Identification of the Most Important Environmental Variables in Spatial Prediction of Flood Prone Areas using the Maximum Entropy Model in Parts of Golestan Province

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

2 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

3 water Dept., kermanshah branch, islamic azad university, kermanshah, iran

Abstract

Flood is a destructive natural hazard over the recent years. In the current study, the maximum entropy model as an advanced data mining model was used to model and provide the spatial maps of flood prone areas in Saliantapeh Watershed, Golestan Province, with an area of 4515.47 km2. For this purpose, the flood inventory map was prepared based on available reports and field surveys. Then, 13 effective variables including the digital elevation model, slope percent, slope direction, rainfall, distance from drainage network, land use, lithology, soil texture, plan curvature, profile curvature, topographic moisture index, drainage density and flow capacity index were identified and introduced to the model. After that, three different series of  flood risk points (i.e. ds1, ds2, and ds3) including 70% for training and 30% for validation of the model were randomly prepared to evaluate the accuracy and robustance of the model based on the ROC Index. The results showed that the maximum entropy model with high accuracy (above 90%) predicted flood prone areas. Moreover, in this study, the degree of importance of the variables was investigated by the model and the results demonstrated that the two factors of the drainage density (about 49% of importance) and distance from the streamflow (about 15% of importance) were detected as the most important environmental factors affecting flood in the studied area.

Keywords


Abdi, P. (2006).Study of flooding potential of Zanjan Roud using SCS method and GIS, national committee of irrigation and drainage. Coexistence with flood technical workshop (in Persian).
Angileri, S.E., Conoscenti, C., Hochschild, V., Märker, M., Rotigliano, E., and Agnesi, V. (2016). Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River basin (Sicily, Italy). Geomorphology, 262, 61-76.
Asqari Moghadm, M.R. (2005). Water and urban habitat, Sara Publication, P. 135 (in Persian).
Avni, Y. (2005). Gully incision as a key factor in desertification in an arid environment, the Negev highlands, Israel. Catena, 63, 185– 220.
Azareh, A., Rahmati, O., Rafiei-Sardooi, E., Sankey, J.B., Lee, S., Shahabi, H., and Ahmad, B.B. (2019). Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models. Science of the Total Environment, 655, 684-696.
Bui, D.T., Pradhan, B., Nampak, H., Bui, Q.T., Tran, Q.A., and Nguyen, Q.P. (2016). Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS. Journal of Hydrology, 540, 317-330.
Bui, D.T., Khosravi, K., Shahabi, H., Daggupati, P., Adamowski, J.F., Melesse, A., Pham, B.T., Pourghasemi, H.R., Mahmoodi, M., Bahrami, S., Pradhan, B., Shirzadi, A., Chapi, K., and Lee, s. (2019). Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model. Remote Sensing, 11(13), 1-27.
Bordoni, M., Meisina, C., Valentiono R., Bittelli M., and Chersich, S. (2015). Site-specific to local-scale shallow landslides triggering zones assessment using TRIGRS. Natural Hazards Earth System Sciences, 15(5):1025-1050
Chen, W., Li, Y., Xue, W., Shahabi, H., Li, S., Hong, H., and Ahmad, B.B. (2020). Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree, and random forest methods. Science of The Total Environment, 701, 134-979.
Conoscenti, C., Angileri, S., Cappadonia, C., Rotigliano, E., Agnesi, V., and Märker, M. (2014). Gully erosion susceptibility assessment by means of GIS-based logistic regression: a case of Sicily (Italy). Geomorphology, 204, 399-411.
[CONRWMGP] Central Office of Natural Resources and Watershed Management in Golestan Province. (2009). Detailed action plan. Iran. pages. 230.
Dickie, J.A., and Parsons, A.J. (2012). Eco‐geomorphological processes within grasslands, shrublands and badlands in the semi‐arid Karoo, South Africa. Land Degradation Dev, 23(6), 534-547.
Demir, G., Aytekin, M., & Akgun, A. (2015). Landslide susceptibility mapping by frequency ratio and logistic regression methods: an example from Niksar–Resadiye (Tokat, Turkey). Arabian Journal of Geosciences, 8(3), 1801-1812.
Felicĺsimo, Á., Cuartero, A., Remondo, J., and Quirόs, E. (2013). Mapping landslide susceptibility with logistiv regression, multiple adaptive regression splines, classification and regression tress, amd maximum entropy methods: a comparative study. Landslides, 10, 175-189.
Fernández, D.S., and Lutz, M.A. (2010). Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Engineering Geology, 111, 90–98.
Glenn, E., Morino, K., Nagler, P., Murray, R., Pearlstein, S. and Hultine, K. (2012). Roles of saltcedar (Tamarix spp.) and capillary rise in salinizing a non-flooding terrace on a flow-regulated desert river. Journal of Arid environment, 79, 56-65.
Guzzetti, F., Cardinali, M., Reichenbach, P., and Carrara, A. (2000). Comparing landslide maps: A case study in the upper Tiber River Basin, central Italy. Environmental management, 25(3), 247-263.
Gomez, H. and Kavzoglu, T. (2005). Assessment of shallow landslide susceptibility using artificial neural networks in jabonosa River Basin, Venezuela. Engineering Geology, 78(1): 11-27.
Gray, D.H. and Leiser, A.T. (1982). Biotechnical slope protection and erosion control. Van Nostrand Reinhold, New York,
Gallardo-Cruz, JA., Pérez-García, EA. and Meave, J.A. (2009). β-Diversity and vegetation structure as influenced by slope aspect and altitude in a seasonally dry tropical landscape. Landsc Ecol, 24:473–482.
Hosmer, D.W. (2000). Wiley series in probability and statistics, Chap. 2. Multiple logistic regression. Applied logistic regression, 31-46.
Hong, H., Pourghasemi, H.R. and Piurtaggi, Z.S. (2016). Landslide susceptibility assessment in Lianhua County (China): a comparison between arandom forest data mining technique and bivariate and multivariate statistical models. Geomorphologe, 259:105-118.
Javidan, N., Kavian, A., Pourghasemi, H.R., Conoscenti, C. and Jafarian, Z. (2020). Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios. Water, 11(11), 231-9.
Khosravi, K., Nohani, E., Maroufinia, E. and Pourghasemi, H.R. (2016). A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards, 83(2), 947-987.
Konrad, C.P. and Booth, D.B. (2005). Hydrologic Changes in Urban Streams and Their Ecological Significance, American Fisheries Society Symposium, Alaska, 11-15 September, 47, 157-177.
Kornejady, A., Ownegh, M. and Bahremand, A. (2017). Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena. 152, 144-162.
Lee, S., Kim, J.C., Jung, H.S., Lee, M.J. and Lee, S. (2017). Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics, Natural Hazards and Risk, 8(2), 1185-1203.
Lee, S. and Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4, 33-41.
Lotfi, E., Asadollahi Shahir, M. and Abbasi, M. (2014).Frequency analysis of occurrence and damage caused by flood in time and spatial scales in Golestan Province. 10th national conference of watershed engineering sciences, Azad University of Maraqe Branch, 175-186 (in Persian).
Marmion, M., Hjort, J., Thuiller, W. and Luoto, M. (2008). A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland. Earth surface processes and landforms, 33(14), 2241-2254,
Moghaddam, D.D., Pourghasemi, H.R. and Rahmati, O. (2019). Assessment of the Contribution of Geo-environmental Factors to Flood Inundation in a Semi-arid Region of SW Iran: Comparison of Different Advanced Modeling Approaches. In Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques. Springer, Cham, 59-78.
Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N. and Ghazali, A.H.B. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk, 8(2), 1080-1102.
Moradi, H. and Hosseini, S.M. (2006). Study of effective factors in flood production of Golestan, natural resources confrence and sustainable development in Southern region of Caspian Sea, Nour, Islamic Azad University of Nour branch, 165-176 (in Persian).
Mancini, F., Ceppi, C. and Ritrovato, G. (2010). GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy. Natural Hazards and Erth System Sciences, 10(9): 1851.
Meinhardt, M., Fink, M. and Tunschel, H. (2015). Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology, 234: 80–97.
Moore, I.D., Grayson, R.B. and Ladson, A.R. (1991). Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process, 5:3-30.
Nematollah, H., Vafakhah, M. and Najafi, A. (2017). Development of Urban Flood Hazard Map for Nour City Using Analytical Hierarchy Process and Fuzzy Logic. Journal of Watershed Management Research. 7(14), 19-11 (in Persian).
Ohlmacher, G.C. (2007). Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Engineering Geology, 91(2):117-134.
Papaioannou, G., Vasiliades, L. and Loukas, A. (2015). Multi-criteria analysis framework for potential flood prone areas mapping. Water Resources Management, 29(2), 399-418.
Park, N.W. (2015). Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets. Environmental Earth Sciences, 73(3), 937-949.
Pourghasemi, H.R., Moradi, H. and Fatemioghdas, M. (2012). Landslide susceptibility mapping using adaptive neuro-fuzzy inference system in north of Tehran. Earth science Research, 3(10), 63-78 (in Persian(.
Pourtaghi, Z.S. and Pourghasemi, H.R. (2014). GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeology Journal, 22(3), 643-662.
Rahi, G. (2017). Prediction of ditch erosion sensitivity using data mining-spatial methods, PhD thesis, natural resources engineering faculty, Sari agricultural sciences and natural resources university, P. 225 (in Persian).
Rahmati, O., Naghibi, S.A., Shahabi, H., Bui, D.T., Pradhan, B., Azareh, A., Rafiei-Sardooi, E., Samani, A.N. and Melesse, A.M. (2018). Groundwater spring potential modelling: comprising the capability and robustness of three different modeling approaches. Journal of Hydrology, 565, 248–261
Rahmati, O., Pourghasemi, H.R. and Melesse, A.M. (2016b). Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region, Iran. Catena, 137, 360-372.
Rahmati, O., Pourghasemi, H.R. and Zeinivand, H. (2015). Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto International, 31(1), 42-70.
Rahmati, O., Zeinivand, H. and Besharat, M. (2016a). Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomatics, Natural Hazards and Risk, 7(3), 1000-1017.
Shirzadi, E. (2017). Prediction of ground surfaces movements around Bijar City using data-mining advanced approaches, Ph.D. thesis, natural resources engineering faculty, Sari agricultural sciences and natural resources University, P. 236 (in Persian).
Siahkamari, S., Haghizadeh, A., Zeinivand, H., Tahmasebipour, N. and Rahmati, O. (2018). Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto international, 33(9), 927-941.
Sidel, R.C. and Ochiai, H. (2006). Landslides: Processes, Prediction, and Land use, Water Resource Monograph: 18, AGU books, ISSN: 0170-9600.
Tehrany, M.S., Pradhan, B. and Jebur, M.N. (2013). Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504, 69-79.
Tehrany, M., Lee, M.J., Pradhan, B., Jebur, M.N. and Lee, S. (2014a). Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci, 72: 4001–4015.
Tehrany, M., Pradhan, B. and Jebur, M.N. (2014b). Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol, 512:332–343.
Tehrany, M.S., Pradhan, B., Mansor, S. and Ahmad, N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, 125, 91-101.
Vorpahl, P., Elsenbeer, H., Mӓrker, M. and Schröder, B. (2012). How can statistical models help to determine driving factors of landslides? Ecological Modelling, 239, 27-39.
Vandekerckhove, L., Poesen, J. and Govers, G. (2003). Medium-term gully headcut retreat rates in Southeast Spain determined from aerial photographs and ground measurements. Catena, 50: 329-352.
Wilson, J.P. and Gallant, J.C. (2000). Terrain analysis: principles and applications. John Wiley and Sons.
Walter, S.D. (2002). Properties of the summary receiver operating characteristic (SROC) curve for diagnostic test data. Stat Med. 21, 1237–1256.
Yalcin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena, 72, 1–12.
Yessilnacar, E.K. (2005). The application of computational intelligence of landslide susceptibility mapping in Turkey. Ph. D Thesis Department of Geomatics the University of Melbourne. 423 pages.
Yost, A.C., Petersen, S.L., Gregg, M. and Miller, R. (2008). Predictive modeling and mapping sage grouse (Centrocercus urophasianus) nesting habitat using Maximum Entropy and a long-term dataset from Southern Oregon. Ecological Informatics, 3(6), 375-386.
Xu, C., Dai F., Xu, X. and Lee, Y.H. (2012). GIS-based support vector mechine modeling of earthquake-triggered landslide susceptibility in the jianjiand River watershed, China. Geomorphology, 145:70-80.