Priority Assessment of Sub-watershed Based on Optimum Number of Parameters Using Fuzzy-AHP Decision Support System in the Environment of RS and GIS

  • C. D. Mishra
  • R. K. Jaiswal
  • A. K. Nema
  • V. K. Chandola
  • Arpit ChoukseyEmail author
Research Article


Identification for planning of land and water resource management based on efficient decision-making tool is very important for providing appropriate weightage in stressed site. In the present study, fuzzy analytical hierarchy process (FAHP) with different erosion hazards parameters (EHPs) have been used as a pronouncement for identification of naturally stressed sub-watershed in Nagwan watershed of Hazaribagh district in Jharkhand, India. In fuzzy-AHP, analytical hierarchy process (AHP) builds a hierarchy (ranking) of decision items using comparisons between each pair of items expressed as a matrix with fuzziness. Paired comparisons produce weighting scores that measure how much importance items and criteria have with each other and checking the consistency of the decision. In this study, the Nagwan watershed was divided in 21 sub-watershed which varies from 2.34 to 7 km2 and all EHPs of sub-watersheds have been computed using remote sensing and GIS. From the study, it has been observed that best consistency ratio has been found when using 13 parameters that is 9.44 with narrow trapezoidal shape. Each morphometric parameter was ranked with respect to the value and weightage obtained by deriving the relationships between the morphometric parameters obtained through classification of the SW by associating the strength of fuzzy analytical hierarchy processes (FAHP). By this weight, the results revealed that the priorities in five categories, out of 21 sub-watershed 19 and 24% sub-watersheds qualify for very high and high priority, whereas 57% sub-watersheds fall under medium, low and very low priority.


Erosion hazard parameter (EHP) Saaty’s analytical hierarchical process (SAHP) Soil loss Sediment yield Sediment production rate (SPR) Watershed prioritization 


  1. Black, P. E. (2005). Watershed hydrology. Hoboken: Wiley.CrossRefGoogle Scholar
  2. Buckley, J. J. (1985). Fuzzy herarchical analysis. Journal of Fuzzy Sets and System, 34, 187–195.CrossRefGoogle Scholar
  3. Chopra, R., Dhiman, D. R., & Sharma, P. K. (2005). Morphometric analysis of sub-watersheds in Gurdaspur district, Punjab using remote sensing and GIS techniques. Journal of the Indian Society of Remote Sensing, 33(4), 532–539.CrossRefGoogle Scholar
  4. Chow, V. T. (1964). Handbook of applied hydrology. Section, 8, 61.Google Scholar
  5. Chowdary, V. M., Chakraborthy, D., Jeyaram, A., Krishna Murthy, Y. V. N., Sharma, J. R., & Dadhwal, V. K. (2013). Multi-criteria decision making approach for watershed prioritization using hierarchy process technique and GIS. Journal of Water Resource Management, 27(10), 3555–3571. Scholar
  6. Deng, H. (1999). Multicriteria analysis with fuzzy pairwise comparisons. International Journal of Approximate Reasoning, 21, 215–231.CrossRefGoogle Scholar
  7. El-Swaify, S. A., Dangler, E. W., & Armstrong, C. L. (1982). Soil erosion by water in the tropics, edit (p. 173). Honolulu: HITAHR, University of Hawai.Google Scholar
  8. Erensal, Y. C., Oncan, T., & Demircan, M. L. (2006). Determining key capabilities in technology management using fuzzy analytic hierarchy process: A case study of Turkey. Journal of Information Sciences, 176, 2755–2770.CrossRefGoogle Scholar
  9. Essiet, E. (1990). A comparison of soil degradation under small holder farming and large-scale irrigation land use in Kano State, northern Nigeria. Wiley Online Library Land Degradation & Development, 2, 209–214.CrossRefGoogle Scholar
  10. Han, W. J., & Tsay, W. D. (1998). Formulation of quality strategy using analytic hierarchy process. In Twenty seven annual meeting of the western decision science institute (pp. 580–583), University of Northern Colorado, USA.Google Scholar
  11. Hansen, H. S. (2005). GIS-based multi-criteria analysis of wind farm development. In H. Hauska and H. Tveite (Eds.), Proceedings of the 10th scandinavian research conference on geographical information science (ScanGIS’2005) (pp. 75–87). Stockholm: Royal Institute of Technology.Google Scholar
  12. Hlaing, T. K., Haruyama, S., & Maung, A. M. (2008). Using GIS-based distributed soil loss modeling and morphometric analysis to prioritize watershed for soil conservation in Bago river basin of Lower Myanmar. Journal of Earth Science, 2(4), 465–478.Google Scholar
  13. Jaiswal, R. K., Dehariya, D. K., Nema, A. K., Thomas, T., & Galkate, R. V. (2012). Soil erosion based prioritization and development of CAT plan for catchment of Rangawan reservoir in Bundelkhand region of Madhya Pradesh, India. In National symposium on water resource management in changing environ (pp. 409–420). Roorkee, India, 8–9 February, 2012.Google Scholar
  14. Jaiswal, R. K., Ghosh, N. C., Lohani, A. K., & Thomas, T. (2015). Fuzzy AHP Based Multi Crteria Decision Support for Watershed Prioritization. Water Resources Management, 29(12), 4205–4227CrossRefGoogle Scholar
  15. Jaiswal, R. K., Thomas, T., Galkate, R. V., Ghosh, N. C., & Singh, S. (2014). Watershed prioritization using Saaty’s AHP based decision support for soil conservation measures. Journal of Water Resource Management, 28(2), 475–494. Scholar
  16. Javed, A., Khanday, M. Y., & Ahmed, R. (2009). Prioritization of sub-watersheds based on morphometric and land use analysis using remote sensing and GIS techniques. Journal of the Indian Society of Remote Sensing, 37, 261–274.CrossRefGoogle Scholar
  17. Jose, C. S., & Das, D. C. (1982). Geomorphic prediction models for sediment production rate and inter soil erosion properties of watersheds in Mayurakshi catchment. In Proceeding international symposium hydrological aspects of mountainous watersheds (Vol. 1, pp. 15–33). Roorkee.Google Scholar
  18. Khan, M. A., Gupta, V. P., & Moharanam, P. C. (2001). Watershed prioritization using remote sensing and geographical information system: A case study from Guhiya India. Journal of Arid Environment, 49, 465–475.CrossRefGoogle Scholar
  19. Kordi, M. (2008). Comparison of fuzzy and crisp analytic hierarchy process (AHP) methods for spatial multicriteria decision analysis in GIS. Master Thesis, Department of Technology and Built Environment, University of Galve, pp. 1–45.Google Scholar
  20. Malczewski, J. (1999). GIS and multicriteria desision analysis. New York: Wiley.Google Scholar
  21. Mikhailov, L., & Tsvetinov, P. (2004). Evaluation of services using a fuzzy analytic hierarchy process. Journal of Applied Soft Computing, 5, 23–33.CrossRefGoogle Scholar
  22. Mishra, S. S., & Nagarajan, R. (2010). Morphometric analysis and prioritization of sub-watersheds using GIS and remote sensing techniques: A case study of Odisha, India. International Journal of Geomatics and Geoscience, 1(3), 501–510.Google Scholar
  23. Mishra, N., Satyanarayan, T., & Mukherjee, R. K. (1984). Effect of topo elements on the sediment production rate from sub-watersheds in upper Damodar valley. Journal of the Indian society of Agricultural Engineers (ISAE), 21(3), 65–70.Google Scholar
  24. Nag, S. K., & Chakraborty, S. (2003). Influence of rock types and structures in the development of drainage network in hard rock area. Journal of the Indian Society of Remote Sensing, 1, 25–35.CrossRefGoogle Scholar
  25. Oyatoye, E. O., Okpokpo, G. U., & Adekoya, G. A. (2010). An application of analytic hierarchy process (AHP) to investment portfolio selection in the banking sectors of the Nigerian capital market. Journal of Economics International Finance, 2(12), 321–335.Google Scholar
  26. Pandey, A., Chowdary, V. M., & Mal, B. C. (2007). Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing. Journal of Water Resource Management, 21, 729–746.CrossRefGoogle Scholar
  27. Patel, D. P., Dholakia, M. B., Naresh, N., & Srivastava, P. K. (2012). Water harvesting structure positioning by using geo-visualization concept and prioritization of mini-watersheds through morphometric analysis in the Lower Tapi Basin. Journal of the Indian Society of Remote Sensing, 40(2), 299–312CrossRefGoogle Scholar
  28. Rao, H. S. S., & Mahabaleswara, H. (1990). Prediction of rate of sedimentation of Tungabhadra Reservoir. In Proceeding of symposium on erosion, sedimentation & resource conservation (Vol. 1, pp. 12–20). Dehradun.Google Scholar
  29. Saaty, T. L. (1980). Fundamentals of decision making and priority theory with analytical hierarchical process (Vol. 4, pp. 3–95). Pittusburgh: RWS Publications University of Pittsburgh.Google Scholar
  30. Sharma, J. C., Prasad, J., Saha, S. K., & Pande, L. M. (2001). Watershed prioritization based on sediment yield index in eastern part of Don Valley using RS and GIS. Indian Journal of Soil Conservation, 29(1), 7–13.Google Scholar
  31. Sharma, S. K., Rajput, G. S., Tignath, S., & Pandey, R. P. (2010). Morphometric analysis and prioritization of a watershed using GIS. Journal of Indian Water Resource Society, 30(2), 33–39.Google Scholar
  32. Shrimali, S. S., Aggarwal, S. P., & Samra, J. S. (2001). Prioritizing erosion-prone areas in hills using remote sensing and GIS: A case study of the Sukhna lake catchment, northern India. International Journal Applied Earth Observation Geoinformatics, 3(1), 54–60.CrossRefGoogle Scholar
  33. Sidhu, G. S., Das, T. H., Singh, R. S., Sharma, R. K., & Ravishankar, T. (1998). Remote sensing and GIS techniques for prioritization of watershed: A case study in upper Mackkund watershed, Andhra Pradesh. Indian Journal of Soil Conservation, 2(3), 71–75.Google Scholar
  34. Smith, K. G. (1950). Standards for grading textures of erosional topography. American Journal of Science, 248, 655–668.CrossRefGoogle Scholar
  35. Tam, C. M., Tong, T. K. L., Leung, A. W. T., & Chiu, G. W. C. (2002). Site layout planning using nonstructural fuzzy decision support system. Journal of Construction Engineering Management, 128(3), 220–228. Scholar
  36. Vittala, S. S., Govindaiah, S., & Gowda, H. H. (2004). Morphometric analysis of sub-watersheds in the Pavagada area of Tumkar district, south India using remote sensing and GIS techniques. Journal of the Indian Society Remote Sensing, 32(4), 351–362.CrossRefGoogle Scholar
  37. Wischmeier, W. H., & Smith, D. P. (1978). Predicting rainfall erosion losses-a guide to conservation planning. In Agriculture hand-book No 537 (pp. 58–61). US Department Agriculture, Washington DC.Google Scholar
  38. Yadav, S. K., Singh, S. K., Gupta, M., & Srivastava, P. K. (2014). Morphometric analysis of upper tons basin from northern foreland of peninsular India using CARTOSAT satellite and GIS. Geocarto International J ournal. Scholar
  39. Yahaya, S., Ahamd, N., & Abdalla, R. F. (2010). Multicriteria analysis for flood vulnerable areas in Hadejia-Jama’are river basin, Nigeria. European Journal of Scientific Research, 42(1), 72–83.Google Scholar
  40. Yoshino, K., & Ishioka, Y. (2005). Guidelines for soil conservation towards integrated basin management for sustainable development: A new approach based on the assessment of soil loss risk using remote sensing and GIS. Journal of Water Environment, 3, 235–247.CrossRefGoogle Scholar
  41. Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.CrossRefGoogle Scholar
  42. Zimmermann, H. J. (1934). Fuzzy set theory—and its applications (3rd ed.). Dordrecht: Kluwer Academic Publishers.Google Scholar

Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  1. 1.Institute of Agriculture SciencesBanaras Hindu UniversityVaranasiIndia
  2. 2.Regional CenterNational Institute of HydrologyBhopalIndia
  3. 3.Indian Institute of Remote SensingDehradunIndia

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