Abstract
Lack of potable water is a perennial problem in the day-to-day life of mankind around the world. The demand-supply variations have been on an increasing trend for so many years in different developing countries. To address this prevailing issue is the need of the hour for the society and the relevant government agencies. In this paper, as an explorative approach, we address this predominant issue in the form of an alternate solution which re-routes the course of the natural water sources, like rivers, through those areas, where the water supply is minimal in comparison with the demand, in a cost-effective and highly beneficial manner. Our analysis and discussions are more prone to Indian scenario where India is one of the worst affected fast developing countries for the water crisis. This involves the consideration of the physical, ecological and social characteristics of the lands on the route that fall under the course of the river and also the regions dependent on its flow. In order to understand and predict the optimized new flow paths to divert the water sources, we have employed Machine Learning algorithms like Multiple Regression and Multi-Swarm Optimization techniques. For selecting the most needed re-route, we have also considered the areas that are prone to droughts, and unite the re-routed water with the original course of the river, finally, draining into the sea, for the sustainable development. The proposed methodology is experimented by analyzing the flow areas (river basins) of river Mahanadi in India, one of the considerably important projects cited many times without any real implementation. The results are validated with the help of a study conducted earlier by the National Water Development Agency (NWDA), Government of India, in 2012.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
United Nations Development Programme Human Development Report 2006: Beyond Scarcity–Power, Poverty and the Global Water Crisis. Palgrave Macmillan, Basingstoke (2006)
Ghassemi, F., White, I.: Inter-basin water transfer: case studies from Australia, United States, Canada, China and India. Cambridge University Press (2007)
Anderson, J.R.: Machine learning: An artificial intelligence approach. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (Eds.), vol. 2. Morgan Kaufmann (1986)
Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems. In: Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26–30, 1989
Blackwell, T., Branke, J.: Multi-swarm optimization in dynamic environments. In: Raidl, G.R., et al. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 489–500. Springer, Heidelberg (2004)
Amarasinghe, U.: Water Policy Research: The National River Linking Project in India- some contentious issues. IWMI-TATA Water Policy Program (2012). www.iwmi.org/iwmi-tata/apm2012
Shukla, A.C., Vandana A.: Anatomy of interlinking rivers in india: a decision in doubt. Program in Arms Control, Disarmament, and International Security, University of Illinois at Urbana-Champaign (2005)
Amarasinghe, U., Shah, T., Malik, R.: Strategic analyses of the National River Linking Project (NRLP) of India. Series1. India’s Water Future: Scenarios and Issues. International Water Management Institute, New Delhi (2008)
Gosain, A.K., Aggarwal, P.K., Rao, S.: Linking water and agriculture in river basins: Impacts of climate change – Impact Assessment on Water Resources of Mahanadi River Basin. Civil Engineering Department, Indian Institute of Technology, Delhi (2011)
Mishra, N.: Macroscale Hydrological Modelling and Impact of land cover change on stream flows of the Mahanadi River Basin. Indian Institute of Remote Sensing, Diss (2008)
Telci, I.T., et al.: Real time optimal monitoring network design in river networks. World environmental & water resources congress (2008)
Karunanithi, N., et al.: Neural networks for river flow prediction. Journal of Computing in Civil Engineering 8(2) 201−220 (1994)
Dibike, Y.B., Solomatine, D.P.: River flow forecasting using artificial neural networks. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere 26(1), 1–7 (2001)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, vol. 1 (1995)
McCaffrey, J.: Test Run - Multi-Swarm Optimization. MSDN Magazine, September 2013
Narayan, G.H.: Water Scarcity and Security in India. India Water Portal (2012). http://www.indiawaterportal.org/articles/water-scarcity-and-security-india
Govt. of India. Background note for consultation meeting with Policy makers on review of National Water Policy. Ministry of Water Resources (2009)
Amarasinghe, U.A., Shah, T., Turral, H., Anand, B.K.: India’s water future to 2025-2050: Business as usual scenario and deviations. Research Report 123, IWMI (2007)
Freedman, D.A.: Statistical models: theory and practice. Cambridge university press (2009)
Eberhart, R.C., Yuhui, S.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1. IEEE (2000)
Orissa Watershed Development Mission, Agriculture Department, Orissa. Perspective and Strategic Plan for Watershed Development Projects, Orissa (2010-2025)
Joshi, N.M.: National River Linking Project of India. Hydro Nepal: Journal of Water, Energy and Environment 12, 13−19 (2013)
Mehta, D., Mehta, N.K.: Interlinking of Rivers in India: Issues & Challenges. GeoEcoMarina 19 (2013)
Central Water Commission. Water Data Complete Book. Central Water Commission, Ministry of Water Resources, Govt. of India, New Delhi, India ( 2005)
Country Policy Support Programme. Water Resources Assessment of Brahmani River Basin, India. International Commission on Irrigation and Drainage (ICID), New Delhi, India (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Saravanan, M., Sridhar, A., Nikhil Bharadwaj, K., Mohanavalli, S., Srividhya, V. (2015). River Network Optimization Using Machine Learning. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9142. Springer, Cham. https://doi.org/10.1007/978-3-319-20469-7_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-20469-7_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20468-0
Online ISBN: 978-3-319-20469-7
eBook Packages: Computer ScienceComputer Science (R0)