Artificial intelligence methods in water systems research : a literature review

• Autorzy: Piotrowska Julia , Dąbrowska Dominika

Identyfikatory

DOI

10.7306/gq.1747

• Języki publikacji: EN

Abstrakty

EN

We overview selected artificial intelligence methods used in research on water systems, specifically artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), genetic programming (GP) and support vector machine (SVM) methods. Each method is characterized and the most effective ways of using these methods are discussed. These methods prove widely useful in forecasting changes in selected surface and groundwater quality parameters, forecasting sewage network failures, assessing water treatment options, climate monitoring, drought detection and environmental issues for farmers and producers. Published studies show that artificial intelligence methods should be used in the analysis of water systems, especially since artificial intelligence now appears in search results for over 60,000 environmental articles.

Słowa kluczowe

EN

hydrogeology; machine learning; urban water systems; artificial intelligence; sensors; analysis

• Wydawca: Państwowy Instytut Geologiczny - Państwowy Instytut Badawczy
• Czasopismo: Geological Quarterly
• Rocznik: 2024
• Tom: Vol. 68, No. 2
• Strony: art. no. 19
• Opis fizyczny: Bibliogr. 71 poz., rys., tab.

Twórcy

autor

Piotrowska Julia

• University of Silesia, Faculty of Natural Sciences, Będzińska 60, 41-200 Sosnowiec, Poland

• https://orcid.org/0009-0004-1169-5853

autor

Dąbrowska Dominika

• University of Silesia, Faculty of Natural Sciences, Będzińska 60, 41-200 Sosnowiec, Poland

• https://orcid.org/0000-0002-6762-8885

Bibliografia

• 1. Abba, S., Hadi, S.J., Abdullahi, J., 2017. River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques. Procedia Computer Science, l20: 75-82; https://doi.org/10.1016/j.procs.2017.11.212
• 2. Abrahart, R.J., Anctil, F., Coulibaly, P., Dawson, C.W., Mount, N.J., See, L.M., Wilby, R.L., 2012. Two decades of anarchy? Emerging themes and outstanding challenges for Neural Network River Forecasting. Progress in Physical Geography: Earth and Environment, 36: 480-513; https://doi.org/10.1177/0309133312444943
• 3. Al-Dousari, A.E., Mishra, A., Singh, S., 2023. Land use land cover change detection and urban sprawl prediction for Kuwait Metropolitan Region, using multi-layer Perceptron Neural Networks (MLPNN). The Egyptian Journal of Remote Sensing and Space Science, 26: 381-392; https://doi.org/10.1016Zj.ejrs.2023.05.003
• 4. Alaa, M., Zaidan, A.A., Zaidan, B.B., Talal, M., Kiah, M., 2017. A review of smart home applications based on internet of things. Journal of Network and Computer Applications, 97: 48-65; https://doi.org/10.1016/jjnca.2017.08.017
• 5. Antrop, M., 2004. Landscape change and the urbanization process in Europe. Landscape and Urban Planning, 67: 9-26; https://doi.org/10.1016/s0169-2046(03)00026-4
• 6. Azad, A., Karami, H., Farzin, S., Mousavi, S.-F., Kisi, O., 2019. Modeling river waterquality parameters using modified adaptive Neuro Fuzzy Inference System. Water Science and Engineering, 12: 45-54; https://doi.org/10.1016/j.wse.2018.11.001
• 7. Bansal, M., Malik, S., Kumar, M., Meena, N., 2020. Arduino based smart walking cane for visually impaired people. 2020 Fourth International Conference on Inventive Systems and Control (ICISC), 462-465; https://doi.org/10.1109/ICISC47916.2020.9171209
• 8. Bates, M., 2014. Managing Landfill Sites Fires in Northampton. Report, Environment and Transport Scrutiny Committee. Northamptonshire, County Council, Northampton, Northamptonshire, 138.
• 9. Batty, M., 2018. Artificial intelligence and smart cities. Environment and Planning B: Urban Analytics and City Science, 45: 3-6; https://doi.org/10.1177/2399808317751169
• 10. Bibri, S., Alexandre, A., Sharifi, A., Krogstie, J., 2023. Environmentally sustainable smart cities and their converging AI, loT, and big data technologies and solutions: an integrated approach to an extensive literature review. Energy Informatics, 6: 9; https://do.org/10.1186/s42162-023-00259-2
• 11. Boniecki, P., Piekarska-Boniecka, H., 2004. Neural identification of selected fruit trees pests based on digital techniques for the analysis of images, Journal of Research and Applications in Agricultural Engineering, 49: 25-30.
• 12. Broad, D.R., Dandy, G.C., Maier, H.R., 2015. A systematic approach to determining metamodel scope for risk-based optimization and its application to water distribution system design. Environmental Modelling & Software, 69: 382-395; https://doi.org/10.1016/j.envsoft.2014.11.015
• 13. Butler, J.J., Stotler, R.L., Whittemore, D.O., Reboulet, E.C., 2013. Interpretation of water level changes in the high plains aquifer in western Kansas. Groundwater, 51: 180-190.
• 14. Butler, J.J., Whittemore, D.O., Wilson, B.B., Bohling, G.C. 2016. A new approach for assessing the future of aquifers supporting irrigated agriculture. Geophysical Research Letters, 43: 2004-2010; https://doi: 10.1002/2016gl067879
• 15. Cardenas-Mercado, L., Escobar-Vargas, J., Pérez-Montiel, J.I., Torres, A., 2023. Integration of physically-based numerical tools and socio-hydrological variables in urban flood modeling. Journal of Hydrology, 625: 129974; https:// doi:10.1016/j.jhydrol.2023.129974
• 16. Chadalawada, J., Havlíček, V., Babovic, V., 2016. Genetic programming based approach towards understanding the dynamics of urban rainfall-runoff process. Procedia Engineering, 154: 1093-1102; https://doi.org/10.1016/j.proeng.2016.07.601
• 17. Chen, P., Wang, B., Wu, Y., Wang, Q., Huang, Z., Wang, C., 2023. Urban River water quality monitoring based on self-optimizing machine learning method using multi-source Remote Sensing Data. Ecological Indicators, 146: 109750; https://doi.org/10.1016/j.ecolind.2022.109750
• 18. Chrysikou, L., Gemenetzis, P., Kouras, A., Manoli, E., Terzi, E., Samara, C., 2007. Distribution of persistent organic pollutants, polycyclic aromatic hydrocarbons and trace elements in soil and vegetation following a large scale landfill fire in northern Greece. Environment International, 34: 210-225.
• 19. Cui, L., Xie, G., Qu, Y., Gao, L., Yang, Y., 2018. Security and privacy in smart cities: challenges and opportunities. IEEE Access, 6: 46134-46145; https://doi.org/ 0.1109/ACCESS.2018.2853985
• 20. Dawson, C.W., Wilby, R.L., 2001. Hydrological modelling using artificial neural networks. Progress in Physical Geography: Earth and Environment, 25: 80-108; https://doi.org/10.1177/030913330102500104
• 21. Deng, C., Liu, L., Li, H., Peng, D., Wu, Y., Xia, H., Zhang, Z., Zhu, Q., 2021. A data-driven framework for spatiotemporal characteristics, complexity dynamics, and environmental risk evaluation of river water quality. Science of the Total Environment, 785: 147134; https://doi.org/10.1016/j.scitotenv.2021.147134
• 22. Ebrahimi, H., Rajaee, T., 2017. Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Global Planet Change 148: 181-191.
• 23. Fazilat, H., Ghatarband, M., Mazinani, S., Asadi, Z.A., Shiri, M.E., Kalaee, M.R., 2012. Predicting the mechanical properties of glass fiber reinforced polymers via artificial neural network and adaptive neuro-fuzzy inference system. Computational Materials Science, 58: 31-37; https://doi.org/10.1016/j.commatsci.2012.01.012
• 24. Feng, S., Kang, S., Huo, Z., Chen, S., Mao, X., 2008. Neural networks to simulate regional groundwater levels affected by human activities. Groundwater, 46: 80-90; https://doi.org/10.1111/j.1745-6584.2007.00366.x
• 25. Ghosh, K., Sarkar, A., 2021. Evaluating urban wastewater remediation efficiency of the hydroponic vetiver system through predictive modelling using artificial neural network. Environmental Technology & Innovation, 24: 102007; https://doi.org/10.1016Zj.eti.2021.102007
• 26. Govindan, K., 2023. How digitalization transforms the traditional circular economy to a smart circular economy for achieving sdgs and net zero. Transportation Research Part E: Logistics and Transportation Review, 177: 103147; https://doi.org/10.1016/j.tre.2023.103147
• 27. Hachoumi, I., Pucher, B., De Vito-Francesco, E., Prenner, F., Ertl, T., Langergraber, G., Fürhacker, M., Allabashi, R., 2021. Impact of green roofs and vertical greenery systems on surface runoff quality. Water, 13: 2609; https://doi.org/10.3390/w13192609
• 28. Hastie, T., Tibshirani, R., Friedman, J., 2009. Elements of statistical learning: data mining, inference, and prediction. (n.d.). Scholars Portal.
• 29. Hoseingholi, P., Moeini, R., 2023. Pipe failure prediction of wastewater network using Genetic Programming: proposing three approaches. Ain Shams Engineering Journal, 14: 101958; https://doi.org/10.1016/j.asej.2022.101958
• 30. Jafar, R., Shahrour, I., Juran, I., 2010. Application of artificial neural networks (ANN) to model the failure of urban water mains. Mathematical and Computer Modelling, 51: 1170-1180; https://doi.org/10.1016/j.mcm.2009.12.033
• 31. Jang, S., 1993. AN FIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23: 665-685; https://doi.org/10.1109/21.256541
• 32. Karthika, B.S., Deka, P.C., 2015. Prediction of air temperature by hybridized model (wavelet-ANFIS) using wavelet decomposed data. Aquatic Procedia, 4: 1155-1161; https://doi.org/10.1016/j.aqpro.2015.02.147
• 33. Kong, Q., Yao, J., Qiu, Z., Shen, D., 2016. Effect of mass proportion of municipal solid waste incinerator bottom ash layer to municipal solid waste layer on the Cu and Zn discharge from landfill. BioMed Research International, 2016: 9687879; https://doi.org/10.1155/2016/9687879
• 34. Laino, E., Iglesias, G., 2023. Extreme climate change hazards and impacts on European coastal cittes: a review. Renewable and Sustainable Energy Reviews, 184: 113587; https://doi.org/10.1016/j.rser.2023.113587
• 35. Langergraber, G., Castellar, J.A., Pucher, B., Baganz, G.F., Milosevic, D., Andreucci, M.-B., Atanasova, N., 2021 . A framework for addressing circularity challenges in cities with nature-based solutions. Water, 13: 2355; https:// doi:10.3390/w13172355
• 36. Larsen, T.A., Hoffmann, S., Lüthi, C., Truffer, B., Mau rer, M., 2016. Emerging solutions to the water challenges of an urbanizing world. Science, 352: 928-933; https:// doi:10.1126/science.aad8641
• 37. Li, F., Yigitcanlar, T., Nepal, M., Nguyen, K., Dur, F., 2023. Machine learning and remote sensing integration for Leveraging Urban Sustainability: a review and framework. Sustainable Cities and Society, 96: 104653; https://doi.org/10.1016/j.scs.2023.104653
• 38. Li, Y., Li, J., Chen, S., Diao, W., 2012. Establishing indices for groundwater contamination risk assessment in the vicinity of hazardous waste landfills in China. Environmental Pollution, 165: 77-90; https:// doi.org/10.1016/j.envpol.2011.12.042
• 39. Lopes, M., Barbosa Lima da Silva, G., 2021. An efficient simulation-optimization approach based on genetic algorithms and hydrologic modeling to assist in identifying optimal low impact development designs. Landscape and Urban Planning, 216: 104251; https://doi.org/10.1016/jJandurbplan.2021.104251
• 40. Maier, H.R., Jain, A., Dandy, G.C., Sudheer, K.P., 2010. Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environmental Modelling & Software, 25: 891-909; https://doi.org/10.1016/j.envsoft.2010.02.003
• 41. Melchiorri, M., Florczyk, A.J., Freire, S., Schiavina, M., Pesaresi, M., Kemper, T., 2018. Unveiling 25 years of planetary urbanization with remote sensing: perspectives from the global human settlement layer. Remote Sensing, 10: 768; https://doi.org/10.3390/rs10050768
• 42. Nielsen, D.M., (ed.), 2006. Practical Handbook of Environmental Site Characterization and Ground-Water Monitoring. 2nd ed. CRC Press Taylor, Francis Group.
• 43. Nourani, V., Asghari Mogaddam, A., Nadiri, A.O., 2008. An ANN-based model for spatiotemporal groundwater level foret casting. Hydrological Processes, 22: 5054-5066; https://doi.org/10.1002/hyp.7129
• 44. Nourani, V., Alami, M.T., Daneshvar Vousoughi, F., 2015. Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling. Journal of Hydrology, 524: 255-269; https://doi.org/10.1016/jJhydrol.2015.02.048
• 45. Oral, H.V., Radinja, M., Rizzo, A., Kearney, K., Andersen, T.R., Krzeminski, P., Carvalho, P. N., 2021. Management of urban waters with nature-based solutions in circular cities - exemplified through seven urban circularity challenges. Water, 13: 3334; https://doi: 10.3390/w13233334
• 46. Quevauviller, P., Fouillac, A.M, Grath, J., Ward, R., 2009. Groundwater Monitoring. Water Quality Measurements Series. John Willey, Sons, Ltd.
• 47. RadFard, M., Seif, M., Ghazizadeh Hashemi, A.H., Zarei, A., Saghi, M.H., Shalyari, N., Samaei, M.R., 2019. Protocol for the estimation of Drinking Water Quality Index (DWQI) in Water Resources: Artificial Neural Network (ANFIS) and arc-GIS. MethodsX, 6: 1021-1029; https://doi.org/10.1016/j.mex.2019.04.027
• 48. Rajaee, T., Ebrahimi, H., Nourani, V., 2019. A review of the artificial intelligence methods in groundwater level modeling, Journal of Hydrology, 572; https://doi.org/10.1016/j.jhydrol.2018.12.037
• 49. Razavi, S. , Araghinejad, S., 2009. Reservoir Inflow Modeling Using Temporal Neural Networks with Forgetting Factor Approach. Water Resour Manage, 23: 39-55; https://doi.org/10.1007/s11269-008-9263-7
• 50. Ruangpan, L., Vojinovic, Z., Di Sabatino, S., Leo, L.S., Capobianco, V., Oen, A.M.P., McClain, M.E., Lopez-Gunn, E., 2020. Nature-based solutions for hydro-meteorological risk reduction: a state-of-the-art review of the research area. Natural Hazards and Earth System Sciences, 20: 243-270; https://doi.org/10.5194/nhess-20-243-2020
• 51. Safa, M., Shariati, M., Ibrahim, Z., Toghroli, A., Baharom, S.B., Nor, N.M., Petković, D., 2016. Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam\'s shear strength. Steel and Composite Structures, 21: 679-688; https://doi.org/10.12989/SCS.2016.21.3.679
• 52. Sameen, M.I., Nahhas, F.H., Buraihi, F.H., Pradhan, B., Shariff, A. R., 2016. A refined classification approach by integrating Land-sat Operational Land Imager (OLI) and RADARSAT-2 imagery for land-use and land-cover mapping in a tropical area. International Journal of Remote Sensing, 37: 2358-2375; https://doi:10.1080/01431161.2016.1176273
• 53. Sardella, A., Palazzi, E., von Hardenberg, J., Del Grande, C., De Nuntiis, P., Sabbioni, C., Bonazza, A., 2020. Risk mapping for the sustainable protection of cultural heritage in extreme changing environments. Atmosphere, 11: 700; https://doi.org/10.3390/atmos11070700
• 54. Schmitt, T.G., Thomas, M., Ettrich, N., 2004. Analysis and modeling of flooding in urban drainage systems. Journal of Hydrology, 299: 300-311; https://doi.org/10.1016/j.jhydrol.2004.08.012
• 55. Selim, A., Shuvo, S.N.A., Islam, M.M., Moniruzzaman, M., Shah S., Ohiduzzaman, M., 2023. Predictive models for dissolved oxygen in an urban lake by regression analysis and Artificial Neural Network. Total Environment Research Themes, 7: 100066; https://doi.org/10.1016/j.totert.2023.100066
• 56. Seng, K., Ang, L., Ngharamike, E., 2022. Artificial intelligence Internet of Things: A new paradigm of distributed sensor networks. International Journal of Distributed Sensor Networks, 18; https://doi.org/10.1177/15501477211062835
• 57. Shabani, S., Yousefi, P., Naser, G., 2017. Support vector machines in urban water demand forecasting using phase space reconstruction. Procedia Engineering, 186: 537-543; https://doi.org/10.1016Zj.proeng.2017.03.267
• 58. Shiri, J., Kisi, O., Yoon, H., Lee, K.K., Nazemi, A.H., 2013. Predicting groundwater level fluctuations with meteorological effect implications - a comparative study among soft computing techniques. Computational Geosciences, 56: 32-44; https://doi.org/10.1016/j.cageo.2013.01.007
• 59. Singh, P., Verma, P., Tiwari, A., Sharma, S., Purty, P., 2015. Review of various contamination index approaches to evaluate groundwater quality with Geographic Information System (GIS). International Journal of ChemTech Research, 7: 1920-1929; http://sphinxsai.com/2015/ch_vol7_no4/4/(1920-1929)%20V7 N4.pdf
• 60. Song, C.J., Li, Q.Y., 2011. The prediction model of macro-road traffic accident basing on radial basis function. Applied Mechanics and Materials, 97-98: 981-984; https://doi.org/10.4028/www.scientific.net/amm.97-98.981
• 61. Strohbach, M., Ziekow, H., Gazis, V., Akiva, N., 2015. Towards a Big Data Analytics Framework for loT and Smart City Applications. Berlin, Springer.
• 62. Suparta, W., Samah, A.A., 2020. Rainfall prediction by using ANFIS times series technique in South Tangerang, Indonesia. Geodesy and Geodynamics, 11: 411-417; https://doi.org/10.1016/j.geog.2020.08.001
• 63. Taormina, R., Chau, K., Sethi, R., 2012. Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice Lagoon. Engineering Applications of Artificial Intelligence, 25: 1670-1676; https://doi.org/10.1016/j.engappai.2012.02.009
• 64. Turan, V., Aydın, S. , Sönmez, O., 2022. Production, Cost Analysis, and Marketing of Bioorganic Liquid Fertilizers and Plant Nutrition Enhancers. Microorganisms for Sustainability, 42.
• 65. Vapnik, V.N., 1998. Statistical Learning Theory. Wiley, New York, USA.
• 66. Venkatesan, P., Anitha, S., 2006. Application of a radial basis function neural network for diagnosis of diabetes mellitus. Current Science, 91: 1195-1199. ISSN 0011-3891.
• 67. Wang, W.C., Chau, K.W., Cheng, C.T., Qiu, L., 2009. A comparison of performance of several Artificial Intelligence Methods for forecasting monthly discharge time series. Journal of Hydrology, 374: 294-306; https://doi.org/10.1016/jJhydrol.2009.06.019
• 68. Wu, W., Dandy, G.C. , Maier, H.R., 2014. Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling. Environmental Modelling & Software, 54: 108-127; https://doi.org/10.1016/j.envsoft.2013.12.016
• 69. Xiao, J., Wang, L., Chai, N., Liu, T., Jin, Z., Rinklebe, J., 2021. Groundwater hydrochemistry, source identification and pollution assessment in intensive industrial areas, eastern Chinese loess plateau. Environmental Pollution, 278: 16930; https://doi.org/10.1016/j.envpol.2021.116930
• 70. Yigitcanlar, T., Mehmood, R., Corchado, J.M., 2021. Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures. Sustainability, 13: 8952; https://doi.org/10.3390/su13168952
• 71. Zhang, A., Venkatesh, V.G., Liu, Y., Wan, M., Qu, T., Huisingh, D., 2019. Barriers to smart waste management for a circular economy in China. Journal of Cleaner Production, 240: 118198; https://doi.org/10.1016/jjclepro.2019.118198

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).