Bulletin of Chinese Academy of Sciences (Chinese Version)


sustainable development goals (SDGs); water security; water environment; water system; sustainable management; artificial intelligence (AI)

Document Type



One of the most pervasive challenges affecting human and planetary well-being is inadequate access to clean water and sanitation. Problems with water are expected to become worse in the coming decades, with water scarcity occurring globally, in the face of ever-growing populations, intensive human activities, and climatic variation. Addressing the aforementioned water security has been achieved consensus and has been included into the sustainable development goals (SDGs) set by the United Nations' Agenda 2030. Despite these ample opportunities, it remains challenging to create reliable, sustainable, and affordable solutions to providing universal access to clean water and sanitation. In this context, the emerging artificial intelligence (AI) technology can be an attractive solution to help with this challenge. We summarized the core of the SDGs-Goal 6 (Clean Water and Sanitation) and the problems encountered during the progress to date. Building upon which, we conducted a literature review and provided a state-of-the-art analysis of leveraging AI to help achieving SDGs-Goal 6 alongside the resultant impacts. Afterwards, we highlighted the key issues necessary to be tackled in the coming years if AI is expected to be well applied with its maximum benefits. Plus, we put forward the prospects of future efforts on this revolution.

First page


Last Page





Bulletin of Chinese Academy of Sciences


Larsen T A, Hoffmann S, Luthi C, et al. Emerging solutions to the water challenges of an urbanizing world. Science, 2018, 352:928-933.

United Nations. Tansforming Our World:The 2030 Agenda for Sustainable Development. New York:United Nations, 2015.

Perrault R, Shoham Y, Brynjolfsson E, et al. The AI Index 2019 Report. Palo Alto:Stanford University, 2019.

United Nations. Sustainable Development Goal 6:Synthesis report on water and sanitation 2018. New York:United Nations, 2018.

Kroll C, Warchold A, Pradhan P. Sustainable Development Goals (SDGs):Are we successful in turning trade-offs into synergies? Palgrave Communications, 2019, 5:1-11.

Mulligan M, van Soesbergen A, Hole D G, et al. Mapping nature's contribution to SDG 6 and implications for other SDGs at policy relevant scales. Remote Sensing of Environment, 2020, 239:111671.

Nerini F F, Tomei J, To L S, et al. Mapping synergies and trade-offs between energy and the Sustainable Development Goals. Nature Energy, 2018, 3:10-15.

United Nations. The Sustainable Development Goals Report 2019. New York:United Nations, 2019.

United Nations-Water. Integrated monitoring guide for Sustainable Development Goal 6 on water and sanitation-Targets and global indicators. New York:UN-Water, 2017.

WHO-UNICEF. Progress on Household Drinking Water, Sanitation and Hygiene 2000-2017:Special Focus on Inequalities. Geneva:WHO-UNICEF, 2019.

Big Earth Data Program Chineses Academy of Sciences. Big Earth Data in Support of the Sustainable Development Goals. Beijing:Big Earth Data Program Chineses Academy of Sciences, 2019.

Daigger G T, Sharvelle S, Arabi M, et al. Progress and promise transitioning to the one water/resource recovery integrated urban water management systems. Journal of Environmental Engineering, 2019, 145(10):04019061.

刘俊新, 王旭.城市污水处理的多目标管理.给水排水, 2015, 41(9):1-3.

Wang X, Daigger G, Lee D J, et al. Evolving wastewater infrastructure paradigm to enhance harmony with nature. Science Advances, 2018, 4(8):eaaq0210.

Johnson A C, Jin X W, Nakada N, et al. Learning from the past and considering the future of chemicals in the environment. Science, 2020, 367:384-387.

Guitton M J. The water challenges:Alternative paths to trigger large-scale behavioural shifts. The Lancet Planetary Health, 2017, 1(2):e46-e47.

张昱, 唐妹, 田哲, 等.制药废水中抗生素的去除技术研究进展.环境工程学报, 2018, 12 (1):1-4.

Stephen L, Danny K, Artificial Intelligence in the 21st Century. 3rd ed. Virginia:Mercury Learning & Information, 2020:750.

Zhang Y S, Wu L, Ren H Z, et al. Mapping water quality parameters in urban rivers from hyperspectral images using a new self-adapting selection of multiple artificial neural networks. Remote Sensing, 2020, 12(2):336.

Chen K Y, Chen H X, Zhou C L, et al. Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data. Water Research, 2020, 171:115454.

Chou J S, Ho C C, Hoang H S. Determining quality of water in reservoir using machine learning. Ecological Informatics, 2018, 44:57-75.

García Nieto P J, García-Gonzalo E, Alonso Fernández J R, et al. Water eutrophication assessment relied on various machine learning techniques:A case study in the Englishmen Lake (Northern Spain). Ecological Modelling, 2019, 404:91-102.

Chen H Z, Xu L L, Ai W, et al. Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy. Science of the Total Environment, 2020, 714:136765.

Zhang X L, Zhang F, Kung H, et al. Estimation of the Fe and Cu contents of the surface water in the Ebinur Lake Basin based on LIBS and a machine learning algorithm. International Journal of Environmental Research and Public Health, 2018, 15(11):2390.

Miller T H, Gallidabino M D, MacRae J I, et al. Machine learning for environmental toxicology:A call for integration and innovation. Environmental Science & Technology, 2018, 52(22):12953-12955.

Hino M, Benami E, Brooks N. Machine learning for environmental monitoring. Nature Sustainability, 2018, 1(10):583-588.

Jia X L, Hu B F, Marchant B P, et al. A methodological framework for identifying potential sources of soil heavy metal pollution based on machine learning:A case study in the Yangtze Delta, China. Environmental Pollution, 2019, 250:601-609.

Ballesté E, Belanche-Muñoz L A, Farnleitner A H, et al. Improving the identification of the source of faecal pollution in water using a modelling approach:From multi-source to aged and diluted samples. Water Research, 2020, 171:115392.

Bonansea M, Ledesma C, Rodriguez C, et al. Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina. Hydrology Research, 2015, 46(3):377-388.

Zodrow K R, Li Q, Buono R M, et al. Advanced materials, technologies, and complex systems analyses:Emerging opportunities to enhance urban water security. Environmental Science & Technology, 2017, 51(18):10274-10281.

曲久辉, 赵进才, 任南琪, 等.城市污水再生与循环利用的关键基础科学问题.中国基础科学, 2017, (1):6-12.

de Luna P, Wei J N, Bengio Y, et al. Use machine learning to find energy materials? Nature, 2017, 552(7683):23-27.

Smith A, Keane A, Dumesic J A, et al. A machine learning framework for the analysis and prediction of catalytic activity from experimental data. Applied Catalysis B:Environmental, 2020, 263:118257.

许国栋, 张婧怡, 陈珺, 等.城市污水处理微污染物的挑战与对策.给水排水, 2016, 52:40-44.

Kulkarni P, Chellam S. Disinfection by-product formation following chlorination of drinking water:Artificial neural network models and changes in speciation with treatment. Science of the Total Environment, 2010, 408(19):4202-4210.

Raza A, Bardhan S, Xu L, et al. A machine learning approach for predicting defluorination of Per-and Polyfluoroalkyl Substances (PFAS) for their efficient treatment and removal. Environmental Science & Technology Letters, 2019, 6(10):624-629.

Han Z, An W, Yang M, et al. Assessing the impact of source water on tap water bacterial communities in 46 drinking water supply systems in China. Water Research, 2020, 172:115469.

张冰, 吴林蔚, 文湘华.全国城市污水处理厂中微生物群落的溯源分析.环境科学, 2019, 40(8):3699-3705.

Lesnik K L, Liu H. Predicting microbial fuel cell biofilm communities and bioreactor performance using artificial neural networks. Environmental Science & Technology, 2017, 51(18):10881-10892.

Wang X, McCarty P L, Liu J, et al. Probabilistic evaluation of integrating resource recovery into wastewater treatment to improve environmental sustainability. PNAS, 2015, 112(5):1630-1635.

Zhao L, Dai T, Qiao Z, et al. Application of Artificial Intelligence to wastewater treatment:A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse. Process Safety and Environmental Protection, 2020, 133:169-182.

Rodell M, Famiglietti J S, Wiese D N, et al. Emerging trends in global freshwater availability. Nature, 2018, 557(7707):651-659.

Daigger G T, Murthy S, Love N G, et al. Transforming environmental engineering and science education, research, and practice. Environmental Engineering Science, 2017, 34(1):42-50.

Hassan W H, Jassem M H, Mohammed S S. A GA-HP model for the optimal design of sewer networks. Water Resources Management, 2018, 32(3):865-879.

Ogidan O, Giacomoni M. Multiobjective genetic optimization approach to identify pipe segment replacements and inline storages to reduce sanitary sewer overflows. Water Resources Management, 2016, 30(11):3707-3722.

Cunha M C, Zeferino J A, Simões N E, et al. Optimal location and sizing of storage units in a drainage system. Environmental Modelling & Software, 2016, 83:155-166.

Abba S, Pham Q B, Usman A G, et al. Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant. Journal of Water Process Engineering, 2020, 33:101081.

Bi W, Dandy G C, Maier H R. Improved genetic algorithm optimization of water distribution system design by incorporating domain knowledge. Environmental Modelling and Software, 2015, 69:370-381.

Zhang Y Y, Gao X, Smith K, et al. Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network. Water Research, 2019, 164:114888.

Vamvakeridou-Lyroudia L S, Chen A S, Khoury M, et al. Assessing and visualising hazard impacts to enhance the resilience of critical infrastructures to urban flooding. Science of the Total Environment, 2020, 707:136078.

Sayers W, Savic D, Kapelan Z. Performance of LEMMO with artificial neural networks for water systems optimisation. Urban Water Journal, 2019, 16(1):21-32.

Reichstein M, Camps-Valls G, Stevens B, et al. Deep learning and process understanding for data-driven Earth system science. Nature, 2019, 566(7743):195-204.

夏军, 谈戈.全球变化与水文科学新的进展与挑战.资源科学, 2002:1-7.

陈发虎, 傅伯杰, 夏军, 等.近70年来中国自然地理与生存环境基础研究的重要进展与展望.中国科学:地球科学, 2019, 49(11):1659-1696.

刘昌明, 梁康.作为水文科学基本理论的水循环研究若干探讨//中国水文科技新发展--2012中国水文学术讨论会.南京:河海大学出版社, 2012:4.

郭华东.地球大数据科学工程.中国科学院院刊, 2018, 33(8):818-824.

Pope A J, Gimblett R. Linking Bayesian and agent-based models to simulate complex social-ecological systems in semiarid regions. Frontiers in Environmental Science, 2015, 3:55.

Shi Z H, Ai L, Li X, et al. Partial least-squares regression for linking land-cover patterns to soil erosion and sediment yield in watersheds. Journal of Hydrology, 2013, 498:165-176.

纪鹏, 郭华东, 张露.近20年西昆仑地区冰川动态变化遥感研究.国土资源遥感, 2013, 25:93-98.

Schluter M, Baeza A, Dressler G, et al. A framework for mapping and comparing behavioural theories in models of social-ecological systems. Ecological Economics, 2017, 131:21-35.

傅伯杰.联合国可持续发展目标与地理科学的历史任务.科技导报, 2020, 38:19-24.

Sinha J, Jha S, Goyal M K. Influences of watershed characteristics on long-term annual and intra-annual water balances over India. Journal of Hydrology, 2019, 577:123970.

Valerio C, De Stefano L, Martínez-Muñoz G, et al. A machine learning model to assess the ecosystem response to water policy measures in the Tagus River Basin (Spain). Science of the Total Environment, 2020:141252.

Giri S, Arbab N N, Lathrop R G. Assessing the potential impacts of climate and land use change on water fluxes and sediment transport in a loosely coupled system. Journal of Hydrology, 2019, 577:123955.

Liao H, Sarver E, Krometis L A H. Interactive effects of water quality, physical habitat, and watershed anthropogenic activities on stream ecosystem health. Water Research, 2018, 130:69-78.

Romulo C L, Posner S, Cousins S, et al. Global state and potential scope of investments in watershed services for large cities. Nature Communications, 2018, 9(1):4375.

Jordan M I, Mitchell T M. Machine learning:Trends, perspectives, and prospects. Science, 2015, 349(6245):255-260.

Will S, Cassidy W, Randolf W, et al. Digital Water:Industry Leaders Chart the Transformation Journey. London:International Water Association and Xylem Inc., 2019.

Hoffmann S, Feldmann U, Bach P M, et al. A research agenda for the future of urban water management:Exploring the potential of nongrid, small-grid, and hybrid solutions. Environmental Science & Technology, 2020, 54(9):5312-5322.

Strubell E, Ganesh A, McCallum A. Energy and Policy Considerations for Deep Learning in NLP//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence:Association for Computational Linguistics, 2019:3645-3650.

UN Environment Programme. Smart 2020:Enabling the low carbon economy in the information age. Nairobi:UN Environment Programme, 2008.

Whitehead B, Andrews D, Shah A, et al. Assessing the environmental impact of data centres part 1:Background, energy use and metrics. Building and Environment, 2014, 82:151-159.

Raissi M, Perdikaris P, Karniadakis G E. Physics informed deep learning (part Ⅰ):Data-driven solutions of nonlinear partial differential equations. arXiv, 2017:1711.10561.

Blumensaat F, Leitao J P, Ort C, et al. How urban storm-and wastewater management prepares for emerging opportunities and threats:Digital transformation, ubiquitous sensing, new data sources, and beyond-A horizon scan. Environmental Science & Technology, 2019, 53(15):8488-8498.

Eggimann S, Mutzner L, Wani O, et al. The potential of knowing more:A review of data-driven urban water management. Environmental Science & Technology, 2017, 51(5):2538-2553.

Perrault R, Shoham Y, Brynjolfsson E, et al. AI Now Report 2018. New York:AI Now Institute, 2018.

Swathi B, S. Shoban B, Monelli A. Artificial Intelligence:Characteristics, subfields, techniques and future predictions. Journal of Mechanics of Continua and Mathematical Sciences, 2019, 14:127-135.

Rai S P, Sharma N, Lohani A K. Risk assessment for transboundary rivers using fuzzy synthetic evaluation technique. Journal of Hydrology, 2014, 519:1551-1559.