Natural Hazards

, Volume 84, Issue 3, pp 1905–1920 | Cite as

Application of AHP with GIS in drought risk assessment for Puruliya district, India

  • Moumita Palchaudhuri
  • Sujata Biswas
Original Paper


The analytic hierarchy process (AHP) in combination with geographic information system (GIS) provides an effective means for disaster monitoring. In the present study, GIS was used for the spatial analysis of drought for the Puruliya district, West Bengal, India. Fourteen parameters, such as annual rainfall, monthly rainfall, maximum temperature, monthly temperature, maximum evapotranspiration, monthly evapotranspiration, relative humidity, soil texture, landuse/landcover, slope, groundwater, cultivators, agricultural labourers and population were chosen and thematic maps for each parameter were developed. AHP was adopted to develop pairwise comparison matrices and to calculate the weightage factors of each parameter. Fourteen thematic maps were analysed, integrated and finally drought severity map was generated using GIS. The results showed that 70 % of the total area of Puruliya district is under severe drought affecting about 14 blocks. The output map thus obtained will provide information on severity of drought vulnerability which has practical relevance to agricultural importance and help in combating drought.


Analytic Hierarchy Process Geographical Information System Disaster monitoring Drought 



The authors would like to thank all the organisations for providing the required data for the study as mentioned in the paper and also thankful to Council of Scientific and Industrial Research (CSIR), India, for providing financial support in the form of fellowship.


  1. Ahamed TRN, Rao KG, Murthy JSR (2000) GIS-based fuzzy membership model for crop-land suitability analysis. Agric Syst 63(2):75–95CrossRefGoogle Scholar
  2. Basu M, Hoshino S, Hashimoto S (2015) Many issues, limited responses: coping with water insecurity in rural India. Water Resour Rural Dev 5:47–63CrossRefGoogle Scholar
  3. Chen YR, Yeh CH, Yu B (2011) Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan. Nat Hazards 59(3):1261–1276CrossRefGoogle Scholar
  4. Chopra P (2006) Drought risk assessment using remote sensing and GIS: a case study of Gujarat. Thesis (ms), International Institute for Geo-information Science and Earth Observation, The NetherlandsGoogle Scholar
  5. Dalezios NR, Blanta A, Spyropoulos NV, Tarquis AM (2014) Risk identification of agricultural drought for sustainable agroecosystems. Nat Hazards Earth Syst Sci 14(9):2435–2448CrossRefGoogle Scholar
  6. Demir G, Aytekin M, Akgun A, Ikizler S, Tatar O (2013) A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards 65(3):1481–1506CrossRefGoogle Scholar
  7. Dutta D, Kundu A, Patel NR (2013) Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index. Geocarto Int 28(3):192–209CrossRefGoogle Scholar
  8. Eastman JR, Weigen J, Kyem PAK, Toledano J (1995) Raster procedure for multi-criteria/multi-objective decisions. Photogramm Eng Remote Sens 61(5):539–547Google Scholar
  9. FAI (2012–13) Fertiliser statistics. The Fertiliser Association of India, New DelhiGoogle Scholar
  10. Feizizadeh B, Blaschke T (2013) GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran. Nat Hazards 65(3):2105–2128CrossRefGoogle Scholar
  11. Gouveia C, Trigo RM, DaCamara CC (2009) Drought and vegetation stress monitoring in Portugal using satellite data. Nat Hazards Earth Syst Sci 9(1):185–195CrossRefGoogle Scholar
  12. GoWB (2012a) West Bengal State Action Plan on Climate Change (WB SAPCC). Government of West Bengal, IndiaGoogle Scholar
  13. GoWB (2012b) District Human Development Report, Human Development Coordination and Research Centre (HDRCC). Development and Planning Department, Government of West Bengal, IndiaGoogle Scholar
  14. Gu Y, Brown JF, Verdin JP, Wardlow B (2007) A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophys Res Lett 34, L06407. doi: 10.1029/2006GL029127
  15. Hasekiogullari G, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Nat Hazards 63(2):1157–1179CrossRefGoogle Scholar
  16. Joerin F, Theriault M, Musy A (2001) Using GIS and outranking multicriteria analysis for land-use suitability assessment. Int J Geogr Inf Sci 15(2):153–174CrossRefGoogle Scholar
  17. Legesse G, Suryabhagavan K (2014) Remote sensing and GIS based agricultural drought assessment in East Shewa Zone, Ethiopia. Trop Ecol 55(3):349–363Google Scholar
  18. Malczewski J (1999) GIS and multicriteria decision analysis. Wiley, New YorkGoogle Scholar
  19. Malczewski J (2006) GIS based multicriteria decision analysis: a survey of the literature. Int J Geogr Inf Sci 20(7):703–726CrossRefGoogle Scholar
  20. Muthumanickam D, Kannan P, Kumaraperumal R, Natarajan S, Sivasamy R, Poongodi C (2011) Drought assessment and monitoring through remote sensing and GIS in western tracts of Tamil Nadu, India. Int J Remote Sens 32(18):5157–5176CrossRefGoogle Scholar
  21. PACS (2008) Drought in India: challenges and initiatives.
  22. Palchaudhuri M, Biswas S (2013) Analysis of meteorological drought using standardized precipitation index—a case study of Puruliya district, West Bengal. Int J Environ Chem Ecol Geol Geophys Eng 7(3):119–126Google Scholar
  23. Pandey S, Pandey AC, Nathawat MS, Kumar M, Mahanti NC (2012) Drought hazard assessment using geoinformatics over parts of Chotanagpur plateau region, Jharkhand, India. Nat Hazards 63(2):279–303CrossRefGoogle Scholar
  24. Patel NR, Yadav K (2015) Monitoring spatio-temporal pattern of drought stress using integrated drought index over Bundelkhand region, India. Nat Hazards 77(2):663–677CrossRefGoogle Scholar
  25. Pereira JMC, Duckstein L (1993) A multiple criteria decision-making approach to GIS-based land suitability evaluation. Int J Geogr Inf Syst 7(5):407–424CrossRefGoogle Scholar
  26. Prakash PHS, Garg PK, Ghosh SK (2006) GIS based modeling for drought assessment. In: 26th Annual ESRI international user conferenceGoogle Scholar
  27. Prathumchai K, Honda K (2001) Drought risk evaluation using remote sensing and GIS: a case study in Lop Buri Province. In: 22nd Asian conference on remote sensingGoogle Scholar
  28. Rojas O, Vrieling A, Rembold F (2011) Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sens Environ 115(2):343–352CrossRefGoogle Scholar
  29. Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15(3):234–281CrossRefGoogle Scholar
  30. Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26CrossRefGoogle Scholar
  31. Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1:83–98Google Scholar
  32. Sadeghravesh MH, Khosravi H, Ghasemian S (2015) Application of fuzzy analytical hierarchy process for assessment of combating-desertification alternatives in central Iran. Nat Hazards 75(1):653–667CrossRefGoogle Scholar
  33. SAFE (2011) Community-ecosystem approach for adaptive watershed management in drought-prone tribal areas of West Bengal.
  34. Shamsipour AA, Reza PZ, Panah SKA, Azizi G (2011) Analysis of drought events for the semi-arid central plains of Iran with satellite and meteorological based indicators. Int J Remote Sens 32(24):9559–9569CrossRefGoogle Scholar
  35. Shang J, Sueyoshi T (1995) A unified framework for the selection of a flexible manufacturing system. Eur J Oper Res 85(2):297–315CrossRefGoogle Scholar
  36. Stefanidis S, Stathis D (2013) Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP). Nat Hazards 68(2):569–585CrossRefGoogle Scholar
  37. Store R, Jokimaki J (2003) A GIS-based multi-scale approach to habitat suitability modeling. Ecol Model 169(1):1–15CrossRefGoogle Scholar
  38. Sun Z, Zhang J, Zhang Q, Hu Y, Yan D, Wang C (2014) Integrated risk zoning of drought and waterlogging disasters based on fuzzy comprehensive evaluation in Anhui Province, China. Nat Hazards 71(3):1639–1657CrossRefGoogle Scholar
  39. Vasiliades L, Loukas A, Liberis N (2011) A water balance derived drought index for Pinios River Basin, Greece. Water Resour Manag 25(4):1087–1101CrossRefGoogle Scholar
  40. WMO (1999) Comprehensive risk assessment for natural hazards. World Meteorological Organisation (WMO)/TD No. 955Google Scholar
  41. Ying X, Zeng GM, Chen GQ, Tang L, Wang KL, Huang DY (2007) Combining AHP with GIS in synthetic evaluation of eco-environment quality—a case study of Hunan Province, China. Ecol Model 209(24):97–109CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of Engineering Science and TechnologyHowrahIndia

Personalised recommendations