Performance evaluation of artificial intelligence paradigms—artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction

Flood prediction has gained prominence world over due to the calamitous socio-economic impacts this hazard has and the anticipated increase of its incidence in the near future. Artificial intelligence (AI) models have contributed significantly over the last few decades by providing improved accuracy and economical solutions to simulate physical flood processes. This study explores the potential of the AI computing paradigm to model the stream flow. Artificial neural network (ANN), fuzzy logic, and adaptive neuro-fuzzy inference system (ANFIS) algorithms are used to develop nine different flood prediction models using all the available training algorithms. The performance of the developed models is evaluated using multiple statistical performance evaluators. The predictability and robustness of the models are tested through the simulation of a major flood event in the study area. A total of 12 inputs were used in the development of the models. Five training algorithms were used to develop the ANN models (Bayesian regularization, Levenberg Marquardt, conjugate gradient, scaled conjugate gradient, and resilient backpropagation), two fuzzy inference systems to develop fuzzy models (Mamdani and Sugeno), and two training algorithms to develop the ANFIS models (hybrid and backpropagation). The ANFIS model developed using hybrid training algorithm gave the best performance metrics with Nash-Sutcliffe Model Efficiency (NSE) of 0.968, coefficient of correlation (R2) of 97.066%, mean square error (MSE) of 0.00034, root mean square error (RMSE) of 0.018, mean absolute error (MAE) of 0.0073, and combined accuracy (CA) of 0.018, implying the potential of using the developed models for flood forecasting. The significance of this research lies in the fact that a combination of multiple inputs and AI algorithms has been used to develop the flood models. In summary, this research revealed the potential of AI algorithm-based models in predicting floods and also developed some useful techniques that can be used by the Flood Control Departments of various states/regions/countries for flood prognosis.

[1]  PAUL J. WERBOS,et al.  Generalization of backpropagation with application to a recurrent gas market model , 1988, Neural Networks.

[2]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[3]  M. J. Hall,et al.  Artificial neural networks as rainfall-runoff models , 1996 .

[4]  Christian W. Dawson,et al.  An artificial neural network approach to rainfall-runoff modelling , 1998 .

[5]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[6]  Klaus-Peter Holz,et al.  Rainfall-runoff modelling using adaptive neuro-fuzzy systems , 2001 .

[7]  Asaad Y. Shamseldin,et al.  A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi–Sugeno fuzzy system , 2001 .

[8]  Christian W. Dawson,et al.  Hydrological modelling using artificial neural networks , 2001 .

[9]  A. Robinson Fayek,et al.  Application of fuzzy logic to forecast seasonal runoff , 2003 .

[10]  K.-Peter Holz,et al.  Short-term water level prediction using neural networks and neuro-fuzzy approach , 2003, Neurocomputing.

[11]  K. P. Sudheer,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[12]  P. C. Nayak,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[13]  R. Hozalski,et al.  Zone of influence of a gas permeable membrane system for delivery of gases to groundwater , 2005 .

[14]  Stefano Alvisi,et al.  Water level forecasting through fuzzy logic and artificial neural network approaches , 2005 .

[15]  K. P. Sudheer,et al.  Short‐term flood forecasting with a neurofuzzy model , 2005 .

[16]  Asaad Y. Shamseldin,et al.  Development of rainfall–runoff models using Takagi–Sugeno fuzzy inference systems , 2006 .

[17]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[18]  Hubert H. G. Savenije,et al.  Hydrological model coupling with ANNs , 2006 .

[19]  R. Abrahart,et al.  Flood estimation at ungauged sites using artificial neural networks , 2006 .

[20]  Yen-Ming Chiang,et al.  Multi-step-ahead neural networks for flood forecasting , 2007 .

[21]  Seyed Mahmood Kashefipour,et al.  Application of Fuzzy Systems and Artificial Neural Networks for Flood Forecasting , 2007 .

[22]  M. Firat,et al.  Comparison of Artificial Intelligence Techniques for river flow forecasting , 2008 .

[23]  C. Shu,et al.  Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system , 2008 .

[24]  Peter Brevern,et al.  GUI Based Mamdani Fuzzy Inference System Modeling To Predict Surface Roughness in Laser Machining , 2009 .

[25]  Narendra Singh Raghuwanshi,et al.  Flood Forecasting Using ANN, Neuro-Fuzzy, and Neuro-GA Models , 2009 .

[26]  Florian Pappenberger,et al.  Ensemble flood forecasting: a review. , 2009 .

[27]  Mehmet Özger,et al.  Comparison of fuzzy inference systems for streamflow prediction , 2009 .

[28]  Kwok-Wing Chau,et al.  Data-driven models for monthly streamflow time series prediction , 2010, Eng. Appl. Artif. Intell..

[29]  V. Kale,et al.  Climate change and the precipitation variations in the northwestern Himalaya: 1866–2006 , 2010 .

[30]  H. Cai.  Flood forecasting on the Humber River using an artificial neutral network approach , 2010 .

[31]  Jan Adamowski,et al.  Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms , 2010 .

[32]  A. K. Lohani,et al.  Development of Flood Forecasting System Using Statistical and ANN Techniques in the Downstream Catchment of Mahanadi Basin, India , 2010 .

[33]  Faridah Othman,et al.  Reservoir inflow forecasting using artificial neural network , 2011 .

[34]  L. Mpallas,et al.  Comparison between Neural Networks and Adaptive Neuro-fuzzy Inference System in Modeling Lake Kerkini Water Level Fluctuation Lake Management using Artificial Intelligence , 2011 .

[35]  Deepika Yadav,et al.  Stream flow forecasting using Levenberg-Marquardt algorithm approach , 2011 .

[36]  Edward Keedwell,et al.  Urban flood prediction in real-time from weather radar and rainfall data using artificial neural networks , 2011 .

[37]  Hirad Abghari,et al.  Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions , 2013, Arabian Journal of Geosciences.

[38]  Riccardo Rossi,et al.  Comparison of fuzzy-based and AHP methods in sustainability evaluation: a case of traffic pollution-reducing policies , 2012, European Transport Research Review.

[39]  Özgür Kisi,et al.  Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff , 2013, Appl. Soft Comput..

[40]  Mukesh Tiwari,et al.  Comparison of multi-objective evolutionary neural network, adaptive neuro-fuzzy inference system and bootstrap-based neural network for flood forecasting , 2013, Neural Computing and Applications.

[41]  A. Arabi Yazdi,et al.  Flood flow forecasting using ANN, ANFIS and regression models , 2013, Neural Computing and Applications.

[42]  A. W. Jayawardena,et al.  A comparative study of fuzzy logic systems approach for river discharge prediction. , 2014 .

[43]  Dushyant Patel,et al.  Flood Forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS) , 2014 .

[44]  S. Sorooshian,et al.  Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information , 2014 .

[45]  Zaw Zaw Latt,et al.  Improving Flood Forecasting in a Developing Country: A Comparative Study of Stepwise Multiple Linear Regression and Artificial Neural Network , 2014, Water Resources Management.

[46]  Shakil Ahmad Romshoo,et al.  Assessing the influence of watershed characteristics on the flood vulnerability of Jhelum basin in Kashmir Himalaya , 2015, Natural Hazards.

[47]  E. Perera,et al.  Fuzzy logic based flood forecasting model for the Kelantan River basin, Malaysia , 2015 .

[48]  M. Imteaz,et al.  Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals , 2016, Climate Dynamics.

[49]  Shakil Ahmad Romshoo,et al.  Assessing the influence of watershed characteristics on the flood vulnerability of Jhelum basin in Kashmir Himalaya: reply to comment by Shah 2015 , 2015, Natural Hazards.

[50]  K. Anusree,et al.  Streamflow Prediction of Karuvannur River Basin Using ANFIS, ANN and MNLR Models , 2016 .

[51]  S. Sorooshian,et al.  A high resolution coupled hydrologic-hydraulic model (HiResFlood-UCI) for flash flood modeling , 2016 .

[52]  A. Aydin,et al.  Flood forecasting using transboundary data with the fuzzy inference system: The Maritza (Meriç) River , 2016 .

[53]  Yifei Sun,et al.  Comparison of a fuzzy control and the data-driven model for flood forecasting , 2016, Natural Hazards.

[54]  Samira Rashetnia.  Flood Vulnerability Assessment by Applying a Fuzzy Logic Method: A Case Study from Melbourne , 2016 .

[55]  G. Karatzas,et al.  A national scale flood hazard mapping methodology: The case of Greece - Protection and adaptation policy approaches. , 2017, The Science of the total environment.

[56]  A. Josa,et al.  Floods and consequential life cycle assessment: integrating flood damage into the environmental assessment of stormwater Best Management Practices , 2017 .

[57]  Dieu Tien Bui,et al.  A novel hybrid artificial intelligence approach for flood susceptibility assessment , 2017, Environ. Model. Softw..

[58]  Syed Mustakim Ali Shah,et al.  Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River , 2017 .

[59]  Nikolay O. Nikitin,et al.  Urban Pluvial Flood Forecasting using Open Data with Machine Learning Techniques in Pattani Basin , 2017 .

[60]  S. Charlesworth,et al.  Opportunity mapping of natural flood management measures: a case study from the headwaters of the Warwickshire-Avon , 2018, Environmental Science and Pollution Research.

[61]  Lloyd H.C. Chua,et al.  A fully-online neuro-fuzzy model for flow forecasting in basins with limited data , 2017 .

[62]  Xiaowen Ding,et al.  Two-stage interval stochastic chance-constrained robust programming and its application in flood management , 2017 .

[63]  Isa Ebtehaj,et al.  Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms , 2017 .

[64]  Vahid Nourani,et al.  A new approach to flow simulation using hybrid models , 2017, Applied Water Science.

[65]  A-Xing Zhu,et al.  Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. , 2018, The Science of the total environment.

[66]  Faith Ka Shun Chan,et al.  Towards resilient flood risk management for Asian coastal cities: Lessons learned from Hong Kong and Singapore , 2018, Journal of Cleaner Production.

[67]  T. Gan,et al.  Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East , 2018, Journal of Cleaner Production.

[68]  Kwok-wing Chau,et al.  Flood Prediction Using Machine Learning Models: Literature Review , 2018, Water.

[69]  G. Kabir,et al.  Assessing urban areas vulnerability to pluvial flooding using GIS applications and Bayesian Belief Network model , 2018 .

[70]  Md. Jalil Piran,et al.  Survey of computational intelligence as basis to big flood management: challenges, research directions and future work , 2018 .

[71]  R. Eker,et al.  FLOOD FORECASTING USING TRANSBOUNDARY DATA WITH THE FUZZY INFERENCE SYSTEM: THE MARITZA (MERIC) RIVER. , 2018, International Journal of Advanced Research.

[72]  T. S. Amjath-Babu,et al.  Sustainable survival under climatic extremes: linking flood risk mitigation and coping with flood damages in rural Pakistan , 2018, Environmental Science and Pollution Research.

[73]  David G. Chandler,et al.  A comparison of SAC‐SMA and Adaptive Neuro‐fuzzy Inference System for real‐time flood forecasting in small urban catchments , 2018, Journal of Flood Risk Management.

[74]  Stelios Kapetanakis,et al.  Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York , 2018, Water.

[75]  Alireza B. Dariane,et al.  Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection , 2018 .

[76]  Shabir Ahmad,et al.  Flood frequency analysis of river Jhelum in Kashmir basin , 2019, Quaternary International.

[77]  Wei Chen,et al.  Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm. , 2019, Journal of environmental management.

[78]  H. Pourghasemi,et al.  Multi-hazard probability assessment and mapping in Iran. , 2019, The Science of the total environment.

[79]  M. Zeleňáková,et al.  Flood risk modelling of the Slatvinec stream in Kružlov village, Slovakia , 2019, Journal of Cleaner Production.

[80]  Amir Mosavi,et al.  Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. , 2019, The Science of the total environment.

[81]  M. E. Banihabib,et al.  Bayesian network model for flood forecasting based on atmospheric ensemble forecasts , 2019, Natural Hazards and Earth System Sciences.

[82]  Kun Li,et al.  Multiple environmental factors analysis of flash flood risk in Upper Hanjiang River, southern China , 2019, Environmental Science and Pollution Research.

[83]  Kenji Tanaka,et al.  Flood mitigation performance of low impact development technologies under different storms for retrofitting an urbanized area , 2019, Journal of Cleaner Production.

[84]  J. Adamowski,et al.  An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. , 2019, The Science of the total environment.

[85]  Shabir Ahmad,et al.  Flood hazard assessment of the Kashmir Valley using historical hydrology , 2019, Journal of Flood Risk Management.

[86]  Ganapathy Pattukandan Ganapathy,et al.  Evaluation of regional flood quantiles at ungauged sites by employing nonlinearity-based clustering approaches , 2019, Environmental science and pollution research international.

[87]  Kiran Yarrakula,et al.  Mapping and assessing spatial extent of floods from multitemporal synthetic aperture radar images: a case study on Brahmaputra River in Assam State, India , 2019, Environmental Science and Pollution Research.

[88]  A. Q. Dar,et al.  Comparison of fuzzy inference algorithms for stream flow prediction , 2020, Neural Computing and Applications.

[89]  A. Q. Dar,et al.  Comparative analysis of neural network training algorithms for the flood forecast modelling of an alluvial Himalayan river , 2020, Journal of Flood Risk Management.

[90]  J. Ballesteros-Cánovas,et al.  Recent flood hazards in Kashmir put into context with millennium-long historical and tree-ring records. , 2020, The Science of the total environment.

[91]  D. Ahmad,et al.  Flood hazards and factors influencing household flood perception and mitigation strategies in Pakistan , 2020, Environmental Science and Pollution Research.

[92]  Dong Liu,et al.  Random forest regression evaluation model of regional flood disaster resilience based on the whale optimization algorithm , 2020 .

[93]  D. Ahmad,et al.  Flood hazards, human displacement and food insecurity in rural riverine areas of Punjab, Pakistan: policy implications , 2020, Environmental Science and Pollution Research.

[94]  Wenyu Yang,et al.  Evaluating the effect of urban flooding reduction strategies in response to design rainfall and low impact development , 2020 .

[95]  M. S. Mir,et al.  Sustainable Transportation Attainment Index: multivariate analysis of indicators with an application to selected states and National Capital Territory (NCT) of India , 2020, Environment, Development and Sustainability.

[96]  Biswajeet Pradhan,et al.  Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques , 2020, Journal of Hydrology.

[97]  Mohammad Shafi Mir,et al.  Modeling Sustainable Mobility Using Fuzzy Logic: An Application to Selected Indian States , 2020 .

[98]  Ruhhee Tabbussum,et al.  Analysis of Bayesian Regularization and Levenberg–Marquardt Training Algorithms of the Feedforward Neural Network Model for the Flow Prediction in an Alluvial Himalayan River , 2020 .