nach oben

Erschienen in:

2023 | OriginalPaper | Buchkapitel

17. A Statistical Approach to the Prediction of Fluoride in River Water Using the Best Subset Method

verfasst von : Madhusudana Rao Chintalacheruvu, Prakhar Modi

Erschienen in: Advanced Treatment Technologies for Fluoride Removal in Water

Verlag: Springer Nature Switzerland

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Water quality parameters were predicted by many methods in the past, but the application of the best subset method is yet to be explored much. This method is unique in its application as it adopts the statistical regression modeling procedure to fit a separate model for every combination of any number of predictors while selecting the best subset for making the optimal decision. The present chapter demonstrates the application of the best subset model for the prediction of fluoride in river water by developing the regression equations for various combinations of other physicochemical parameters of the water quality of the Godavari River, the second largest river in India. The desired fluoride concentrations were evaluated using the concentrations of 16 other water quality parameters data collected for a period of 13 years (1993–2005) from 13 monitoring sites during the Monsoon (June-October) and Post-Monsoon (November-February) periods in the study region. Several best subsets of independent variables are chosen based on the percentage variation in the dependent variable, with a gradual increase in the number of predictors using various combinations of water quality parameters, and the best models are selected in agreement with the highest R² value and the lowest RSS value. The final best subset regression model is selected among the various best models using F-value statistics criteria. The best subset regression model is successful in explaining 90% variation in fluoride concentrations for both seasons in the study area. It would be interesting to try this method for the study of both surface water and groundwater quality parameters.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Performance Analysis of Passive Solar Still for De-fluoridation of Water

Nächstes Kapitel Environmental and Health Effects of Fluoride Contamination and Treatment of Wastewater Using Various Technologies

Abdelmalik, K. W. (2016). Role of statistical remote sensing for Inland water quality parameters prediction. The Egyptian Journal of RS and Space Sciences, 21,193–200.

Abyaneh, H. M. (2014). Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters. Journal of Environmental Health Science & Engineering.

Adimalla, N. (2018). Elevated fluoride concentration levels in rural villages of Siddipet Telangana State South India. Data in Brief, 16, 693–769.CrossRef

Adimalla, N., & Li, P. (2018). Occurrence, health risks, and geochemical mechanisms of fluoride and nitrate in groundwater of the rock-dominant semi-arid region, Telangana State, India. Human and Ecological Risk Assessment: An International Journal, 24(8), 2143–2161.

Adimalla, N., & Rajitha, S. (2018). Spatial distribution and seasonal variation in fluoride enrichment in groundwater and its associated human health risk assessment in Telangana State, South India. Human and Ecological Risk Assessment: An International Journal, 24(8), 2119–2132.CrossRef

Adimalla, N., Li, P., & Qian, H. (2018c). Evaluation of groundwater contamination for fluoride and nitrate in semi-arid region of Nirmal Province, South India: A special emphasis on human health risk assessment (HHRA). Human and Ecological Risk Assessment: An International Journal, 24(7), 1949–1971.

Adimalla, N., Li, P., & Venkatayogi, S. (2018b). Hydrogeochemical evaluation of groundwater quality for drinking and irrigation purposes and integrated interpretation with water quality index studies. Environmental Processes, 5(4), 363–383.CrossRef

Adimalla, N., Vasa, S. K., & Li, P. (2018a). Evaluation of groundwater quality, Peddavagu in Central Telangana (PCT), South India: An insight of controlling factors of fluoride enrichment model. Earth Systems and Environment, 4(3), 841–852.CrossRef

Adimalla, N., Venkatayogi, S., & Das, S. V. G. (2019). Assessment of fluoride contamination and distribution: A case study from a rural part of Andhra Pradesh, India. Applied Water Science, 9(94), 1–10. https://doi.org/10.1007/s13201-019-1023-4CrossRef

Akoteyon, I. S., Akintuyi, A. O., & Mbata, U. A. (2013). Regression model for predicting water quality parameters: Study of Igando, Lagos-Nigeria. American Academic & Scholarly Research Journal, 5(4).

Ali, S., Thakur, S. K., Sarkar, A., & Shekhar, S. (2016). Worldwide contamination of water by fluoride. Environmental Chemistry Letters, 14(3), 291–315.CrossRef

Allam, E., Gangula, H. K., Arukonda, R., & Muralidhar, M. (2020). Groundwater quality with special reference to fluoride concentration in the granitic and basaltic contact zone of Southern India. Data in Brief, 33, 106462.CrossRefPubMedPubMedCentral

Amini, M., Mueller, K., Abbaspour, K. C., Rosenberg, T., Afyuni, M., Moller, K. N., Sarr, M., & Johnson, C. A. (2008). Statistical modeling of global geogenic fluoride contamination in groundwaters. Environmental Science & Technology, 42, 3662–3668.ADSCrossRef

Barzegar, R., Moghaddam, A. A., Adamowski, J., & Fijani, E. (2016). Comparison of machine learning models for predicting fluoride contamination in groundwater. Stochastic Environmental Research and Risk Assessment, 30, 1207–1225.

Bhatia, K. K. S., Tyagi, A., & Sharma, M. K. (1997). Groundwater conductivity modelling using best subset procedure. In Proceeding of International Symposium on Emerging Trends in Hydrology (pp. 41–53), Department of Hydrology, University of Roorkee, Roorkee, 25–27.

Biswas, J. K., Bera, B., Chanda, R., Sarkar, S. K., Majumdar, J., Majumdar, S., & Hazra, S. (2017) Nutrient Modelling of an Urban Lake using Best Subset Method. Int. J. of Environ. Sci. Technology.

Central Water Commission (2016) Godavari Basin Water Quality Year Book, Volume III

Central Water Commission (2021) Reports on Water Quality Scenario of Rivers, Volume-1

Chintalacheruvu, M. R., & Modi, P. (2023). Application of best subset method for river water quality modeling: A study on Godavari River, India. Environmental Quality Management, 33(1), 393–409.

Das, B., Talukdar, J., Sarma, S., Gohain, B., Dutta, R. K., Das, H. B., & Das, S. C. (2003). Fluoride and other inorganic constituents in groundwater of Guwahati, Assam, India. Current Science, 85, 657–661.

Davne MP, Pradhan V (2021) The analytical study of heavy metals present in water samples of Godavari River. International Journal of Scientific Development and Research, 6(11), 138–141. https://doi.org/10.31695/IJSDR.2021.34824

Draper, N. R., & Smith, H. (1981). Applied Regression Analysis (2nd ed.). John Wiley and Sons.

Gbadebo, A. M. (2012). Groundwater fluoride and dental fluorosis in southwestern Nigeria. Environmental Geochemistry and Health, 34(6), 597–604.CrossRefPubMed

Gedefaw, M., Hao, W., Denghua, Y., & Girma, A. (2018). Variable selection methods for water demand forecasting in Ethiopia: Case study Gondar town. Cogent Environmental Science, 4(1), 1537067.CrossRef

Gizaw, B. (1996). The origin of high bicarbonate and fluoride concentrations in waters of the main Ethiopian Rift Valley. Journal of African Earth Sciences, 22(4), 391–402. https://doi.org/10.1016/S0899-5362(96)00097-6ADSCrossRef

Gupta, M. K., Singh, V., Rajwanshi, P., Agarwal, M., Rai, K., Srivastava, S., Shrivastav, R., & Dass, S. (1999). Groundwater quality assessment of Tehsil Kheragarh Agra (India) with special reference to fluoride. Environmental Monitoring and Assessment, 59, 272–285.CrossRef

Gupta, S. K., Deshpande, R. D., Agarwal, M., & Raval, B. R. (2005). Origin of high fluoride in groundwater in the North Gujarat-Cambay region India. Hydrogeology Journal, 13, 596–605.CrossRef

Haque, M. M., Rahman, A., Hagare, D., & Chowdhury, R. K. (2018). A comparative assessment of variable selection methods in urban water demand forecasting. Water, 10, 419.

Heumann, C., Schomaker, M., & Shalabh (2016). Introduction to statistics and data analysis. Springer International Publishing Switzerland.

Hirtle, H., & Rencz, A. (2003). The relation between spectral reflectance and dissolved organic carbon in lake water: Kejimkujik National Park, Nova Scotia, Canada. International Journal of Remote Sensing, 24(5), 953–967.

Hocking, R. R. (1976). The analysis and selection of variables in linear regression. Biometrics, 32, 1–49.MathSciNetCrossRef

India Meteorological Department (IMD). (2021). Annual Report.

Jacks, G., Bhattacharya, P., Chaudhary, V., & Singh, K. (2005). Controls on the genesis of some high-fluoride groundwaters in India. Applied Geochemistry, 20(2), 221–228.ADSCrossRef

Kim, K., & Jeong, Y. (2005). Factors influencing natural occurrence of fluoride-rich ground waters: A case study in the southeastern part of the Korean Peninsula. Chemosphere, 58(10), 1399–1408.ADSCrossRefPubMed

Kundu, M. C., & Mandal, B. (2009a). Agricultural activities influence nitrate and fluoride contamination in drinking groundwater of an intensively cultivated district in India. Water, Air, and Soil Pollution, 198, 243–252.ADSCrossRef

Kundu, M. C., & Mandal, B. (2009b). Assessment of potential hazards of fluoride contamination in drinking groundwater of an intensively cultivated district in West Bengal, India. Environmental Monitoring and Assessment, 152, 97–103.CrossRefPubMed

Li, P., He, X., Li, Y., et al. (2019). Occurrence and health implication of fluoride in groundwater of loess aquifer in the Chinese loess plateau: A case study of Tongchuan, Northwest China. Exposure and Health, 11, 95–107.

Magesh, N. S., Chandrasekar, N., & Elango, L. (2016). Occurrence and distribution of fluoride in the groundwater of the Tamiraparani River basin, South India: A geostatistical modeling approach. Environmental Earth Sciences, 75, 1483.ADSCrossRef

Misra, A. K., & Mishra, A. (2006). Study of quaternary aquifers in Ganga Plain, India: Focus on groundwater salinity, fluoride and fluorosis. Journal of Hazardous Materials, 144, 438–448.ADSCrossRefPubMed

Monteiro, M., & Costa, M. (2018). A time series model comparison for monitoring and forecasting water quality variables. Hydrology, 5, 37.CrossRef

Mukherjee, I., & Singh, U. K. (2018). Groundwater fluoride contamination, probable release, and containment mechanisms: A review on Indian context. Environmental Geochemistry and Health, 40(6), 2259–2301.CrossRefPubMed

Mulla, J. G., Farooqui, M., & Zaheer, A. (2007). A correlation and regression equations among water quality parameters. International Journal of Chemical Sciences, 5(2), 943–952.

Murray, J. J., & World Health Organization. (1986). Appropriate use of fluorides for human health. World Health Organization.

Nafouanti, M. B., Li, J., Mustapha, N. A., Uwamungu, P., & AL-Alimi, D. (2021). Prediction on the fluoride contamination in groundwater at the Datong Basin, Northern China: Comparison of random forest, logistic regression and artificial neural network. Applied Geochemistry, 132, 105054.

Najafzadeh, M., Ghaemi, A., & Emamgholizadeh, S. (2018). Prediction of water quality parameters using evolutionary computing-based formulations. International Journal of Environmental Science and Technology, 16, 6377–6396.CrossRef

Podgorski, J. E., Labhasetwar, P., Saha, D., & Berg, M. (2018). Prediction modeling and mapping of groundwater fluoride contamination throughout India. Environmental Science & Technology, 52, 9889–9898. https://doi.org/10.1021/acs.est.8b01404ADSCrossRef

Rao, N. S., Rao, P. S., Dinakar, A., Rao, P. V. N., & Marghade, D. (2017). Fluoride occurrence in the groundwater in a coastal region of Andhra Pradesh, India. Applied Water Science, 7(4), 1467–1478. https://doi.org/10.1007/s13201-016-0511-yADSCrossRef

Reddy, S. K. K., Sahadevan, D. K., Gupta, H., & Reddy, D. V. (2019). GIS-based prediction of groundwater fluoride contamination zones in Telangana, India. Journal of Earth System Science, 128(5), 132. https://doi.org/10.1007/s12040-019-1167-8ADSCrossRef

Sadat, N. (2012). Study of fluoride concentration in the river (Godavari) and groundwater of Nanded City. International Journal of Engineering Inventions, 1(1), 11–15.

Satish, N., Jagadeesh, A., Rajitha, K., & Varma, R. R. M. (2022). Prediction of stream water quality in Godavari River Basin, India using statistical and artificial neural network models. H2Open Journal, 1(1), 1–14. https://doi.org/10.2166/h2oj.2021.066

Sattari, M. T., Joudi, A. R., & Kusiak, A. (2016). Estimation of water quality parameters with data-driven model. Journal‐American Water Works Association.

Saxena, V., & Ahmed, S. (2003). Inferring the chemical parameters for the dissolution of fluoride in groundwater. Environmental Geology, 43(6), 731–736.CrossRef

Shirin, S., Jamal, A., Emmanouil, C., Singh, V. P., & Yadav, A. K. (2022). Assessment and characterization of waste material used as backfilling in an abandoned mine. International Journal of Coal Preparation and Utilization, 1–9. https://doi.org/10.1080/19392699.2022.2118259

Shirin, S., Jamal, A., Emmanouil, C., & Yadav, A. K. (2021). Assessment of characteristics of acid mine drainage treated with fly ash. Applied Sciences, 11, 3910. https://doi.org/10.3390/app11093910CrossRef

Shirin, S., & Yadav, A. K. (2014). Physico chemical analysis of municipal wastewater discharge in ganga river, Haridwar district of Uttarakhand, India. Current World Environment, 9, 536–543. https://doi.org/10.12944/CWE.9.2.39CrossRef

Singh, G., Kumari, B., Sinam, G., Kriti, N. K., & Mallick, S. (2018). Fluoride distribution and contamination in the water, soil and plants continuum and its remedial technologies, an Indian perspective: A review. Environmental Pollution, 239, 95–108.CrossRefPubMed

Singanan, M., Rao, K. S., & Rambabu, C. (1995). A correlation study on Physico-chemical characteristics of groundwater in Rameswaram Island. Indian Journal of Environmental Protection, 15(3), 212–217.

Solunke, K. R. (2021). Study of fluoride concentration in the Godavari River near Shahagad. Journal of Emerging Technologies and Innovative Research, 8(12), 111–116.

Subba, R. N. (2011). High-fluoride groundwater. Environmental Monitoring and Assessment, 176, 637–645.CrossRef

Subba, R. N., Vidyasagar, G., Surya, R. P., & Bhanumurthy, P. (2015). Assessment of hydrogeochemical processes in a coastal region: Application of multivariate statistical model. Journal of the Geological Society of India, 86(1), 89–96.

Susheela, A. K. (1999). Fluorosis management programme in India. Current Science, 77, 1250–1256.

Vikas, C., Kushwaha, R. K., & Pandit, M. K. (2009). Hydrochemical status of groundwater in district Ajmer (NW India) with reference to fluoride distribution. Journal of the Geological Society of India, 73, 773–784.

Vithanage, M., & Bhattacharya, P. (2015). Fluoride in drinking water: Health effects and remediation. CO₂ Sequestration, Biofuels and Depollution, 105–151.

Weisberg, S. (1980). Applied linear regression. Wiley and Sons.

World Health Organization. (2017). Guidelines for drinking water quality: First addendum to the fourth edition.

Yadav, M. K. (2021). Seasonal variation in water quality of Krishna river. CWC Reports on Water Quality Scenario of Rivers.

Titel: A Statistical Approach to the Prediction of Fluoride in River Water Using the Best Subset Method
verfasst von: Madhusudana Rao Chintalacheruvu
Prakhar Modi
Verlag: Springer Nature Switzerland
Buch: Advanced Treatment Technologies for Fluoride Removal in Water
Print ISBN: 978-3-031-38844-6

Electronic ISBN: 978-3-031-38845-3

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-38845-3_17