کاربرد مدلهاي هوشمند در مديريت منابع آب
محورهای موضوعی : مدیریت منابع آب
یاسر سبزواری
1
,
سعید اسلامیان
2
1 - گروه علوم و مهندسي آب، دانشکده مهندسي کشاورزي؛ دانشگاه صنعتي اصفهان.
2 - گروه علوم و مهندسي آب، دانشکده مهندسي کشاورزي؛ دانشگاه صنعتي اصفهان.
کلید واژه: هوش مصنوعي, مدلهاي هوشمند, يادگيري عميق, اينترنت اشيا, مديريت منابع آب,
چکیده مقاله :
منابع آب، يکي از مهم‌ترين عناصر حياتي بشر و از جمله عوامل پيشرفت در مناطق مختلف به شمار مي‌رود. مديريت اين منابع مهم، امري ضروري و چالش برانگيز در دنياي امروز بوده که مولفه‌هاي مختلفي را شامل مي‌شود. امور مربوط به حفظ منابع آب، برداشت آب، برنامه‌ريزي منابع آب موجود و توزيع بسيار مناسب آن بين بخش‌هاي مختلف مصرف ازجمله اين مولفه‌ها هستند. يکي از مولفه-هاي کليدي در استفاده بهينه از منابع آب موجود، مديريت صحيح منابع آب موجود با استفاده از فناوري‌هاي پيشرفته است. مديريت آب شامل امور حفظ منابع آب، برداشت آب، برنامه‌ريزي منابع آب موجود و توزيع بسيار مناسب آن بين مصرف کنندگان است. شيوه‌هاي مديريت آب بايد به طور کامل در نظر گرفته شده تا منابع آب در دراز مدت پايدار بماند. منابع آب شيرين در دسترس بسيار محدود بوده و از اين ميزان اندک نيز تقريباً ۹۷ درصد آن، شور بوده و براي آشاميدن مناسب نيست. مشکل آلودگي، آب موجود را نيز تحت تأثير قرار مي‌دهد. بخش‌هاي مختلفي منابع آب را تحت تاثير قرار مي‌دهد، از جمله: کشاورزي، شرب، صنعت. آب از منابع مختلف بايد به شيوه‌اي کارآمد استفاده شده که در روش‌هاي مديريت سنتي آب وجود ندارد. روش‌هاي موجود براي استفاده از آب چندان مقرون به صرفه نيستند. در کنار فشار بر منابع از جانب اين بخش‌ها، تمايلي هم به اجراي آخرين فناوري‌هاي اطلاعات و ارتباطات وجود ندارد. الگوريتم‌هاي يادگيري ماشين اين پتانسيل را دارند که فرآيند يادگيري را به صورت تصاعدي با يک هدف خاص گسترش دهند. مديريت به شيوه نوين آب در زمينه‌هايي مانند کشاورزي، تامين آب، صنعت، توليد برق آبي، توليدات دامي و... مورد نياز است. از اين رو مطالعه و کاربرد روش‌هاي نوين مانند يادگيري ماشين براي بهبود مديريت منابع آب ضروري است. اين مطالعه چالش‌ها و فرصت‌هاي مختلف در رابطه با اجراي شبکه‌هاي عصبي عميق براي فرآيند مديريت آب را مورد بحث قرار داد. بنابراين، اين مطالعه، پيشنهادي در راستاي جهت‌گيري‌هاي آينده براي فعاليت‌هاي تحقيقاتي آتي در مورد چالش‌ها و مسائل اجراي مديريت آب با شبکه‌هاي عصبي عميق ارائه مي‌کند.
Abstract:
Water resources are one of the most important elements of humanity and are among the factors of progress in various regions. The management of these important resources is an essential and challenging matter in today's world that includes various components. Matters related to the conservation of water resources, water withdrawal, planning of existing water resources and their very appropriate distribution among different consumption sectors are among these components. One of the key components in the optimal use of existing water resources is the proper management of existing water resources using advanced technologies. Water management includes matters related to the conservation of water resources, water withdrawal, planning of existing water resources and their very appropriate distribution among consumers. Water management methods must be fully considered so that water resources remain sustainable in the long term. Available freshwater resources are very limited and of this small amount, approximately 97% of it is saline and not suitable for drinking. The problem of pollution also affects existing water. Various sectors affect water resources, including: agriculture, drinking, industry. Water from different sources needs to be used in an efficient manner that is not available in traditional water management methods. Existing methods for water use are not very cost-effective. Along with the pressure on resources from these sectors, there is also a lack of interest in implementing the latest information and communication technologies. Machine learning algorithms have the potential to expand the learning process exponentially with a specific goal. Modern water management is needed in fields such as agriculture, water supply, industry, hydroelectric power generation, livestock production, etc. Hence, the study and application of modern methods such as machine learning is essential for improving water resource management. This study discussed various challenges and opportunities related to the implementation of deep neural networks for the water management process. Therefore, this study provides a suggestion in the direction of future research activities on the challenges and issues of implementing water management with deep neural networks.
Akhund, T.M., Ullah, N., Newaz, N.T., Zaman, Z., Sultana, A., Barros, A., Whaiduzzaman, M. (2022). Iot-based low-cost automated irrigation system for smart farming. In Intelligent Sustainable Systems, Springer: Berlin/Heidelberg, Germany, pp. 83–91.
Alam, G., Ihsanullah, I., Naushad, M., Sillanpää, M. (2022). Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects. Chem. Eng. J. 427, 130011.
Allen, M., Preis, A., Iqbal, M., Whittle, A.J. (2013). Water distribution system monitoring and decision support using a wireless sensor network. In Proceedings of the 2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Honolulu, HI, USA, 1–3 July 2013, pp. 641–646.
Azami, H., Hajatmand, M. and Qanbarabadi, F. 2024, Application of Artificial Intelligence in Water Resources Management, First Biennial International Conference on Artificial Intelligence and Data Sciences, Bushehr, https://civilica.com/doc/2008173. (In Persian)
Baek, S.S., Pyo, J., Chun, J.A. (2020). Prediction of water level and water quality using a CNN-LSTM combined deep learning approach. Water 12, 3399.
Balist, J., Malekmohammadi, B., Jafari, H. R., Nohegar, A. & Geneletti, D. (2021) Modeling the supply, demand, and stress of water resources using ecosystem services concept in Sirvan River Basin (Kurdistan-Iran). Water Supply 22 (3), 2816–2831.
Berthet, A., Vincent, A., Fleury, P. (2021). Water quality issues and agriculture: An international review of innovative policy schemes. Land Use Policy 109, 105654.
Bhagat, S.K., Tung, T.M., Yaseen, Z.M. (2020). Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research. J. Clean. Prod. 250, 119473.
Bhattacharyya, D., Swathi, K., Rao, N.T., Ku-mari, N.M.J. (2021). Long term prediction of rainfall in Andhra Pradesh with Deep learning. J. Med Pharm. Allied Sci. 10, 3132–3137.
Chang, C.C., Wang, J.H., Wu, J.L., Hsieh, Y.Z., Wu, T.D., Cheng, S.C., Chang, C.C., Juang, J.G., Liou, C.H., Hsu, T.H., et al. (2021). Applying artificial intelligence (AI) techniques to implement a practical smart cage aquaculture management system. J. Med Biol. Eng. 41, 652–658.
Chang, L.C., Chang, F.J., Yang, S.N., Kao, I.F., Ku, Y.Y., Kuo, C.L., Amin, I.M.Z.b.M. (2019). Building an intelligent hydroinformatics integration platform for regional flood inundation warning systems. Water 11, 9.
Chellaswamy, C., Nisha, J., Sivakumar, K., Kaviya, R. (2018). An IoT based dam water management system for agriculture. In Proceedings of the 2018 International Conference on Recent Trends in Electrical, Control and Communication (RTECC), Johor, Malaysia, 20–22 March 2018, pp. 51–56.
Dawood, T., Elwakil, E., Novoa, H.M., Delgado, J.F.G. (2020). Artificial intelligence for the modeling of water pipes deterioration mechanisms. Autom. Constr. 120, 103398.
Ding, L., Yang, Q., Yang, Y., Ma, H., Martin, J.D. (2021). Potential risk assessment of groundwater to address the agricultural and domestic challenges in Ordos Basin. Environ. Geochem. Health 43, 717–732.
El Mezouari, A., Fazziki, A. E. & Sadgal, M. (2022). A hybrid artificial neural network: an optimization-based framework for smart groundwater governance. Water Supply 22 (5), 5237–5252.
Emami, S. and Choopan, Y. (2013). Using modern methods of artificial intelligence in investigating the quality of underground water resources (Case study: Selmas plain). Scientific Research Quarterly of Environmental Geology. Year 14, Number 50 Spring 2019. (In Persian)
Fan, G., Xia, J., Song, J., Sun, H. & Liang, D. (2021). Research on application of ecohydrology to disaster prevention and mitigation in China: a review. Water Supply 22 (3), 2946–2958.
Feng, B.-f., Xu, Y.-s., Zhang, T. & Zhang, X. (2021). Hydrological time series prediction by extreme learning machine and sparrow search algorithm. Water Supply 22 (3), 3143–3157.
Figueiredo, I., Esteves, P., Cabrita, P. (2021). Water wise—A digital water solution for smart cities and water management entities. Procedia Comput. Sci. 181, 897–904.
Flouris, I., Giatrakos, N., Deligiannakis, A., Garofalakis, M., Kamp, M., Mock, M. (2017). Issues in complex event processing: Status and prospects in the big data era. J. Syst. Softw. 127, 217–236.
Gaurav, V., Vinod, V., Singh, S.K., Sharma, T., Pradyumna, K., Choudhary, S. (2021). RainRoof: Automated Shared Rainwater Harvesting Prediction. In Sustainable Communication Networks and Application, Springer: Berlin/Heidelberg, Germany, pp. 105–122.
Gautam, G., Sharma, G., Magar, B.T., Shrestha, B., Cho, S., Seo, C. (2021). Usage of IoT Framework in Water Supply Management for Smart City in Nepal. Appl. Sci. 11, 5662.
Gaya, M., Zango, M., Yusuf, L., Mustapha, M., Muhammad, B., Sani, A., Tijjani, A., Wahab, N., Khairi, M. (2017). Estimation of turbidity in water treatment plant using Hammerstein-Wiener and neural network technique. Indones. J. Electr. Eng. Comput. Sci. 5, 666–672.
Glória, A., Dionisio, C., Simões, G., Cardoso, J., Sebastião, P. (2020). Water management for sustainable irrigation systems using internet-of-things. Sensors 20, 1402.
Goedertier, S., Vanthienen, J., (2015). Caron, F. Declarative business process modelling: Principles and modelling languages. Enterp. Inf. Syst. 9, 161–185.
Gonçalves, R., Soares, J.J.M., Lima, R.M.F. (2020). An IoT-Based Framework for Smart Water Supply Systems Management. Future Internet 12, 114.
Hafizi Md Lani, N., Yusop, Z., Syafiuddin, A. (2018). A review of rainwater harvesting in Malaysia: Prospects and challenges. Water 10, 506.
Ibrahim, S.N., Asnawi, A., Abdul Malik, N., Mohd Azmin, N., Jusoh, A., Mohd Isa, F. (2018). Web based Water Turbidity Monitoring and Automated Filtration System: IoT Application in Water Management. Int. J. Electr. Comput. Eng. 8, 2088–8708.
Jadhav, S.B., Pingle, N.S. (2016). Automatic Measurement and Reporting System of Water Quality Based On GSM. Imp. J. Interdiscip. Res 2, 657–662.
Jain, H., Buch, M., Babu, P. (2021). Water management system using machine learning. In Data Engineering and Intelligent Computing, Springer: Berlin/Heidelberg, Germany, 2021, pp. 481–492.
Jan, F., Min-Allah, N., Düştegör, D. (2021). IoT Based Smart Water Quality Monitoring: Recent Techniques, Trends and Challenges for Domestic Applications. Water 13, 1729.
Jenny, H., Alonso, E.G., Wang, Y., Minguez, R. (2020). Using Artificial Intelligence for Smart Water Management Systems, Asian Development Bank: Mandaluyong, Philippines.
Jun, Z. J., Ming, L. K., Qiang, C. Y., Min, W. & Xin, P. Z. (2022). Impacts of changing conditions on the ecological environment of the Shiyang River Basin, China. Water Supply 22 (6), 5689–5697.
Kamienski, C., Soininen, J.P., Taumberger, M., Dantas, R., Toscano, A., Salmon Cinotti, T., Filev Maia, R., Torre Neto, A. (2019). Smart Water Management Platform: IoT-Based Precision Irrigation for Agriculture. Sensors 19, 276.
Kanneganti, D., Reinersman, L. E., Holm, R. H. & Smith, T. (2022). Estimating sewage flow rate in Jefferson County, Kentucky, using machine learning for wastewater-based epidemiology applications. Water Supply 22 (12), 8434–8439.
Khullar, S., Singh, N. (2022). Water quality assessment of a river using deep learning Bi-LSTM methodology: Forecasting and validation. Environ. Sci. Pollut. Res. 29, 12875–12889.
Koech, R., Langat, P. (2018). Improving irrigation water use efficiency: A review of advances, challenges and opportunities in the Australian context. Water 10, 1771.
Krishnan, S. R., Nallakaruppan, M. K., Chengoden, R., Koppu, S., Iyapparaja, M., Sadhasivam, J., & Sethuraman, S. (2022). Smart water resource management using Artificial Intelligence—A review. Sustainability, 14(20), 13384.
Li, E.Y., Wang, W.H., Hsu, Y.S. (2017). Adopting IoT technology to optimize intelligent water management. In Proceedings of the International Conference on Electronic Business (ICEB), Dubai, United Arab Emirates, 4–8 December 2017, pp. 38–46.
Li, H. Y., Deng, C., Zhao, L., Gong, C. H., Zhu, M. F. & Chen, J. W. (2022b). Ozone water production using a SPE electrolyzer equipped with boron doped diamond electrodes. Water Supply 22 (4), 3993–4005.
Lowe, M., Qin, R., Mao, X. (2022). A review on machine learning, artificial intelligence, and smart technology in water treatment and monitoring. Water 14, 1384.
Manu, D., Thalla, A.K. (2017). Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater. Appl. Water Sci. 7, 3783–3791.
Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I.A.T., Siddiqa, A., Yaqoob, I. (2017). Big IoT data analytics: Architecture, opportunities, and open research challenges. IEEE Access 5, 5247–5261.
Maroli, A.A., Narwane, V.S., Raut, R.D., Narkhede, B.E. (2021). Framework for the implementation of an Internet of Things (IoT)-based water distribution and management system. Clean Technol. Environ. Policy 23, 271–283.
Mercier-Laurent, E. (2021). Can Artificial Intelligence Effectively Support Sustainable Development? In IFIP International Workshop on Artificial Intelligence for Knowledge Management, Springer: Berlin/Heidelberg, Germany, 2021, pp. 144–159.
Nabavi-Pelesaraei, A., Rafiee, S., Mohtasebi, S.S., Hosseinzadeh-Bandbafha, H., Wing Chau, K. (2018). Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production. Sci. Total Environ. 631–632, 1279–1294.
Naik, B., Kaushik, V. & Kumar, M. (2022). Water surface profile in converging compound channel using gene expression programming. Water Supply 22 (5), 5221–5236.
Nandhini, R., Poovizhi, S., Jose, P., Ranjitha, R., Anila, S. (2017). Arduino based smart irrigation system using IoT. In Proceedings of the 3rd National Conference on Intelligent Information and Computing Technologies (IICT ‘17), Paris, France, 16–17 March 2017, pp. 1–5.
Nasser, A.A., Rashad, M.Z., Hussein, S.E. (2020). A two-layer water demand prediction system in urban areas based on micro-services and LSTM neural networks. IEEE Access 8, 147647–147661.
Nemade, B., Shah, D. (2022). An efficient IoT based prediction system for classification of water using novel adaptive incremental learning framework. J. King Saud-Univ.-Comput. Inf. Sci. 34, 5121–5131.
Nie, X., Fan, T., Wang, B., Li, Z., Shankar, A., Manickam, A. (2020). Big Data analytics and IoT in Operation safety management in Under Water Management. Comput. Commun. 154, 188–196.
Nourani, V., Asghari, P., Sharghi, E. (2021). Artificial intelligence based ensemble modeling of wastewater treatment plant using jittered data. J. Clean. Prod. 291, 125772.
Obaideen, K., Yousef, B.A., AlMallahi, M.N., Tan, Y.C., Mahmoud, M., Jaber, H., Ramadan, M. (2022). An Overview of Smart Irrigation Systems Using IoT. Energy Nexus 7, 100124.
Oberascher, M., Kinzel, C., Kastlunger, U., Kleidorfer, M., Zingerle, C., Rauch, W., Sitzenfrei, R. (2021). Integrated urban water management with micro storages developed as an IoT-based solution—The smart rain barrel. Environ. Model. Softw. 139, 105028.
Okoji, C.N., Okoji, A.I., Ibrahim, M.S., Obinna, O. (2022). Comparative analysis of adaptive neuro-fuzzy inference system (ANFIS) and RSRM models to predict DBP (trihalomethanes) levels in the water treatment plant. Arab. J. Chem. 15, 103794.
Phasinam, K., Kassanuk, T., Shinde, P.P., Thakar, C.M., Sharma, D.K., Mohiddin, M., Rahmani, A.W. (2022). Application of IoT and cloud computing in automation of agriculture irrigation. J. Food Qual. 2022.
Pour Tabari, M.R., Ebadi, T. and Maknoun, R. (2010). Presenting an intelligent model for determining the behavior of changes in the level of underground water, taking into account the dynamic state of the aquifer system. Water and Wastewater Journal, 76 (Winter 2019) 70. (In Persian)
Prasad, D.V.V., Venkataramana, L.Y., Kumar, P.S., Prasannamedha, G., Harshana, S., Srividya, S.J., Harrinei, K., Indraganti, S. (2022). Analysis and prediction of water quality using deep learning and auto deep learning techniques. Sci. Total. Environ. 821, 153311.
Radhakrishnan, V., Wu, W. (2018). IoT technology for smart water system. IEEE 20th International Conference on High Performance Computing and Communications, IEEE 16th International Conference on Smart City. In Proceedings of the IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, UK, 28–30 June 2018, pp. 1491–1496.
Raei, E., Asanjan, A.A., Nikoo, M.R., Sadegh, M., Pourshahabi, S., Adamowski, J.F. (2022). A deep learning image segmentation model for agricultural irrigation system classification. Comput. Electron. Agric. 198, 106977.
Ray, P., Kaluri, R., Reddy, T., Lakshmanna, K. (2021). Contemporary Developments and Technologies in Deep Learning–Based IoT. In Deep Learning for Internet of Things Infrastructure, CRC Press: Boca Raton, FL, USA, pp. 61–82.
Reddy, N., Satya, B. & Pramada, S. K. (2022). A hybrid artificial intelligence and semi-distributed model for runoff prediction. Water Supply 22 (7), 6181–6194.
Roshni, T., Mirzania, E., Hasanpour Kashani, M., Bui, Q.A.T., Shamshirband, S. (2022). Hybrid support vector regression models with algorithm of innovative gunner for the simulation of groundwater level. Acta Geophys. 70, 1885–1898.
Saraiva, M., Protas, É., Salgado, M., Souza, C. (2020). Automatic mapping of center pivot irrigation systems from satellite images using deep learning. Remote Sens. 12, 558.
Sayari, S., Mahdavi-Meymand, A., Zounemat-Kermani, M. (2021). Irrigation water infiltration modeling using machine learning. Comput. Electron. Agric. 180, 105921.
Shah, J. (2017). An internet of things based model for smart water distribution with quality monitoring. Int. J. Innov. Res. Sci. Eng. Technol 6, 3446–3451.
Shevale, R., Karad, S., Merchant, M., Kardile, A., Mishra, V. (2018). IOT based real time water monitoring system for smart city. Int. J. Innov. Res. Technol. 3, 246–251.
Tabatabaei, S., & Nosrati Nahook, H. (2022). Designing and Implementing a Wireless Sensor Node-Based Intelligent Management System for Agricultural Product Irrigation Scheduling and Timing. Irrigation and Water Engineering, 13(2), 391-403. doi: 10.22125/iwe.2022.162683. (In Persian)
Victor, N.C.R., Alazab, M., Bhattacharya, S., Magnusson, S., Maddikunta, P.K.R., Ramana, K., Gadekallu, T.R. (2022). Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities. arXiv arXiv:2207.13976.
Viet, N.D., Jang, D., Yoon, Y., Jang, A. (2022). Enhancement of membrane system performance using artificial intelligence technologies for sustainable water and wastewater treatment: A critical review. Crit. Rev. Environ. Sci. Technol. 52, 3689–3719.
Vijayakumar, J. (2021). Turmeric Farm Monitoring System using Wireless Sensor Network with ESP32 Module. In Proceedings of the 2021 Smart Technologies, Communication and Robotics (STCR), Sathyamangalam, India, 9–10 October 2021, pp. 1–5.
Wang, J., Lei, X., Cai, S. & Zhao, J. (2022). Location identification of river bathymetric error based on the forward and reverse flow routing. Water Supply 22 (5), 5095–5110.
Wu, Y., Kim, K., Henry, M.F., Youcef-Toumi, K. (2017). Design of a leak sensor for operating water pipe systems. In Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada, 24–28 September 2017, pp. 6075–6082.
Xiang, X., Li, Q., Khan, S., Khalaf, O.I. (2021). Urban water resource management for sustainable environment planning using artificial intelligence techniques. Environ. Impact Assess. Rev. 86, 106515.
Xu, C., Sun, Q. & Lu, C. (2022). Analysis of rainfall and temperature characteristics and Its correlation with southern oscillation index in Beijing, China. Water Supply 22 (4), 4544–4557.
Yadav, A., Joshi, D., Kumar, V., Mohapatra, H., Iwendi, C., Gadekallu, T.R. (2022). Capability and Robustness of Novel Hybridized Artificial Intelligence Technique for Sediment Yield Modeling in Godavari River, India. Water 14, 1917.
Yan, L., He, X., Lu, C., Sun, Q. & Wu, C. (2021). Groundwater management zones and their groundwater level thresholds in the Tongliao Plain. Water Supply 22 (3), 2586–2595.
Yang, L.I.U., Lei, Z.B., Wu, K.H., Hua, C.A.I., Xuan, L.I., Zhao, X.Y., Tang, Z.M. (2016). Edge-centric Computing for Smart Water Supply: Management and Service. Destech Trans. Mater. Sci. Eng. 1, 1–8.
Yousefi, P., Montaseri, M., & Rezaverdinejad, V. (2018). Intelligent Water Resource Allocation System to Mitigating Water Shortage Effects in the Reservoir,Case Study: Bukan Reservoir. Iranian journal of Ecohydrology, 5(2), 343-355. doi: 10.22059/ije.2017.230147.539. (In Persian)
Zanfei, A., Brentan, B.M., Menapace, A., Righetti, M., Herrera, M. (2022). Graph convolutional recurrent neural networks for water demand forecasting. Water Resour. Res. 58, e2022WR032299.
Zhang, J., Jing, H., Dong, K., Jin, Z. & Ma, J. (2022). The effect of drip irrigation under mulch on groundwater infiltration and recharge in a semiarid agricultural region in China. Water Supply 22 (4), 4043–4054.
Zhao, L., Dai, T., Qiao, Z., Sun, P., Hao, J., Yang, Y. (2020). Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse. Process Saf. Environ. Prot. 133, 169–182.
Zhou, K. (2022). Multi-objective water resources optimum allocation scheme based on an Improved Standard Cuckoo Search Algorithm (ISCSA). Water Supply 22 (10), 7893–7903.
