Spatial Prediction of Flood Susceptable Areas in Karkheh Watershed of Lorestan Province Using the Combined Random Forest – Weight of Evidence Model

Document Type : Research

Authors

1 Ph.D. Student, Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

2 Assistant Professor, Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran

3 Assistant Professor, Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

Introduction and goal
The phenomenon of flood is one of the frequent hazards that have caused financial losses and many lives in Iran in the last decade. One of the three main natural hazards of Iran is the occurrence of floods, and it is safe to say that a large flood occurs at least once a year in some part of this country. Floods are considered as a great threat to human life by damaging or killing people and animals, especially buildings and houses, agricultural land and crop production, urban infrastructure, bridges and roads. Floods in Iran cause a lot of damage from an economic point of view; it causes destruction of the environment, natural and residential resources and loss of life. In the past years, about 70% of the annual credits of the plan to reduce the effects of natural disasters and the headquarters of unexpected events have been used to compensate for the damages caused by floods. In this research, by using flood location data and using machine learning models and random forest data mining and the weight of evidence and their combination, the spatial prediction of flood prone areas has been discussed.
Materials and methods
In this research, combined models and 11 predicting variables of flood probability in Karkheh watershed located in Lorestan province have been used. These variables include maps of geomorphometric indicators, including topographic humidity index, relative slope position and topographic position index, hydrological maps including: drainage density and distance from the drainage network, for this purpose, first, data mining models for the initial analysis of the relationships between environmental variables and events Past risks are used and their results are used as input data for machine learning models. Flood data were randomly divided into two groups: training (70%) and validation (30%). Prediction accuracy was evaluated using operating characteristic curve (ROC) method.
Results and discussion
According to the results of the random forest model, the accuracy was 0.904, the weight of evidence was 0.886, and the combined model of random forest - the weight of evidence was 0.978. Based on the combined model of random forest and the weight of evidence as the superior model, 20.49% of the surface has medium upward potential. Based on the random forest model, drainage density, distance from waterways, height and land use were the most important factors affecting flood potential.

Keywords

References

Arnal L, Ramos MH, Coughlan de Perez E, Louise Cloke H, Stephens E, Wetterhall F, Jan van Andel S, Pappenberger F. 2016. Willingness-to-pay for a probabilistic flood forecast: A risk-based decision-making game. Hydrology and Earth System Sciences, 20(8): 3109-3128.
Corsini A, Cervi F, Ronchetti F. 2009. Weight of evidence and artificial neural networks for potential groundwater spring mapping: an application to the Mt. Modino area (Northern Apennines, Italy). Geomorphology, 111(1-2): 79–87.
Darabi  H, Rahmati O, Naghibi SA, Mohammadi F, Ahmadisharaf E, Kalantari Z, Torabi Haghighi A, Soleimanpour SM, Tiefenbacher JP, Tien Bui D. 2022. Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood. Geocarto International, 37(19): 5716-5741.
Dehghanian N, Mousavi Nadoushani SS, Saghafian B, Rayati Damavandi M. 2020. Evaluation of coupled ANN-GA model to prioritize flood source areas in ungauged watersheds. Hydrology Research, 51(3): 423-442.
Deng S, Liu S, Mo X. 2020. Assessment of Three Common Methods for Estimating Terrestrial Water Storage Change with Three Reanalysis Datasets. Journal of Climate, 33(2): 11–525.
Donyaii A, Sarraf A. P, Mardanifar M. 2020. Evaluation of Crisis Resolution Strategies for Groundwater Revival Plan Using Fuzzy Best – Worst Muti Criteria Decision Model. Journal of Hydraulic Structures. 6 (3): 21-44.
Donyaii A, Sarraf AP, Ahmadi H. 2021. Optimization of reservoir dam operation using gray wolf, crow search and whale algorithms based on the solution of the nonlinear programming model. JWSS-Isfahan University of Technology. 24(4):
159-175.
Donyaii A, Sarraf A. P, Ahmadi H. 2021. Multi-Objective Optimal Utilization Policy of Boostan Dam Reservoir Using Whale and NSGA-II Algorithms Based on Game Theory and Shannon Entropy Method. Iranian Water Researches Journal. 14(4):
99-111.
Felicĺsimo Á, Cuartero A, Remondo J, Quirόs E. 2013. Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression tress, and maximum entropy methods: A comparative study. Landslides, 10,(11): 175-189.
Kazemi R, Porhemmat J. 2021. An Analysis of the factors Affecting Flooding Severity in Iran. Watershed Management Research Journal, 34(1): 59-73.
Lee S, Kim JC, Jung HS, Lee MJ, 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, pp. 1-19.
Mirzaee N, Sarraf A. P. 2022. Application of data fusion models in river flow simulation using signals of large-scale climate, case study: Jiroft Dam Basin, Watershed Engineering and Management. 13(4): 672-689.
Moghaddam DD, Pourghasemi HR, 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, pp. 59-78.
Poudyal CP, Chang C, Oh HJ, lee S. 2010. Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environmental Earth Sciences, 61(5): 1049-1064.
Porhemmat J. 2016. A model on investigation on flood hazard over watersheds of Iran. Iranian Journal of Watershed Management and Science, 10(34): 1-15.
Pradhan B, Neamah Jebur M, Zulhaidi Mohd Shafri H, Shafapour Tehrany M. 2015. Data fusion technique using wavelet transform and Taguchi methods for automatic landslide detection from airborne laser scanning data and quickbird satellite imagery, IEEE Transactions on Geoscience and remote sensing. 54(3): 1610-1622.
Rahmati O, Tahmasebipour N, Pourghasemi HR. 2015. Sub-watershed flooding prioritization using morphometric and correlation analysis (Case study: Golestan Watershed). Iranian journal of Ecohydrology, 2(2): 151-161.
Rahmati O, Pourghasemi HR. 2017. Identification of critical flood prone areas in data-scarce and ungauged regions: A comparison of three data mining models. Water Resources Management, 31(5): 1473-1487.
Sampson CC, Smith AM, Bates PD, Neal JC, Alfieri L, Freer JE. 2015. A high‐resolution global flood hazard model. Water Resource Research, 51(9): 358-7381.
Sarraf A. P, Mohagheghzadeh G, Mohagheghzadeh N. 2017. Flood Hydraulic Modeling and its Deliniation Using Orthophoto Pictures in Shapourkhesht River located in Kazeroun City. Geographic Space, 17(57): 175-194.
Satarzadeh E, Sarraf A. P, Hajikandi H, Sadeghian MS. 2022. Flood hazard mapping in western Iran: assessment of deep learning vis‑à‑vis machine learning models. Natural Hazard, 111(2): 1355-1373.
Shafapour Tehrany M, Kumar L, Neamah Jebur M, Shabani F. 2019. Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geomatics, Natural Hazards and Risk, 10(1): 79-101.
Siahkamari S, Haghizadeh A, Zeinivand H, Tahmasebipour N, Rahmati O. 2018. Spatial prediction of flood susceptible areas using frequency ratio and maximum entropy models. Geocarto International, 33(9): 927-941.
Soleimanipour M, Sarraf A. P. 2020. Evaluating the effects of climate change on Lar Basin Water Resources Using SWAT Model and comparing its results with Bayesian Networks and Hybrid Intelligent Models. Natural Geography 13 (50): 61-79. (In Persian).
Zamanirad M, Sedghi H, Sarraf A. P, Saremi A, Rezaee P. 2018. Potential Impacts of Climate change on groundwater levels on the Kerdi-Shirazi plain, Iran. Environmental Earth Sciences. 77(11): 1-10.
Zamanirad M, Sarraf A. P, Sedghi H, Saremi A, Rezaee P. 2019. Modeling the influence of Groundwater Exploitation on Land Subsidence Susceptibility Using Machine Learning Algorithms. Natural Resources Research, 29 (1): 127-1141.

Files

History

  • Receive Date: 10 July 2022
  • Revise Date: 25 August 2022
  • Accept Date: 21 September 2022

Share

How to cite

Statistics