Investigating the Interrelationships between Hydro-Social Parameters in the Asian Continent Using Data Mining Methods

Document Type : Research Paper


1 Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

2 Department of Water Engineering, Imam Khomeini International University, Qazvin, Iran

3 Department of Irrigation and Reclamation Engineering,, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

4 Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran.


Water scarcity and subsequent social and economic crises have doubled the need to use new interdisciplinary knowledge-based approaches in reforming water resources management structures and policies and implementing water resources plans and projects. In the meantime, recognizing the interrelationships of the social sciences and their water resources systems has become a problem that its solution help us to correct existing dysfunctional structures. Therefore, the main purpose of this study is to prove the interrelationship of some social parameters and water resources on a continental scale with the use of software modeling tools. In this study, per capita data on renewable water resources and social parameters including the ratio of rural population to urban population, population density, number of Internet users and education index on an annual scale are considered. The statistical period of the data was 13 years (2005-2007) and this study was performed for 42 countries in Asia whose per capita water resources were declining. Then, using soft copmuting methods such as Artificial Neural Network (ANN), Decision Tree (M5) and Adaptive Fuzzy-neural Inference System (ANFIS), the interrelationship between per capita water resources and social parameters was modeled. Modeling results were evaluated by the criteria of determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) values. Finally, the results indicate the superior performance of ANFIS method compared to the other two models in evaluating the interaction of per capita water resources and social parameters. Also, after the ANFIS model, the M5 and ANN models had better performance, respectively.


Anusree, K. & Varghese, K. O. (2016). "Streamflow prediction of Karuvannur River Basin using ANFIS, ANN and MNLR models." Procedia Technology, 24, 101-108.
Biswas, A. K., & Seetharam, K. E. (2008). "Achieving Water Security for Asia: Asian water development outlook, 2007." International Journal of Water Resources Development, 24(1), 145-176.
Breiman  L,  Friedman  J,  Olshen  R,  Stone  C,  (1984) Classification  and Regression  Trees,Chapman& Hall/CRC  Press,  Boca  Raton,  FL. Development of a decision tree modeling approach .Geoderma 139, Pp. 277-287.
Carey, M., Baraer, M., Mark, B. G., French, A., Bury, J., Young, K. R.., & McKenzie, J. M. (2014). “Toward hydro-social modeling: Merging human variables and the social sciences with climate-glacier runoff models (Santa River, Peru).” Journal of Hydrology, 518, 60-70.
Diaz, M. E., Figueroa, R., Alonso, M. L. S., & Vidal-Abarca, M. R. (2018). “Exploring the complex relations between water resources and social indicators: The Biobío Basin (Chile).” Ecosystem Services, 31, 84-92.
Diep, L. (2018). “The liquid politics of an urban age.” Palgrave Communications, 4(1), 76.
Esmaeilzadeh, B., Sattari, M.T., & Samadianfard, S. (2017). Performance evaluation of ANNs and an M5 model tree in Sattarkhan Reservoir inflow prediction. ISH Journal of Hydraulic Engineering, 23(3), 283-292.
Forouzani, M., Karami, E., Zamani, G. H., & Moghaddam, K. R. (2013). “Agricultural water poverty: Using Q-methodology to understand stakeholders' perceptions.” Journal of arid environments, 97, 190-204.
Gehrig, J. & Rogers, M. M. (2009). "Water and conflict: incorporating peacebuilding into water development." PQ Publication, United States.
Hensel, P. R., Mitchell, S. M., & Sowers II, T. E. (2006). "Conflict management of riparian disputes." Political Geography, 25(4), 383-411.
Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). "Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence." IEEE Transactions on automatic control, 42(10), 1482-1484.
Kennedy, W. G., Hailegiorgis, A. B., Rouleau, M., Bassett, J. K., Coletti, M., Balan, G. C., & Gulden, T. (2010). "An Agent-Based Model of Conflict in East Africa and the Effect of Watering Holes." Proceedings of the 19th Conference on Behavior Representation in Modeling and Simulation, Charleston, SC, 21 - 24 March 2010
Khanna, T. (1990). Foundation of neural networks. Addison-Wesley Publishing Company. U.S.A. 327 pp.
Kişi, Ö. (2009). "Evolutionary fuzzy models for river suspended sediment concentration estimation." Journal of Hydrology, 372(1–4), 68-79.
Kuylenstierna, J. L., Bjorklund, G., & Najlis, P. (1997). "Sustainable water future with global implications: Everyone’s responsibility." Natural Resources Forum, 21(3), 181–190.
Lima, J. M. T., Valle, D., Moretto, E. M., Pulice, S. M. P., Zuca, N. L., Roquetti, D. R., ... & Branco, E. A. (2016). “A social-ecological database to advance research on infrastructure development impacts in the Brazilian Amazon.” Scientific data, 3, 160071.
Londhe, S.N. & Dixit, P.R. (2011). Forecasting stream flow using model trees. International Journal of Earth Sciences and Engineering, 4(6), 282-285.
Moret, B.M. (1982). Decision trees and diagrams. ACM Computing Surveys (CSUR), 14(4), 593-623.
Murthy, S.K. (1998). Automatic construction of decision trees from data: A multi-disciplinary survey. Data mining and knowledge discovery, 2(4), 345-389.
Nabezadeh, M., Mosaedi, A., Hessam, M., Dehghani, A. A., Zakerneya, M., & Holghi, M. (2012). "Investigating efficiency fuzzy logic to predict daily river flow." Iran-Watershed Management Science & Engineering, 5(17), 7-14.
Nourani, V., Komasi, M., and Mano, A. (2009). "A multivariate ANN-wavelet approach for rainfall–runoff modeling." Water resources management, 23(14), 2877.
Ortega-Reig, M., Palau-Salvador, G., Sempere, M. J. C., Benitez-Buelga, J., Badiella, D., & Trawick, P. (2014). "The integrated use of surface ground and recycled wastewater in adapting to drought in the traditional irrigation system of Valencia." Agricultural Water Management, 133, 55-64.
Pande, S. & Sivapalan, M. (2017). “Progress in sociohydrology: a metaanalysis of challenges and opportunities.” Wiley Interdisciplinary Reviews: Water, 4(4), e1193.
Radfard, M., Seif, M., Hashemi, A. H. G., 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.
Rezaei, A., Bozorg Haddad, O., & Zamanzad Ghavidel, S. (2019). A study of the trend and impact of changes in water resources on socio-economic parameters, the 6th National Conference on Applied Research in Civil Engineering, Architecture and Urban Management and the 5th Specialized Exhibition of Mass Housing Builders And Tehran Province Building, Tehran.
Safavian, S.R. & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics, 21(3), 660-674.
Sattari, M. T., Pal, M., Apaydin, H., & Ozturk, F. (2013). M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey. Water Resources, 40(3), 233-242.
Sobhani, R., Zamanzad Ghavidel, S., Rezaei, A., & Bozorg Haddad, O. (2019). Modeling and evaluation of socio-economic parameters in water resources, 14th National Conference on Watershed Management Science and Engineering of Iran, Urmia.
Talebizadeh, M. & Moridnejad, A. (2011). "Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models." Expert Systems with Applications, 38(4), 4126-4135.
Vorosmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., & Davies, P. M. (2010). "Global threats to human water security and river biodiversity." Nature, 467(7315), 555-561.
Zamanzad-Ghavidel, S. (2020). Interaction of Hydro-Socio-Knowledge Indicators in Integrated Water Resources Management, International Conference on Civil Engineering, Architecture, Development and Reconstruction of Urban Infrastructure in Iran, Tehran.