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The present study evaluates the water quality status of 6-km-long Kali River stretch that passes through the Aligarh district in Uttar Pradesh, India, by utilizing high-resolution IRS P6 LISS IV imagery. In situ river water samples collected at 40 random locations were analyzed for seven physicochemical and four heavy metal concentrations, and the...
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... Remote sensing and artificial intelligence models have been applied to evaluate river water quality indices (Chebud et al. 2012;Najafzadeh and Basirian 2023). Data-driven models have been used to evaluate the reliability of groundwater quality indices (Najafzadeh et al. , 2022Said and Khan 2021). Additionally, a novel multiple-kernel support vector regression algorithm has been developed for estimating water quality parameters (Najafzadeh and Niazmardi 2021). ...
The Ouislane sub-watershed is currently experiencing severe water shortages and is highly dependent on its water supply. The sub-watershed spans two communes: Meknes to the north and El Hajeb to the south. It serves as the primary water source for irrigation and drinking purposes for the local population. Consequently, it is crucial to assess the spatio-temporal variations of water quality to identify and address potential gaps; these focused on effective monitoring systems to detect contaminants, pollutants and health risks. This research project aims on the application of self-organizing map (SOM) techniques combined with cluster analysis to classify water quality in springs for drinking and irrigation purposes. The present study evaluates the water quality variations using physicochemical parameters of twelve water springs, collected during the wet and dry seasons of 2022. For this purpose, the water quality index (WQI), self-organizing map (SOM), hierarchical cluster analysis (HCA), and principal component analysis (PCA) are used as evaluation and classification methods. As a result, the SOM algorithm with a size of 5 × 5 units identified as the most suitable, based on the minimum quantization error (QE) and topographic error (TE), yielding a QE of 0.379 and a TE of 0.000. It grouped the water quality data into five distinct clusters, Cluster I represented 37.5% of the total samples, while cluster II represented 25%. Cluster III and IV each accounted for 8.33% of the samples, while 20.83% of the sampling water are classified in cluster V. Clusters I, II, and IV indicate good water suitable for drinking. However, cluster V had the highest WQI, suggesting very high contamination due to increased levels of the 10 studied physicochemical parameters. The water quality in this region (cluster V) is influenced by natural processes, such as precipitation intensity, weathering and vegetation cover, as well as anthropogenic factors like agriculture and urban concentration. PCA confirmed the clustering results obtained by SOM. However, SOM provides a more detailed classification and additional insights into the dominant variables influencing the classification processes. The results of this study suggest that SOM was an effective tool for gaining a better understanding of the patterns and processes driving water quality in the Ouislane sub-watershed and provides valuable avenues for further research to establish and monitor water quality for effective management of water resources.
... In Table 5, the R 2 of TP is the lowest among the parameters. This is because remote sensing data are limited by atmospheric conditions, sensor noise, viewing angles, and other factors [32], resulting in the fact that achievement of very high R 2 values is challenging when using remote sensing to inverse water parameters. However, data provide broad and continuous observations, which offer valuable information over large areas that traditional in situ measurements cannot reach. ...
With rapid social and economic development, land use/land cover change (LUCC) has intensified with serious impacts on water quality in the watershed. In this study, we took Dongjiang Lake watershed as the study area and obtained measured data on water quality parameters from the watershed’s water quality monitoring stations. Based on Landsat-5, Landsat-8, or Sentinel-2 remote sensing data for multiple periods per year between 1992 and 2022, the sensitive satellite bands or band combinations of each water quality parameter were determined. The Random Forest method was used to classify the land use types in the watershed into six categories, and the area proportion of each type was calculated. We established machine learning regression models and polynomial regression models with WQI as the dependent variable and the area proportion of each land use type as the independent variable. Accuracy test results showed that, among them, the quadratic cubic polynomial regression model with grassland, forest land, construction land, and unused land as its independent variables was the best model for coupling watershed water quality with LUCC. This study’s results provide a scientific basis for monitoring spatial and temporal changes in water quality caused by LUCC in the Dongjiang Lake watershed.
... It is recommended to collect in situ samples within a day from the satellite image capture. As a result, the errors will be minimized, and the algorithms will be better calibrated (Brezonik et al., 2005;Said & Khan, 2021). If there is an extended time gap between the satellite image capture and in situ sample collection, the accuracy of the data may be adversely affected. ...
The continuous availability of spatial and temporal distributed data from satellite sensors provides more accurate and timely information regarding surface water quality parameters. Remote sensing data has the potential to serve as an alternative to traditional on-site measurements, which can be resource-intensive due to the time and labor involved. This present study aims in exploring the possibility and comparison of hyperspectral and multispectral imageries (PRISMA) for accurate prediction of surface water quality parameters. Muthupet estuary, situated on the south side of the Cauvery River delta on the Bay of Bengal, is selected as the study area. The remote sensing data is acquired from the PRISMA hyperspectral satellite and the Sentinel-2 multispectral instrument (MSI) satellite. The in situ sampling from the study area is performed, and the testing procedures are carried out for analyzing different water quality parameters. The correlations between the water sample results and the reflectance values of satellites are analyzed to generate appropriate algorithmic models. The study utilized data from both the PRISMA and Sentinel satellites to develop models for assessing water quality parameters such as total dissolved solids, chlorophyll, pH, and chlorides. The developed models demonstrated strong correlations with R² values above 0.80 in the validation phase. PRISMA-based models for pH and chlorophyll displayed higher accuracy levels than Sentinel-based models with R² > 0.90.
... Researchers analyzed samples in a laboratory, and usually, the processes were time-consuming, laborious, and costly (Cassidy and Jordan 2011). With the widespread application of remote sensing and advancements of geographic analysis systems, studies have investigated the water contamination levels using space and time perspectives (Pahlevan et al. 2019;Sagan et al. 2020;O'Grady et al. 2021;Said and Khan 2021). Remote sensing offers a speedy and economically viable method to monitor surface water quality in large geographic areas (Chen et al. 2022;Wang and Yang 2019;Yang et al. 2022). ...
... In this study, the authors have integrated images from optical and RADAR remote sensing sources to optimize the results and reduce the noise impacts of both types (Chawla et al. 2020). The combination is used to increase the detection sensitivity of water quality indicators (Gholizadeh et al. 2016;Abdelmalik 2018;Wang and Yang 2019;Said and Khan 2021). In addition, RADAR image data plays a supporting role in improving parameters and complementing optical image data to estimate water quality (Zhang et al. 2014;Klemas and Pieterse 2015;Liao and Wen 2020). ...
... Studies have also concluded that the spectral extractions from optical images detect high correlation in surface water pollution indices for TSS and COD but does not give a strong correlation result for BOD (Said and Khan 2021). To overcome this limitation, this paper integrated other remote sensing data (i.e., RADAR) to estimate BOD, COD, and TSS, and establish water pollution intensity maps of the city. ...
Rapid urbanization led to significant land-use changes and posed threats to surface water bodies worldwide, especially in the Global South. Hanoi, the capital city of Vietnam, has been facing chronic surface water pollution for more than a decade. Developing a methodology to better track and analyze pollutants using available technologies to manage the problem has been imperative. Advancement of machine learning and earth observation systems offers opportunities for tracking water quality indicators, especially the increasing pollutants in the surface water bodies. This study introduces machine learning with the cubist model (ML-CB), which combines optical and RADAR data, and a machine learning algorithm to estimate surface water pollutants including total suspended sediments (TSS), chemical oxygen demand (COD), and biological oxygen demand (BOD). The model was trained using optical (Sentinel-2A and Sentinel-1A) and RADAR satellite images. Results were compared with field survey data using regression models. Results show that the predictive estimates of pollutants based on ML-CB provide significant results. The study offers an alternative water quality monitoring method for managers and urban planners, which could be instrumental in protecting and sustaining the use of surface water resources in Hanoi and other cities of the Global South.
... These results follow research from Triaji et al. (2017), which states that in Indonesia, there are WQI values in rivers that are included in the medium class. Similar results were found elsewhere with a WQI range of 0-50, which is included in the good/excellent classification (Said and Khan, 2021;Soni and Thomas, 2013). In another analysis, the results of a different WQI classification with a score of 0-25 were included in the very bad/poor class (Akinbile and Omoniyi, 2018;Chen et al., 2022). ...
Rivers on Java Island are one of the water supply sources to meet the surrounding population's water needs. However, only large, high-priority rivers underwent a comprehensive water quality assessment. Rivers that are not a priority are rarely examined, such as sub-watersheds in Kuntulan, Rejoso. Upper Serayu, Gajahwong, and Glondong. The surrounding community utilizes these five watersheds for irrigation, industry, and domestic. Hence, analyzing the water quality index in the five watersheds during the dry season is necessary. The method used in this research is a comparison of the water quality results between the standards of the Indonesian government and WHO, as well as a comparison of the Water Quality Index (WQI) and Pollution Index (PIj). The method often used in Indonesia is PIj, while WQI is more global and hardly used. The difference in the two ways is expected to provide variations in the water quality index. The water quality parameters were pH, TDS, TSS, COD, PO4 3-, NO3-, total coliform, temperature, and EC. Comparing water quality with water quality standards in the five watersheds shows that several samples exceed the standard. WQI result shows that all river water in the five watersheds belongs to the excellent classification. A different result from the PIj index shows that the five watersheds were dominantly moderately polluted, with several samples considered polluted and extremely polluted. Differences in the index formula and water quality standards cause these different results. The results of the analysis show that the PIj index is more representative than the WQI as the PIj index shows the suitability of the classification comparison of water quality values with water quality standards compared to WQI. Keywords: pollution index river water quality young volcanic area To cite this article: Hendrayana, H., Riyanto, I.A. and Nuha, A. 2023. River water quality variability in the young volcanic areas
... Eq. 4 Where n = The total number of samples taken (24) = The noise levels in dB of the sample = fraction of total sample time of the sample t = Sampling time of sample (1hr) T = Total sampling time (24hrs) The average equivalent noise levels were recorded in the mine as 76.64 dB, in the cement site as 58.16 dB, and in the villages found 52.285 dB. ...
For emerging countries, mining has been a vital factor in employment, economic development, infrastructure, and supply of essential raw materials for Nation’s Gross domestic product (GDP) growth. The Limestone mine industry is serving as a viable route for economic transformation in India. Limestone exploration causes major damage to the environment at Yerraguntla industrial zone, YSR Kadapa district, Andhra Pradesh, India. The main objective of this study is to evaluate the environmental Pollution parameter that causes Air, Water, Noise, and Soil pollution in and around limestone quarries started in the early 1984. The present studyestimated Air Quality Index (AQI) as 76 based on the air quality sub-index approach using four pollutants (PM10, PM2.5, SO2, NOx) for a period of 24 hrs by taking one sample per hour during the post monsoon.Water Quality Index (WQI) obtained as 303.91 from fourteen physicochemical parameters (pH, EC, fluoride, Total alkalinity etc.) measured from water samples. Soil quality was determined using four physicochemical parameters (pH, EC, WHC, Calcium and Magnesium) from the soil samples collected from ten sampling stations. The obtained pH range was (7.6 to 9.4), EC of the soil was deter-mined as 4,140 μs/cm, the water retention capacity of the soil, ranges from (17.68 to 97.68) %, and the Calcium (Ca2+) and Magnesium (Mg2+) ranged from 74.5 to 272.75 mEq/L. Noise levels were determined as 76.64 dB in the mine’s, 58.16 dB in the cement industry, and 52.285 dB in the mine surrounding villages. This study can help mining sector management’s in develop-ing a sustainable Environmental Management frame work to meet the world sustainable development goals (SDGs).
... The BP consists of input layers, implicit layers and output layers, and contains two stages: forward propagation and back propagation of the error [52]. The error is back-propagated through the implicit layer to the output layer, and apportioned to all units in each layer, until the error is eventually decreased to an acceptable level after continual training [53]. ...
Timely monitoring of inland water quality using unmanned aerial vehicle (UAV) remote sensing is critical for water environmental conservation and management. In this study, two UAV flights were conducted (one in February and the other in December 2021) to acquire images of the Zhanghe River (China), and a total of 45 water samples were collected concurrently with the image acquisition. Machine learning (ML) methods comprising Multiple Linear Regression, the Least Absolute Shrinkage and Selection Operator, a Backpropagation Neural Network (BP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were applied to retrieve four water quality parameters: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphors (TP), and permanganate index (CODMn). Then, ML models based on the stacking approach were developed. Results show that stacked ML models could achieve higher accuracy than a single ML model; the optimal methods for Chl-a, TN, TP, and CODMn were RF-XGB, BP-RF, RF, and BP-RF, respectively. For the testing dataset, the R2 values of the best inversion models for Chl-a, TN, TP, and CODMn were 0.504, 0.839, 0.432, and 0.272, the root mean square errors were 1.770 μg L−1, 0.189 mg L−1, 0.053 mg L−1, and 0.767 mg L−1, and the mean absolute errors were 1.272 μg L−1, 0.632 mg L−1, 0.045 mg L−1, and 0.674 mg L−1, respectively. This study demonstrated the great potential of combined UAV remote sensing and stacked ML algorithms for water quality monitoring.
... Many different ML systems are available nowadays; one can choose the best based on the output required and the available data. It is observed that, within inland water remote sensing, ML algorithms such as artificial neural networks (ANN) (Liu et al. 2015;Said and Khan 2021;Sharaf El Din et al. 2017;Teodoro et al. 2007), genetic algorithms/programming (GA/GP) (Chang et al. 2012;Lounis et al. 2013;Swain & Sahoo, 2017b), support vector machines (SVM) Xili Wang et al. 2010), random forest (RF)/boosted regression trees (Hafeez et al. 2019;Rubin et al. 2021), and CNN for wetland water area (Günen 2022) have shown promise in accurately estimating WQPs across a variety of spatio-temporal scales. ...
Understanding the dynamics of water quality in any water body is vital for the sustainability of our water resources. Thus, investigating spatio-temporal changes of dominant water quality parameters (WQPs) in any study is indeed critical for proposing the appropriate treatment for the water bodies. Traditionally, concentrations of WQPs have been measured through intensive fieldwork. Additionally, many studies have attempted to retrieve concentrations of WQPs from satellite images using regression-based methods. However, the relationship between WQPs and satellite data is complex to be modeled accurately by using simple regression-based methods. Our study attempts to develop a machine learning model for mapping the concentrations of dominant optical and non-optical WQPs such as electrical conductivity (EC), pH, temperature (Temp), total dissolved solids (TDS), silicon dioxide (SiO2), and dissolved oxygen (DO). In this context, a remote sensing framework based on the extreme gradient boosting (XGBoost) and multi-layer perceptron (MLP) regressor with optimized hyper parameters (HPs) to quantify concentrations of different WQPs from the Landsat-8 satellite imagery is developed. We evaluated six years of satellite data stretching spatially from upstream to downstream Ankinghat to Chopan (20 stations under Central Water Commission (CWC), Middle Ganga Basin) for characterizing the trends of dominant physico-chemical WQPs across the four clusters identified in our previous study. Through the developed XGBoost and MLP regression models between measured WQPs and the reflectance of the pixels corresponding to the sampling stations, a significant coefficient of determination (R²) in the range of 0.88–0.98 for XGBoost and 0.72–0.97 for MLP were generated, with bands B1–B4 and their ratios more consistent. Indeed, these findings indicate that from a small number of in-situ measurements, we can develop reliable models to estimate the spatio-temporal variations of physico-chemical and biological WQPs. Therefore, models generated from Landsat-8 could facilitate the environmental, economic, and social management of any waterbody.
This study objects to evaluate the Water Quality Indices (WQIs) of the Tigris River in Wasit, Iraq, using the Arithmetic Weighted Water Quality Index (AW-WQI), Canadian Water Quality Index (CCME-WQI), Heavy Metal Pollution Index (HPI-WQI), National Sanitation Foundation Index (NSF-WQI), and Overall Index of Pollution (OIP-WQI). Twelve water samples were collected at different locations in the study area during the winter and spring of 2024. Each index evaluates the water quality in the study area based on speciϐic criteria. In separate periods (winter and spring seasons of 2024), we categorized the water quality in the research region according to each indication: AW-WQI (70.517-102.611), CCME-WQI (39.763-47.1404), HPI-WQI (82.526-118.846), NSF-WQI (54.66-60.12), and OIP-WQI (1.9769-2.4686). We have created twenty-six combinations of spectral reϐlectance bands, reϐlectance values of seven bands, band ratios for the ϐirst ϐive bands, and nine spectral indices. This study showed a signiϐicant correlation between the spectral reϐlectance data of Landsat-9 OLI-2 bands and the WQIs using Pearson correlation and multiple linear regression (MLR) model equations. We evaluated the performance of the MLR model for the WQIs across different seasons. The AW-WQI model showed a coefϐicient of determination R 2 of 84% in winter and 98% in spring. At the same time, the CCME-WQI recorded R 2 of 97% in winter and 75% in spring. The HPI-WQI received R 2 of 93% and 98% in spring. The NSF-WQIs received R 2 of 62% and 98% in spring. Finally, the OIP-WQI received R 2 of 92% and 99% in spring. These results highlight the seasonal variation in the predictive accuracy of the WQI models, with some minor differences between the experimental results and those obtained through remote sensing techniques. The WQIs showed that the water needed to be more suitable for consumption due to elevated levels beyond the permissible limit in most study area locations. Multiple sources of pollution in the region discharge hazardous waste into the river, causing WQIs to exceed permissible limits in most study areas.
