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Forecasting flood-prone areas using Shannon’s entropy model

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Abstract

With regard to the lack of quality information and data in watersheds, it is of high importance to present a new method for evaluating flood potential. Shannon’s entropy model is a new model in evaluating dangers and it has not yet been used to evaluate flood potential. Therefore, being a new model in determining flood potential, it requires evaluation and investigation in different regions and this study is going to deal with this issue. For to this purpose, 70 flooding areas were recognized and their distribution map was provided by ArcGIS10.2 software in the study area. Information layers of altitude, slope angle, slope aspect, plan curvature, drainage density, distance from the river, topographic wetness index (TWI), lithology, soil type, and land use were recognized as factors affecting flooding and the mentioned maps were provided and digitized by GIS environment. Then, flood susceptibility forecasting map was provided and model accuracy evaluation was conducted using ROC curve and 30% flooding areas express good precision of the model (73.5%) for the study area.

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References

  • Abdideh M, Qorashi M, Rangzan M and Aryan M 2011 Assessment of relative active tectonics using morphometric analysis, case study of Dez river (southwestern Iran); Scientific Quart. J. Geosci. 80 33–46.

    Google Scholar 

  • Aniya M 1985 Landslide-susceptibility mapping in the Amahata river basin, Japan; ANN Assoc. Am. Geogr. 75 102–114.

    Article  Google Scholar 

  • Bednarik M, Magulova B, Matys M and Marschalko M 2010 Landslide susceptibility assessment of the Kralovany–Liptovsky Mikulaš railway case study; Phys. Chem. Earth 35 162–171.

    Article  Google Scholar 

  • Campolo M, Soldati A and Andreussi P 2003 Artificial neural network approach to flood forecasting in the River Arno; Hydrol. Sci. J. 48 381–398.

    Article  Google Scholar 

  • Constantin M, Bednarik M, Jurchescu M C and Vlaicu M 2011 Landslide susceptibility bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania); Environ. Earth Sci. 63 397–406.

    Article  Google Scholar 

  • Hosseini M and Matlabifar F 2008 Study and investigation of flood management and solutions to reduce flood damages, Sepehr (Geographical Organization), 16th year; 63.

  • Hosseinpour A and Abbaspour R A 2014 Optimization of landslide forecasting results using Shannon’s entropy theory; Knowl. Hazards 2 253–268.

    Google Scholar 

  • Jaafari A, Najafi A, Pourghasemi H R, Rezaeian J and Sattarian A 2013 GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran; Int. J. Environ. Sci. Technol., doi:10.1007/s13762-013-0464-0.

  • Kawachi T, Maruyama T and Singh V P 2001 Rainfall entropy for delineation of water resources zones in Japan; J. Hydrol. 246 (1) 36–44.

    Article  Google Scholar 

  • Kia M B, Pirasteh S, Pradhan B, Mahmud A R, Sulaiman W N A and Moradi A 2012 An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia; Environ. Earth Sci. 67 251–264.

    Article  Google Scholar 

  • Lee M J, Kang Je and Jeon S 2012 Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. IGARSS; IEEE Int., doi:10.1109/IGARSS.2012.6351414.

  • Li C, Singh V P and Mishra A K 2012 Entropy theory-based criterion for hydrometric network evaluation and design: Maximum information minimum redundancy; Water Resour. Res. 48 (5).

  • Liu Y and De Smedt F 2005 Flood modeling for complex terrain using GIS and remote sensed information; Water Resour. Manag. 19 605–624.

    Article  Google Scholar 

  • Manandhar B, Balla M K, Awal R and Pradhan B M 2010. Floodplain analysis and risk assessment of lothar khola (stream); MSc Thesis, Tribhuvan University, Phokara, Nepal, 64p.

  • Mishra A K and Coulibaly P 2010 Hydrometric network evaluation for Canadian watersheds; J. Hydrol. 380 (3) 420–437.

    Article  Google Scholar 

  • Mishra A K and Coulibaly P 2014 Variability in Canadian seasonal streamflow information and its implication for hydrometric network design; J. Hydrol. Eng. 19 (8) 05014003.

    Article  Google Scholar 

  • Mishra A K, Özger M and Singh V P 2009 An entropy-based investigation into the variability of precipitation ; J. Hydrol. 370 (1) 139–154.

    Article  Google Scholar 

  • Naghibi S A, Pourghasemi H R, Pourtaghie Z S and Rezaei A 2014 Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran; Earth Sci. Inform. 8 (1) 171–186.

    Article  Google Scholar 

  • Ozdemir A and Altural T 2013 A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey; J. Asian Earth Sci. 64 180– 197.

    Article  Google Scholar 

  • Pourghasemi H R, Mohammady M and Pradhan B 2012 Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran; Catena 97 71–84.

    Article  Google Scholar 

  • Pourghasemi H R, Pradhan B, Gokceoglu C and Moezzi K D 2013a A comparative assessment of prediction capabilities of Dempster-Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS; Geomat. Nat. Hazards 4 93–118.

  • Pourghasemi H R, Pradhan B, Gokceoglu C, Mohammadi M and Moradi H R 2013b Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran; Arab. J. Sci. Eng. 6 2351–2365.

  • Pourghasemi H R, Jirandeh A G, Pradhan B, Xu C and Gokceoglu C 2013c Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran; J. Earth Syst. Sci. 122 349–369.

  • Pourghasemi H R, Moradi H R and Aghda S M F 2013d Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances; Nat. Hazards 69 749–779.

  • Pourghasemi H R, Moradi H R and Fatemi Aghda S M 2014 Prioritizing factors affecting landslide and zonation of its susceptibility using Shannon’s entropy index; Soil Water Sci. 70 181–191.

    Google Scholar 

  • Pradhan B and Lee S 2010 Landslide susceptibility assessment and factor effect analysis: Back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling; Environ. Model. Softw. 25 747–759.

    Article  Google Scholar 

  • Rahmati O, Haghizadeh A and Stefanidis S 2016a Assessing the accuracy of GIS-based analytical hierarchy process for watershed prioritization; Gorganrood River Basin, Iran; Water Resour. Manag. 30 (3) 1131–1150.

  • Rahmati O, Haghizadeh A, Pourghasemi H R and Noormohamadi F 2016b Gully erosion susceptibility mapping: The role of GIS-based bivariate statistical models and their comparison; Nat. Hazards 82 (2) 1231–1258.

  • Rahmati O, Pourghasemi H R and Zeinivand H 2016c Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran; Geocarto Int. 31 (1) 42–70.

  • Rajsekhar D, Mishra A K and Singh V P 2012 Regionalization of drought characteristics using an entropy approach; J. Hydrol. Eng. 18 (7) 870–887.

    Article  Google Scholar 

  • Regmi A D, Devkota K C, Yoshida K, Pradhan B, Pourghasemi H R, Kumamoto T and Akgun A 2013 Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in central Nepal Himalaya; Arab. J. Sci. Eng. 7 725–742.

    Google Scholar 

  • Regmi N R, Giardino J R and Vitek J D 2010 Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA; Geomorphol. 115 172–187.

    Article  Google Scholar 

  • Sanyal J and Lu X X 2004 Application of remote sensing in flood management with special reference to monsoon Asia: A review; Nat. Hazards 33 283–301.

    Article  Google Scholar 

  • Shafapour Tehrani M, Lee M J, Pradhan B and Neamah Jebur M 2013 Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS ; J. Hydrol. 504 69–79.

    Article  Google Scholar 

  • Shafapour Tehrani M, Lee M J, Pradhan B, Neamah Jebur M and Lee S 2014 Flood susceptibility mapping using integrated bivariate and multivariate statistical models; J. Hydrol. 512 332–343.

    Article  Google Scholar 

  • Singh V P 1997 The use of entropy in hydrology and water resources; Hydrol. Process. 11 587–626.

    Article  Google Scholar 

  • Skilodimou H, Livaditis G, Bathrellos G and Verikiouâ Papaspiridakou E 2003 Investigating the flooding events of the urban regions of Glyfada and Voula, Attica, Greece: A contribution to urban geomorphology; Geogr. Ann. A 85 197–204.

    Article  Google Scholar 

  • Tahmassebipoor N, Rahmati O, Noormohamadi F and Lee S 2016 Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing; Arab. J. Geosci. 9 (1) 1–18.

    Article  Google Scholar 

  • Zare’ Garizi A, Brady Sheikh V, Saad Eddin A and Salman Mahini A 2012 Applications logistic regression modeling to change the spatial pattern of vegetation (Case study: Watershed Chehelchay, Golestan Province); Geogr. Space 37 55–68.

    Google Scholar 

  • Zare’ M, Pourghasemi H R, Vafakhah M and Pradhan B 2013 Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: A comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms; Arab. J. Sci. Eng. 6 2873–2888.

    Google Scholar 

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Acknowledgements

This research was supported by Department of Watershed Management Engineering, Lorestan University. We thank our colleagues from Lorestan University who provided insight and expertise that greatly assisted the research.

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Authors and Affiliations

  1. Department of Watershed Management Engineering, Faculty of Agriculture, Lorestan University, Lorestan, Iran

    Ali Haghizadeh, Safoura Siahkamari & Omid Rahmati

  2. Department of Water Engineering, Faculty of Agriculture, Lorestan University, Lorestan, Iran

    Amir Hamzeh Haghiabi

Authors
  1. Ali Haghizadeh
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  2. Safoura Siahkamari
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  3. Amir Hamzeh Haghiabi
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  4. Omid Rahmati
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Correspondence to Ali Haghizadeh.

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Corresponding editor: Rajib Maity

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Haghizadeh, A., Siahkamari, S., Haghiabi, A.H. et al. Forecasting flood-prone areas using Shannon’s entropy model. J Earth Syst Sci 126, 39 (2017). https://doi.org/10.1007/s12040-017-0819-x

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  • DOI: https://doi.org/10.1007/s12040-017-0819-x

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