ارزیابی مدل‌های هوش مصنوعی مبتنی بر درخت به منظور پیش‌بینی خطر سیل در بستر GIS

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی ارشد، گروه مهندسی نقشه برداری، دانشکده مهندسی نقشه ‎برداری و اطلاعات مکانی، دانشگاه تهران، تهران، ایران.

2 دانش‌آموخته کارشناسی ارشد مهندسی عمران آب و سازه‌های هیدرولیکی و عضو باشگاه پژوهشگران جوان و نخبگان، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران.

3 دانش ‎آموخته کارشناسی ارشد مهندسی سنجش از دور، دانشگاه تحصیلات تکمیلی صنعتی فناوری پیشرفته کرمان، ایران.

4 دانشیار گروه مهندسی عمران، دانشگاه بیرجند، بیرجند، ایران.

5 دانش ‎آموخته کارشناسی ارشد، گروه آب و سازه‌های هیدرولیکی، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران.

چکیده

سیل یکی از مخرب‌ترین انواع بلایای طبیعی است که هر ساله باعث از دست رفتن جان و مال انسان‌ها در سراسر جهان می‌شود. هدف از تحقیق حاضر ارزیابی و مقایسه قابلیت سه مدل یادگیری ماشین یعنی درخت بیز ساده (‏NBTree)‏، درخت تصمیم متناوب (ADTree) و جنگل تصادفی (‏RF) ‏برای پیش‌بینی خطر وقوع سیل در شهرستان مانه و سملقان می‌باشد. نوآوری تحقیق حاضر ارائه مدل‌های ترکیبی مبتنی بر درخت می‌باشد که کمتر در تحقیقات پیشین مورد استفاده قرار گرفته‌اند. برای تهیه نقشه مرجع سیل در منطقه موردمطالعه، 300 موقعیت مستعد سیل شناسایی شدند و از طریق انتخاب تصادفی با نسبت 70 به 30 به مجموعه داده‌های آموزشی و اعتبارسنجی تقسیم شدند. پایگاه‌ داده مکانی سیل با استفاده از 15 معیار هیدروژئولوژیکی و محیطی مؤثر بر سیل ایجاد شد. در نهایت، نقشه‌های پیش‌بینی خطر سیل با استفاده از مدل‌های NBTree، ADTree و RF تهیه شدند. به منظور اعتبار‌سنجی مدل‌های پیش‌بینی خطر سیل، معیار سطح زیر منحنی (‏AUC)‏ و معیارهای آماری نرخ پیش‌بینی مثبت، نرخ پیش‌بینی منفی، حساسیت، ویژگی و دقت مورد استفاده قرار گرفتند. نتایج نشان داد که مدل RF دقت بالاتری نسبت به مدل‌های NBTree و ADTree در پیش‌بینی خطر سیل منطقه موردمطالعه دارد. ​همچنین، نتایج نشان داد که احتمال وقوع خطر سیل در مناطق مرکزی منطقه موردمطالعه به دلیل ارتفاع و شیب کمتر، بیشتر از سایر مناطق است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation of Tree-Based Artificial Intelligence Models to Predict Flood Risk using GIS

نویسندگان [English]

  • Seyed Ahmad Eslaminezhad 1
  • Mobin Eftekhari 2
  • Saeid Mahmoodizadeh 3
  • Mohammad Akbari 4
  • Ali Haji Elyasi 5
1 M.Sc. Graduate, Department of Surveying Engineering, Faculty of Surveying Engineering and Spatial Information, University of Tehran, Tehran, Iran.
2 M.Sc. Graduate, Civil Engineering, Water and Hydraulic Structures, Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
3 M.Sc. Graduate, Department of Remote Sensing Engineering, Faculty of Surveying, University of Industrial and Technological Advanced Studies, Kerman, Iran.
4 Associate Professor at Department of Civil Engineering, University of Birjand, Birjand, Iran.
5 M.Sc. Graduate, Department of Water and Hydraulic Structure, K. N. Toosi University of Technology, Tehran, Iran.
چکیده [English]

Floods are one of the most devastating types of natural disasters that every year causes the loss of human lives and properties around the world. The purpose of this study is to evaluate and compare the capability of three machine learning models namely Naïve Bayes Tree (NBTree), Alternating Decision Tree (ADTree), and Random Forest (RF) to predict flood risk in Maneh and Samalqan county. The novelty of the present study is the presentation of tree-based hybrid models that have been less used in previous research. To prepare a flood reference map in the study area, 300 flood-prone locations were identified and were divided into training and validation data sets through random selection with a ratio of 70 to 30. The spatial database of the flood was created using 15 hydrogeological and environmental criteria affecting the flood. Finally, three flood risk prediction maps were generated using NBTree, ADTree, and RF models. To validate the flood risk predicting models, the Area Under the Curve (AUC) factor and the statistical criteria of Positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy were used. The results showed that the RF model had higher accuracy than the NBTree and ADTree models in predicting flood risk in the study area. The results also showed that the risk of flooding in the central areas of the study area is higher than other areas due to lower altitude and slope.

کلیدواژه‌ها [English]

  • Flood Prediction
  • Naïve Bayes Tree
  • Alternating Decision Tree
  • Random Forest
Ahmadlou M, Karimi M, Alizadeh S, Shirzadi A, Parvinnejhad D, Shahabi H, Panahi M (2018) Flood susceptibility assessment using integration of Adaptive Network-Based Fuzzy Inference System (ANFIS) and Biogeography-Based Optimization (BBO) and BAT Algorithms (BA). Geocarto International 34(11):1252-1272
Bui DT, Panahi M, Shahabi H, Singh VP, Shirzadi A, Chapi K, Ahmad AA (2018) Novel hybrid evolutionary algorithms for spatial prediction of floods. Scientific Reports 8(1):1-14
Bhowmick S, Eijkhout V, Freund Y, Fuentes E, Keyes D (2010) Application of alternating decision trees in selecting sparse linear solvers. In: K. Naono, K. Teranishi, J. Cavazos, R. Suda (Eds.), Software Automatic Tuning: From Concepts to State-of-the-Art Results. Springer New York, New York, NY, 153-173
Chapi K, Singh VP, Shirzadi A, Shahabi H, Bui D, Pham BT, Khosravi K (2017) A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modelling & Software 95:229-245
Chen H, Ito Y, Sawamukai M, Tokunaga T (2015) Flood hazard assessment in the Kujukuri Plain of Chiba Prefecture Japan based on GIS and multicriteria decision analysis. Natural Hazards 78(1):105-120
Chen W, Shirzadi A, Shahabi H, Ahmad BB, Zhang S, Hong H, Zhang N (2017) A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County China. Geomatics, Natural Hazards and Risk 8(2):1955-1977
Chen W, Hong H, Li S, Shahabi H, Wang Y, Wang X, Ahmad BB (2019) Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles. Journal of Hydrology 575:864-873
Chen W, Li Y, Xue W, Shahabi H, Li S, Hong H, Ahmad BB (2020) Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree and random forest methods. Science of the Total Environment 701:134979
Choubin B, Moradi E, Golshan M, Adamowski J, Sajedi-Hosseini F, Mosavi A (2019) An ensemble prediction of flood susceptibility using multivariate discriminant analysis classification and regression trees and support vector machines. Science of the Total Environment 651(Pt2):2087-2096
de Santana FB, de Souza AM, Poppi RJ (2018) Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 191:454-462
Du J, Fang J, Xu W, Shi P (2013) Analysis of dry/wet conditions using the standardized precipitation index and its potential usefulness for drought/flood monitoring in Hunan Province China. Stochastic Environmental Research and Risk Assessment 27(2):377-387
Eftekhari M, Eslaminezhad SA, Haji Elyasi A, Akbari M (2021) Geostatistical evaluation with Drinking Groundwater Quality Index (DGWQI) in Birjand Plain Aquifer. Environment and Water Engineering 7(2):268-279 (In Persian)
Eini M, Kaboli HS, Rashidian M, Hedayat H (2020) Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts. International Journal of Disaster Risk Reduction:101687
Farid DM, Zhang L, Rahman CM, Hossain MA, Strachan R )2014( Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks. Expert Systems with Applications 41:1937-1946
Hong H, Tsangaratos P, Ilia I, Liu J, Zhu AX, Chen W (2018) Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Science of the Total Environment 625:575-588
Imani S, Hassanoli S, Farkhnia A, Javadi F, Najafi M (2021) Evaluating the efficiency of WRF-Hydro Model for development of flood forecasting systems (Case study: Kashkan Watershed). Iran-Water Resources Research 16(4):225-240 (In Persian)
Jancewicz K, Migoń P, Kasprzak M (2019) Connectivity patterns in contrasting types of tablelandsandstone relief revealed by Topographic Wetness Index. Science of the Total Environment 656:1046-1062
Kanani-Sadat Y, Arabsheibani R, Karimipour F, Nasseri M (2019) A new approach to flood susceptibility assessment in data-scarce and ungauged regions based on GIS-based hybrid multi criteria decision-making method. Journal of Hydrology 572:17-31
Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR (2016) A GIS-based flood susceptibility assessment and its mapping in Iran: A comparison between frequency ratio and weights-ofevidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards 83(2):947-987
Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Dou J, Ly HB, Gróf G, Ho HL (2019) A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. Journal of Hydrology 573:311-323
Levy JK, Hartmann J, Li KW, An Y, Asgary A (2007) Multi-criteria decision support systems for flood hazard mitigation and emergency response in urban watersheds 1. Journal of the American Water Resources Association 43:346-358
Liu R, Chen Y, Wu J, Gao L, Barrett D, Xu T, Li L, Huang C, Yu J (2016) Assessing spatial likelihood of flooding hazard using naïve Bayes and GIS: A case study in Bowen Basin, Australia. Stochastic Environmental Research and Risk Assessment 30(6):1575-1590
Mukerji A, Chatterjee C, Raghuwanshi NS (2009) Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models. Journal of Hydrologic Engineering 14(6):647-652
Pham BT, Tien Bui D, Dholakia MB, Prakash I, Pham HV (2016) A comparative study of least square support vector machines and multiclass alternating decision trees for spatial prediction of rainfall-induced landslides in a tropical cyclones area. Geotechnical and Geological Engineering 34(6):1807-1824
Pham BT, Tien Bui D, Prakash I (2017) Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and j48 decision trees methods: A comparative study. Geotechnical and Geological Engineering 35(6):2597-2611
Pham BT, Avand M, Janizadeh S, Phong TV, Al-Ansari N, Ho LS, Jafari F (2020) GIS based hybrid computational approaches for flash flood susceptibility assessment. Water 12(683):1-30
Pourghasemi HR, Razavi-Termeh SV, Kariminejad N, Hong H, Chen W (2020) An assessment of metaheuristic approaches for flood assessment. Journal of Hydrology 582:124536
Quiroz JC, Mariun N, Mehrjou MR, Izadi M, Misron N, Mohd Radzi MA (2018) Fault detection of broken rotor bar in LS-PMSM using random forests. Measurement 116:273-280
Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto International 31(1):42-70
Razavi Termeh SV, Kornejady A, Pourghasemi HR, Keesstra S (2018) Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Science of the Total Environment 615:438-451
Saedi A, Saghafian B, Moazami S (2020) Uncertainty of flood forecasts via ensemble precipitation forecasts of seven NWP models for Spring 2019 Golestan Flood. Iran-Water Resources Research 16(1):347-359 (In Persian)
Sahoo GB, Schladow SG, Reuter JE (2009) Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models. Journal of Hydrology 378:325-342
Smith K, Ward R (1998) Floods: physical processes and human impacts. John Wiley and Sons Ltd
Tang X, Li J, Liu M, Liu W, Hong H (2020) Flood susceptibility assessment based on a novel random Naïve Bayes method: A comparison between different factor discretization methods. Catena 190:104536
Tehrany MS, Pradhan B, Jebur MN (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
Tien Bui D, Pradhan B, Nampak H, Bui QT, Tran QA, Nguyen QP (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
Wang LM, Li XL, Cao CH, Yuan SM (2006) Combining decision tree and Naive Bayes for classification. Knowledge-Based Systems 19(7):511-515
Wang S, Jiang L, Li C (2015) Adapting naive Bayes tree for text classification. Knowledge information system 44:77-89
Witten IH, Frank E, Mark AH (2011) Data mining: Practical machine learning tools and techniques. Third edition, Morgan Kaufmann, Burlington, USA
Zhao G, Pang B, Xu Z, Yue J, Tu T (2018) Mapping flood susceptibility in mountainous areas on a national scale in China. Science of the Total Environment 615:1133-1142