence (AI). Significant number of samples have to be collected along with the GPS data which is used to model the relationship. In this context, a remote-sensing framework based on the back-propagation neural network (BPNN) will be developed to quantify concentrations of different SWQPs from the Landsat8 satellite imagery. The study area chosen for this research is Bijayapur River of distance approximately 10 km flowing above, through and down the Pokhara city. The sole purpose of this research is to examine the water quality before it flows through the city and analysing after it passes through the city.

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