Assessment of correlation amongst physico-chemical, topographical, geological, lithological and soil type parameters for measuring water quality of Rawal watershed using remote sensing

Ahmed, Mehreen; Mumtaz, Rafia; Baig, Shahbaz; Zaidi, Syed Muhammad Hassan

doi:10.2166/ws.2022.006

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…However, remote sensing alone does not have the capability to assess the water quality with precise and accurate results. Thus, modern techniques involving the combination of remote sensing and AI for accurate and timely water quality forecasts can be a more useful approach [13,14].…”

Section: Introductionmentioning

confidence: 99%

A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing

Ahmed

Mumtaz

Anwar

et al. 2022

Water

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Water is a fundamental resource for human survival but the consumption of water that is unfit for drinking leads to serious diseases. Access to high–resolution satellite imagery provides an opportunity for innovation in the techniques used for water quality monitoring. With remote sensing, water quality parameter concentrations can be estimated based on the band combinations of the satellite images. In this study, a hybrid remote sensing and deep learning approach for forecasting multi–step parameter concentrations was investigated for the advancement of the traditionally employed water quality assessment techniques. Deep learning models, including a convolutional neural network (CNN), fully connected network (FCN), recurrent neural network (RNN), multi–layer perceptron (MLP), and long short term memory (LSTM), were evaluated for multi–step estimations of an optically active parameter, i.e., electric conductivity (EC), and an inactive parameter, i.e., dissolved oxygen (DO). The estimation of EC and DO concentrations can aid in the analysis of the levels of impurities and oxygen in water. The proposed solution will provide information on the necessary changes needed in water management techniques for the betterment of society. EC and DO parameters were taken as independent variables with dependent parameters, i.e., pH, turbidity, total dissolved solids, chlorophyll–α, Secchi disk depth, and land surface temperature, which were extracted from Landsat–8 data from the years 2014–2021 for the Rawal stream network. The bi–directional LSTM obtained better results with a root mean square error (RMSE) of 0.2 (mg/L) for DO and an RMSE of 281.741 (μS/cm) for EC, respectively. The results suggest that a hybrid approach provides efficient and accurate results in feature extraction and evaluation of multi–step forecast of both optically active and inactive water quality parameters.

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Section: Introductionmentioning

confidence: 99%

A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing

Ahmed

Mumtaz

Anwar

et al. 2022

Water

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“…The results were analyzed based on several different statistical procedures applied in similar researches: descriptive statistical analysis (Pantelić et al 2012), ANOVA analysis (Deng et al 2022), Pearson correlations (Kharake, Raut 2021;Ahmed et al 2022) and PCA analyses (Maji, Chaudhary 2019). The post-hoc Scheffe's test was used to determine statistically significant differences between certain groups of data (Obradović et al 2020).…”

Section: Methodsmentioning

confidence: 99%

Water quality evaluation of the Danube River basin in Bačka (northern Serbia) using multivariate statistical techniques

Milentijević,

Pantelić,

Obradović

et al. 2024

Geografie

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The study aimed to analyze changes in water quality based on ten physicochemical parameters for the Danube River in Bačka. Data from three profiles over a twenty-year interval were used and processed using multivariate statistics. An ANOVA indicated that the quality of surface water is mostly at a satisfactory level. Sufficient amounts of dissolved oxygen indicate a favorable aeration regime. The surpassing of the annual and monthly values of suspended solids was recorded for all profiles (especially in summer). The summary results show that most parameters, except for suspended solids, are within the allowed values for water quality Class II. A Pearson’s correlation test indicates a moderate to weak correlation of the analyzed parameters. A PCA analysis determined the influence of (a) eutrophication processes; (b) ecological factors; and (c) concentrated sources of pollution. Changes in the Danube’s water quality, primarily increased concentrations of suspended solids, can exert a limiting influence on some economic activities and their planning.

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“…The dynamic nature of the water body can cause certain changes in the physico-chemical properties [30]. Moreover, the influence of air pollutants, meteorological features and hydrological features on the aquatic ecosystems is ignored in the development of the WQI method [31]. These challenges indicate that most WQIs fail to accurately classify a water body.…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Water Quality Index for Water Quality Monitoring Using Remote Sensing and Machine Learning

2022

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Water quality deterioration is a serious problem with the increase in the urbanization rate. However, water quality monitoring uses grab sampling of physico-chemical parameters and a water quality index method to assess water quality. Both processes are lengthy and expensive. These traditional indices are biased towards the physico-chemical parameters because samples are only collected from certain sampling points. These limitations make the current water quality index method unsuitable for any water body in the world. Thus, we develop an enhanced water quality index method based on a semi-supervised machine learning technique to determine water quality. This method follows five steps: (i) parameter selection, (ii) sub-index calculation, (iii) weight assignment, (iv) aggregation of sub-indices and (v) classification. Physico-chemical, air, meteorological and hydrological, topographical parameters are acquired for the stream network of the Rawal watershed. Min-max normalization is used to obtain sub-indices, and weights are assigned with tree-based techniques, i.e., LightGBM, Random Forest, CatBoost, AdaBoost and XGBoost. As a result, the proposed technique removes the uncertainties in the traditional indexing with a 100% classification rate, removing the necessity of including all parameters for classification. Electric conductivity, secchi disk depth, dissolved oxygen, lithology and geology are amongst the high weighting parameters of using LightGBM and CatBoost with 99.1% and 99.3% accuracy, respectively. In fact, seasonal variations are observed for the classified stream network with a shift from 55:45% (January) to 10:90% (December) ratio for the medium to bad class. This verifies the validity of the proposed method that will contribute to water management planning globally.

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Assessment of correlation amongst physico-chemical, topographical, geological, lithological and soil type parameters for measuring water quality of Rawal watershed using remote sensing

Cited by 9 publications

References 24 publications

A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing

A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing

Water quality evaluation of the Danube River basin in Bačka (northern Serbia) using multivariate statistical techniques

An Enhanced Water Quality Index for Water Quality Monitoring Using Remote Sensing and Machine Learning

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