Forecasting water consumption on transboundary water resources for water resource management using the feed-forward neural network: a case study of the Nile River in Egypt and Kenya
Anne Wambui Mumbi
A B , Fengting Li A B C , Jean Pierre Bavumiragira A B and Fangnon Firmin Fangninou B
+ Author Affiliations
- Author Affiliations
A College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai, 200092, PR China.
B State Key Laboratory of Pollution Control and Resource Reuse Study, College of Environmental Science and Engineering, Tongji University, Siping Road 1239, Shanghai, 200092, PR China.
C Corresponding author. Email: fengting@tongji.edu.cn
Marine and Freshwater Research 73(3) 292-306 https://doi.org/10.1071/MF21118
Submitted: 26 April 2021 Accepted: 30 September 2021 Published: 10 November 2021
Journal Compilation © CSIRO 2021 Open Access CC BY-NC-ND
Abstract
Water resources are an essential component of a country’s natural resource potential. Pressure on these resources is set to increase due to increased water demand, climate change and rainfall variability. This could lead to conflicts between sectoral users, within or between countries, especially among transboundary countries. Interest in transboundary water resources is a priority, especially where issues such as uncertainty regarding the status of transboundary waterbodies and reductions in water volume persist. In this study, we used the feed-forward neural network to forecast water demand along the Nile River in two countries, Egypt and Kenya. Two scenarios were modelled. Input data for the first scenario included preceding records of precipitation, gross domestic product, population and water use in the agricultural sector. The second scenario observed the effects of the growing economy on water resources by doubling the gross domestic product and keeping all other inputs constant. For Kenya, the results projected a steady increase in water demand throughout the next 20 years for both scenarios. However, for Egypt, the observed trend in both scenarios was a decline in water demand, followed by a steady increase. The results underscore the importance of forecasting for easier future planning and management, and to help governing bodies along transboundary water resources develop timely strategies in the future to alleviate future water shortages and poor management of water resources.
Keywords: feed-forward neural network, Nile River, recurrent neural network, transboundary resources, water demand, water resources.
References
Abdel-Satar, A. M., Ali, M. H., and Goher, M. E. (2017). Indices of water quality and metal pollution of Nile River, Egypt. Egyptian Journal of Aquatic Research 43, 21–29.| Indices of water quality and metal pollution of Nile River, Egypt.Crossref | GoogleScholarGoogle Scholar |
Adamowski, J. F. (2008). Peak daily water demand forecast modeling using artificial neural networks. Journal of Water Resources Planning and Management 134, 119–128.
| Peak daily water demand forecast modeling using artificial neural networks.Crossref | GoogleScholarGoogle Scholar |
Adamowski, J., and Karapataki, C. (2010). Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms. Journal of Hydrologic Engineering 15, 729–743.
| Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms.Crossref | GoogleScholarGoogle Scholar |
Ahmed, A. A., Fogg, G. E., and Gameh, M. A. (2014). Water use at Luxor, Egypt: consumption analysis and future demand forecasting. Environmental Earth Sciences 72, 1041–1053.
| Water use at Luxor, Egypt: consumption analysis and future demand forecasting.Crossref | GoogleScholarGoogle Scholar |
Allan, J. A. (2009) Nile basin asymmetries: a closed fresh water resource, soil water potential, the political economy and Nile transboundary hydropolitics. In ‘The Nile’. pp. 749–770. (Springer)
Arbués, F., Garcıa-Valiñas, M. Á., and Martınez-Espiñeira, R. J. (2003). Estimation of residential water demand: a state-of-the-art review. Journal of Socio-Economics 32, 81–102.
| Estimation of residential water demand: a state-of-the-art review.Crossref | GoogleScholarGoogle Scholar |
Arnell, N. W. (2004). Climate change and global water resources: Sres emissions and socio-economic scenarios. Global Environmental Change 14, 31–52.
| Climate change and global water resources: Sres emissions and socio-economic scenarios.Crossref | GoogleScholarGoogle Scholar |
Asaminev, A. C., Denghua, Y., Xinshan, S., Hao, W., Dorjsuren, B., Abel, G., Baisha, W., and Tianlin, Q. (2020). Assessing the impact of climate change on hydro-ecological processes of the Abbay River Basin in Ethiopia. Молодой ученый 50, 394–402.
Bastiaanssen, W. G., Karimi, P., Rebelo, L.-M., Duan, Z., Senay, G., Muthuwatte, L., and Smakhtin, V. (2014). Earth observation based assessment of the water production and water consumption of Nile Basin agro-ecosystems. Remote Sensing 6, 10306–10334.
| Earth observation based assessment of the water production and water consumption of Nile Basin agro-ecosystems.Crossref | GoogleScholarGoogle Scholar |
Baumann, D. D., Boland, J., and Hanemann, W. M. (1998). ‘Urban Water Demand Management and Planning’ (McGraw-Hill: New York, NY, USA.)
Bierkens, M. F., Knotters, M., and Hoogland, T. (2001). Space‐time modeling of water table depth using a regionalized time series model and the Kalman Filter. Water Resources Research 37, 1277–1290.
| Space‐time modeling of water table depth using a regionalized time series model and the Kalman Filter.Crossref | GoogleScholarGoogle Scholar |
Boadu, F. K. (1997). Rock properties and seismic attenuation: neural network analysis. Pure and Applied Geophysics 149, 507–524.
| Rock properties and seismic attenuation: neural network analysis.Crossref | GoogleScholarGoogle Scholar |
Boretti, A., and Rosa, L. (2019). Reassessing the projections of the world water development report. NPJ Clean Water 2, 15.
| Reassessing the projections of the world water development report.Crossref | GoogleScholarGoogle Scholar |
Brandt, L., and Rawski, T. G. (2008). ‘China’s Great Economic Transformation.’ (Cambridge University Press.)
Brezak, D., Bacek, T., Majetic, D., Kasac, J., and Novakovic, B. (2012). A comparison of feed-forward and recurrent neural networks in time series forecasting. In ‘2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)’, 2012, pp. 1–6. (IEEE)
Brownlee, J. (2019). How to scale data for long short-term memory networks in Python. (Machine Learning Mastery.) Available at https://machinelearningmastery.com/how-to-scale-data-for-long-short-term-memory-networks-in-python/ [Verified 11 October 2021].
Chakamera, C., and Alagidede, P. (2018). The nexus between infrastructure (quantity and quality) and economic growth in Sub-Saharan Africa. International Review of Applied Economics 32, 641–672.
| The nexus between infrastructure (quantity and quality) and economic growth in Sub-Saharan Africa.Crossref | GoogleScholarGoogle Scholar |
Chen, S., and Hu, X. (2018). Individual identification using the functional brain fingerprint detected by the recurrent neural network. Brain Connectivity 8, 197–204.
| Individual identification using the functional brain fingerprint detected by the recurrent neural network.Crossref | GoogleScholarGoogle Scholar | 29634323PubMed |
Clawson, M., Landsberg, H. H., and Alexander, L. T. (1971). The agricultural potential of the middle east. In ‘The agricultural potential of the Middle East’. (Elsevier Science: New York, NY, USA.)
Connor, R. (2015). ‘The United Nations World Water Development Report 2015: Water for a Sustainable World.’ (UNESCO Publishing)
Coulibaly, P., and Baldwin, C. K. (2005). Nonstationary hydrological time series forecasting using nonlinear dynamic methods. Journal of Hydrology 307, 164–174.
| Nonstationary hydrological time series forecasting using nonlinear dynamic methods.Crossref | GoogleScholarGoogle Scholar |
FAO Land (1997). Irrigation potential in Africa; a basin approach. Land and Water Bulletin number 4. (Food and Agriculture Organization of the United Nations: Rome, Italy.) Available at http://www.fao.org/docrep/ W4347E/w4347e00.htm#Contents
Farajzadeh, J., Fard, A. F., and Lotfi, S. (2014). Modeling of monthly rainfall and runoff of Urmia Lake Basin using ‘feed-forward neural network’ and ‘time series analysis’ model. Water Resources and Industry 7–8, 38–48.
| Modeling of monthly rainfall and runoff of Urmia Lake Basin using ‘feed-forward neural network’ and ‘time series analysis’ model.Crossref | GoogleScholarGoogle Scholar |
Gad, W. A. (2017). Water scarcity in Egypt: causes and consequences. The IIOAB Journal 8, 40–47.
Ghose, D. K., Panda, S. S., and Swain, P. C. (2010). Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networks. Journal of Hydrology 394, 296–304.
| Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networks.Crossref | GoogleScholarGoogle Scholar |
Goher, M. E., Abdo, M. H., Mangood, A. H., and Hussein, M. M. (2015). Water quality and potential health risk assessment for consumption of Oreochromis niloticus from el-Bahr el-Pharaony Drain, Egypt. Fresenius Environmental Bulletin 24, 3590–3602.
Goulden, M., Conway, D., and Persechino, A. (2009). Adaptation to climate change in international river basins in Africa: a review. Hydrological Sciences Journal 54, 805–828.
| Adaptation to climate change in international river basins in Africa: a review.Crossref | GoogleScholarGoogle Scholar |
Hamouda, M. A., El-Din, M. M. N., and Moursy, F. I. (2009). Vulnerability assessment of water resources systems in the eastern Nile Basin. Water Resources Management 23, 2697–2725.
| Vulnerability assessment of water resources systems in the eastern Nile Basin.Crossref | GoogleScholarGoogle Scholar |
Herrera, M., Torgo, L., Izquierdo, J., and Pérez-García, R. (2010). Predictive models for forecasting hourly urban water demand. Journal of Hydrology 387, 141–150.
| Predictive models for forecasting hourly urban water demand.Crossref | GoogleScholarGoogle Scholar |
Houghton, E. (1996). ‘Climate Change 1995: the Science of Climate Change: Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel On Climate Change’ (Cambridge University Press: New York, NY, USA.)
Howe, C. W. (1976). The effects of water resource development on economic growth the conditions for success. Natural Resources Journal 16, 939–955.
Kenya National Bureau of Statistics (2019). 2019 Kenya population and housing census. Available at https://housingfinanceafrica.org/documents/2019-kenya-population-and-housing-census-reports/
Kheireldin, A. T. (2016). Using the water-energy-food nexus to enhance Egypt’s cooperation with Nile basin countries. Thesis, American University in Cairo, Sustainable Development. Available at http://dar.aucegypt.edu/handle/10526/4745
Kimenyi, M. S., and Mbaku, J. M. (2015). The limits of the new ‘Nile Agreement’. In ‘Africa in Focus’, 28 April 2015. (The Brookings Institution.) Available at https://www.brookings.edu/blog/africa-in-focus/2015/04/28/the-limits-of-the-new-nile-agreement/
Kingma D. P.Ba J. (2014 ).
Kliot, N., Shmueli, D., and Shamir, U. (2001). Institutions for management of transboundary water resources: their nature, characteristics and shortcomings. Water Policy 3, 229–255.
| Institutions for management of transboundary water resources: their nature, characteristics and shortcomings.Crossref | GoogleScholarGoogle Scholar |
Krogh, A., and Hertz, J. A. (1992). A simple weight decay can improve generalization. In ‘Advances in Neural Information Processing Systems’. Vol. 4, pp. 950–957. (Morgan Kaufmann Publishers: San Mateo, CA, USA.)
Kulshreshtha, S. N. (1998). A global outlook for water resources to the year 2025. Water Resources Management 12, 167–184.
| A global outlook for water resources to the year 2025.Crossref | GoogleScholarGoogle Scholar |
Kumar, D., Singh, A., Samui, P., and Jha, R. K. (2019). Forecasting monthly precipitation using sequential modelling. Hydrological Sciences Journal 64, 690–700.
| Forecasting monthly precipitation using sequential modelling.Crossref | GoogleScholarGoogle Scholar |
Kurup, P. U., and Dudani, N. K. (2002). Neural networks for profiling stress history of clays from PCPT data. Journal of Geotechnical and Geoenvironmental Engineering 128, 569–579.
| Neural networks for profiling stress history of clays from PCPT data.Crossref | GoogleScholarGoogle Scholar |
Lee, D., and Derrible, S. (2020). Predicting residential water demand with machine-based statistical learning. Journal of Water Resources Planning and Management 146, 04019067.
| Predicting residential water demand with machine-based statistical learning.Crossref | GoogleScholarGoogle Scholar |
McCracken, M., and Wolf, A. T. (2019). Updating the register of international river basins of the world. International Journal of Water Resources Development 35, 732–782.
| Updating the register of international river basins of the world.Crossref | GoogleScholarGoogle Scholar |
Mekonnen, M. M., and Hoekstra, A. Y. (2014). Water conservation through trade: the case of Kenya. Water International 39, 451–468.
| Water conservation through trade: the case of Kenya.Crossref | GoogleScholarGoogle Scholar |
Mumbi, A. W., and Fengting, L. (2020). Exploring changes in water use patterns, demand and stress along the Nile River Basin through the lens of Kenya and Egypt. Marine and Freshwater Research 71, 1478–1487.
| Exploring changes in water use patterns, demand and stress along the Nile River Basin through the lens of Kenya and Egypt.Crossref | GoogleScholarGoogle Scholar |
Nawi, N. M., Atomi, W. H., and Rehman, M. Z. (2013). The effect of data pre-processing on optimized training of artificial neural networks. Procedia Technology 11, 32–39.
| The effect of data pre-processing on optimized training of artificial neural networks.Crossref | GoogleScholarGoogle Scholar |
Nelson, P. J., Nyarangi, J., Maritim, Z. K., Teferi, T., Awer, M., Awale, B., Hamisi, S., Nkako, F. O., Gichangi, K., Kasanga, W., et al. (2012). The trans-boundary Mara River Basin strategic environmental assessment (MRB SEA). Report prepared for LVBC, WWF, USAID and the Governments of Tanzania and Kenya. (Earth Interactions.) Available at http://repository.eac.int/bitstream/handle/11671/708/The%20Trans-Boundary%20Mara%20River%20Basin%20Strategic%20Environmental%20Assessment.pdf?sequence=1&isAllowed=y [Verified 18 October 2021].
Nile Basin Initiative (2016). Building on shared benefits: transforming lives in the Nile Basin. (Nile Basin Initiative Secretariat (Nile-SEC): Entebbe, Uganda.) Available at https://www.nilebasin.org/index.php/our-story/95-keeping-the-nile-flowing-and-boosting-livelihoods-2
Oki, T., and Kanae, S. (2006). Global hydrological cycles and world water resources. The Science of the Total Environment 313, 1068–1072.
Pal, M. (2006). Support vector machines‐based modelling of seismic liquefaction potential. International Journal for Numerical and Analytical Methods in Geomechanics 30, 983–996.
| Support vector machines‐based modelling of seismic liquefaction potential.Crossref | GoogleScholarGoogle Scholar |
Perkins, D. H., and Rawski, T. G. (2008). Forecasting China’s economic growth over the next two decades. In ‘China’s Great Economic Transformation’ (Eds L. Brandt and T. G. Rawski.) Ch. 20, pp. 829–886. (Cambridge University Press.)
Pfister, S., Bayer, P., Koehler, A., and Hellweg, S. (2011). Projected water consumption in future global agriculture: Scenarios and related impacts. The Science of the Total Environment 409, 4206–4216.
| Projected water consumption in future global agriculture: Scenarios and related impacts.Crossref | GoogleScholarGoogle Scholar | 21840571PubMed |
Pranav, E., Kamal, S., Chandran, C. S., and Supriya, M. (2020). Facial emotion recognition using deep convolutional neural network. In ‘2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS)’, 6–7 March 2020, Coimbatore, India. pp. 317–320. (IEEE.)
Rahman, M. A. (2013). Water security: Ethiopia–Egypt transboundary challenges over the Nile River Basin. Journal of Asian and African Studies 48, 35–46.
| Water security: Ethiopia–Egypt transboundary challenges over the Nile River Basin.Crossref | GoogleScholarGoogle Scholar |
Savenije, H. H., and Van der Zaag, P. (2000). Conceptual framework for the management of shared river basins; with special reference to the SADC and EU. Water Policy 2, 9–45.
| Conceptual framework for the management of shared river basins; with special reference to the SADC and EU.Crossref | GoogleScholarGoogle Scholar |
Shenton, A. K. (2004). Strategies for ensuring trustworthiness in qualitative research projects. Education for Information 22, 63–75.
| Strategies for ensuring trustworthiness in qualitative research projects.Crossref | GoogleScholarGoogle Scholar |
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15, 1929–1958.
Steduto, P., Faurès, J.-M., Hoogeveen, J., Winpenny, J., and Burke, J. (2012). Coping with water scarcity: An action framework for agriculture and food security. Journal of FAO Water Reports 16, 78.
Swain, A. (1997). Ethiopia, the Sudan, and Egypt: the Nile River dispute. The Journal of Modern African Studies 35, 675–694.
| Ethiopia, the Sudan, and Egypt: the Nile River dispute.Crossref | GoogleScholarGoogle Scholar |
Tilman, D., Fargione, J., Wolff, B., D’antonio, C., Dobson, A., Howarth, R., Schindler, D., Schlesinger, W. H., Simberloff, D., and Swackhamer, D. (2001). Forecasting agriculturally driven global environmental change. The Science of the Total Environment 292, 281–284.
Tran, Q.-K., and Song, S.-K. (2017). Water level forecasting based on deep learning: a use case of Trinity River, Texas, the United States. Journal of KIISE 44, 607–612.
| Water level forecasting based on deep learning: a use case of Trinity River, Texas, the United States.Crossref | GoogleScholarGoogle Scholar |
Tsai, Y.-T., Zeng, Y.-R., and Chang, Y.-S. (2018). Air pollution forecasting using RNN with LSTM. In ‘2018 IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, 16th International Conference on Pervasive Intelligence and Computing, 4th International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech)’, 12–15 August 2018, Athens, Greece. pp. 1074–1079. (IEEE.) Available at http://cyber-science.org/2018/dasc/
van der Lugt, B. J., and Feelders, A. J. (2019). Conditional forecasting of water level time series with RNNS. In ‘International Workshop on Advanced Analysis and Learning on Temporal Data’. pp. 55–71. (Springer.)
Wada, Y., Flörke, M., Hanasaki, N., Eisner, S., Fischer, G., Tramberend, S., Satoh, Y., Van Vliet, M., Yillia, P., and Ringler, C. J. G. M. D. (2016). Modeling global water use for the 21st century: the water futures and solutions (WFAS) initiative and its approaches. Geoscientific Model Development 9, 175–222.
| Modeling global water use for the 21st century: the water futures and solutions (WFAS) initiative and its approaches.Crossref | GoogleScholarGoogle Scholar |
Wehling, P. (2020a). ‘Nile Water Rights.’ (Springer.)
Wehling, P. (2020b). The Nile and its catchment area. In ‘Nile Water Rights’. pp. 95–103. (Springer.)
Yue, Z., Alun, G., and Bolin, P. (2017). Relationship between industrial water consumption and economic growth in China based on environmental Kuznets curve. Energy Procedia 105, 3557–3564.
| Relationship between industrial water consumption and economic growth in China based on environmental Kuznets curve.Crossref | GoogleScholarGoogle Scholar |
Zhao, X., Fan, X., and Liang, J. (2017). Kuznets type relationship between water use and economic growth in China. Journal of Cleaner Production 168, 1091–1100.
| Kuznets type relationship between water use and economic growth in China.Crossref | GoogleScholarGoogle Scholar |
Zhou, S. L., McMahon, T. A., Walton, A., and Lewis, J. (2000). Forecasting daily urban water demand: a case study of Melbourne. Journal of Hydrology 236, 153–164.
| Forecasting daily urban water demand: a case study of Melbourne.Crossref | GoogleScholarGoogle Scholar |
Zubaidi, S. L., Ortega-Martorell, S., Al-Bugharbee, H., Olier, I., Hashim, K. S., Gharghan, S. K., Kot, P., and Al-Khaddar, R. (2020). Urban water demand prediction for a city that suffers from climate change and population growth: Gauteng Province case study. Water 12, 1885.
| Urban water demand prediction for a city that suffers from climate change and population growth: Gauteng Province case study.Crossref | GoogleScholarGoogle Scholar |
