Principal component analysis incorporated water quality index modeling for Dhaka-based rivers

Nath Roy, Bimol; Roy, Hridoy; Rahman, Kazi Saidur; Mahmud, Foysal; Bhuiyan, Md Mahmud Kamal; Hasan, Mobassarul; Bhuiyan, Al-Amin Kabir; Hasan, Mahmudul; Mahbub, Mallick Syed; Jahedi, Rezaul Maksud; Islam, Md Shahinoor

doi:10.1016/j.cacint.2024.100150

Cited by 3 publications

(1 citation statement)

References 75 publications

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“…The definition of WQI is a numerical expression of how each water quality parameter affects the overall quality of the water that is suitable for human consumption (Varol et al, 2021;Antonio et al, 2024). Very few studies to date have attempted to investigate the intricate relationship between several (Okimiji et al, 2024;Aldrees et al, 2024;Nath Roy et al, 2024;Prabagar et al, 2023). WQI is a crucial metric for evaluating groundwater quality in both urban and rural regions.…”

Section: Contamination Classification Microbiological Parameters Have...mentioning

confidence: 99%

Water Sanitation Quality Assessment for Hand-Dug Wells

Sari,

Anggoro,

Sunoko

et al. 2024

RGSA

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Purpose: Public toilet sanitation facilities need to be analyzed for water sanitation quality. This research aims to explain the benefits of hand-well water for sanitation in densely populated communal environments. Theoretical framework: This article presents literature support regarding sanitation, water quality, and pollutant index in Indonesia, regarding water sanitation conditions in dense urban organizations. Method: Comprehensive data on the quality of water sanitation used by people in dense settlements. Data was collected in the form of water as a laboratory test from 24 random dug wells in four sub-districts in Bandung City and analyzed by the regulations of the Minister of Health of the Republic of Indonesia. Results and Discussion: The research results show that an average of 75% of air wells are contaminated based on water sanitation quality and pollutant index based on Minister of Health Regulation Number 32 of 2017 and Number 2 of 2023 concerning Indonesian Environmental Health and Water Health Quality Standards. Research Implications: Our findings are important for policymakers, health workers, public health practitioners, and so that the community provides public toilet facilities that can be monitored and receive government attention to improve their quality. Originality/Value: The novelty of this research lies in that it presents a comprehensive assessment of water quality indices and physical, chemical, and microbiological parameters of hand wells in dense urban organizations.

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Section: Contamination Classification Microbiological Parameters Have...mentioning

confidence: 99%

Water Sanitation Quality Assessment for Hand-Dug Wells

Sari,

Anggoro,

Sunoko

et al. 2024

RGSA

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Enhanced Assessment of Water Quality and Pollutant Source Apportionment Using APCS-MLR and PMF Models in the Upper Reaches of the Tarim River

Zhang,

Wang,

et al. 2024

Water

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To evaluate the pollution sources and dynamics of the upper reaches of the Tarim River, 10 typical sampling points were selected, and 23 water quality parameters from 2020 to 2022 were analyzed using one-way analysis of variance, the comprehensive Water Quality Identification Index (WQI), and Principal Component Analysis (PCA). The pollution status, sources, and contribution rates of water quality were investigated using the Absolute Principal Component-Multiple Linear Regression Model (APCS-MLR) and Positive Matrix Factorization (PMF). The results indicated that the water quality parameters of dissolved oxygen (DO), chemical oxygen demand (CODMn), biochemical oxygen demand after 5 days (BOD5), total nitrogen (TN), total phosphorus (TP), fluoride ions (F-), and ammonia-nitrogen (NH3-N) in the upper reaches of the Tarim River exceed standards, with noticeable spatial variations observed for each parameter. The water quality evaluation grades in the upper reaches of Tarim River primarily indicate “moderate” and “good” levels, with DO, TN, NH3-N, and electrical conductivity (EC) being the key parameters influencing variations in water quality. The source analysis results from APCS-MLR and PMF yielded similar outcomes, identifying six potential pollution sources. Among these, soil weathering, livestock and poultry breeding, and agricultural activities exhibited higher contribution rates. Specifically, the contribution rates for these sources according to APCS-MLR were 44.11%, 19.63%, and 11.67%, respectively; while according to PMF they are 24.08%, 17.88%, and 27.54%, respectively. Furthermore, industrial pollution sources contributed at a rate of 6.01% according to APCS-MLR, while urban living sources contributed at a rate of 2.13%. However, based on PMF analysis, the contribution rates for industrial pollution sources increased significantly to 16.71%. Additionally, APCS-MLR identified natural sources as contributing at a rate of 16.45%, whereas PMF suggested that a combination of agricultural activities and natural sources contributed at a lower rate of only 9.52%. In conclusion, the water quality within the upper reaches of the Tarim River is predominantly satisfactory. Nonetheless, localized pollution, primarily attributable to human activities, presents a substantial challenge. These observations provide critical insights into improving and protecting the fragile water quality of the Tarim River.

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Prediction of Floor Failure Depth in Coal Mines: A Case Study of Xutuan Mine, China

Feng,

Bi,

2024

Water

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Accurately predicting the failure depth of coal seam floors is crucial for preventing water damage, ensuring the safe and efficient mining of coal seams, and protecting the ecological environment of mining areas. In order to improve the prediction accuracy of the coal seam floor failure depth, an improved support vector regression (SVR) model is proposed to predict the floor failure depth by taking the 3234 working face in Xutuan Mine as an example. This improved model incorporates principal component analysis (PCA) and slime mould algorithm (SMA) optimization techniques. First, based on the measured data of seam floor failure depth in several mining areas, a prediction index system of floor failure depth was constructed. Subsequently, the PCA method was used to reduce the dimension of the measured data of the coal seam floor failure depth, and the input structure of the SVR model was optimized. Then, the SMA was used to optimize the key parameters, namely the penalty factor (C) and kernel function parameter (g), in the SVR model, achieving automatic parameter selection and obtaining the optimal parameter combination. This process led to the establishment of a coal seam floor failure depth prediction model based on PCA-SMA-SVR. The predictive performance of the PCA-SMA-SVR model, SMA-SVR model, and SVR model was quantitatively evaluated and compared using four quantitative indicators, and the results showed that the PCA-SMA-SVR model had the smallest MAE, RMSE, MRE, and TIC values, which were 1.0470 m, 1.2928 m, 0.0628, and 0.0374, respectively. Finally, the PCA-SMA-SVR model was used to predict that the floor failure depth of the 3234 working face in Xutun Mine was 17.09 m, and the predicted result was compared and analyzed with the results of four commonly used empirical formulas (16.03–21.74 m). The results show that the model is close to the results of four commonly used empirical formulas, indicating that the model has high predictive performance and good practicality. This study is of great significance for the safety, green mining, and ecological environment protection of coal mines.

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Principal component analysis incorporated water quality index modeling for Dhaka-based rivers

Cited by 3 publications

References 75 publications

Water Sanitation Quality Assessment for Hand-Dug Wells

Water Sanitation Quality Assessment for Hand-Dug Wells

Enhanced Assessment of Water Quality and Pollutant Source Apportionment Using APCS-MLR and PMF Models in the Upper Reaches of the Tarim River

Prediction of Floor Failure Depth in Coal Mines: A Case Study of Xutuan Mine, China

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