Abstract
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.
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Data availability
The data used in the research has been provided by various agencies recognized by the Government of India and the details of which are included in the Data Procurement section of the manuscript.
References
Abbas A, Amjath-Babu TS, Kächele H, Usman M, Amjed Iqbal M, Arshad M, Adnan Shahid M, Müller K (2018) Sustainable survival under climatic extremes: linking flood risk mitigation and coping with flood damages in rural Pakistan. Environ Sci Pollut Res 25:32491–32505. https://doi.org/10.1007/s11356-018-3203-8
Abebe Y, Kabir G, Tesfamariam S (2018) Assessing urban areas vulnerability to pluvial flooding using GIS applications and Bayesian Belief Network model. J Clean Prod 174:1629–1641. https://doi.org/10.1016/j.jclepro.2017.11.066
Adamowski J, Karapataki C (2010) Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN Learning algorithms. J Hydrol Eng 15:729–743. https://doi.org/10.1061/(asce)he.1943-5584.0000245
Ahmad D, Afzal M (2020a) Flood hazards and factors influencing household flood perception and mitigation strategies in Pakistan. Environ Sci Pollut Res 27:15375–15387. https://doi.org/10.1007/s11356-020-08057-z
Ahmad D, Afzal M (2020b) Flood hazards, human displacement and food insecurity in rural riverine areas of Punjab, Pakistan: policy implications. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-020-11430-7
Ahmed AAM, Shah SMA (2017) Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River. J King Saud Univ - Eng Sci 29:237–243. https://doi.org/10.1016/j.jksues.2015.02.001
Alvisi S, Mascellani G, Franchini M (2006a) Water level forecasting through fuzzy logic and artificial neural network approaches. Hydrol Earth Syst Sci 10:1–17
Alvisi S, Mascellani G, Franchini M, Bárdossy A (2006b) Water level forecasting through Fuzzy Logic and Artificial Neural Network approaches. Hydrol Earth Syst Sci 10:1–17. https://doi.org/10.5194/hess-10-1-2006
Anusree K, Varghese KO (2016) Streamflow Prediction of Karuvannur River Basin Using ANFIS, ANN and MNLR Models. Procedia Technol 24:101–108. https://doi.org/10.1016/j.protcy.2016.05.015
Arabameri A, Saha S, Chen W, Roy J, Pradhan B, Bui DT (2020) Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques. J Hydrol 587:125007. https://doi.org/10.1016/j.jhydrol.2020.125007
Ashrafi M, Chua LHC, Quek C, Qin X (2017) A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data. J Hydrol 545:424–435. https://doi.org/10.1016/j.jhydrol.2016.11.057
Aydın A, Yücedağ İ, Eker R (2016) Flood forecasting using transboundary data with the fuzzy inference system: The Maritza (Meriç) River. Nat Hazards Earth Syst Sci Discuss 1–19. https://doi.org/10.5194/nhess-2016-86
Ballesteros-Cánovas JA, Koul T, Bashir A, del Pozo JMB, Allen S, Guillet S, Rashid I, Alamgir SH, Shah M, Bhat MS, Alam A, Stoffel M (2020) Recent flood hazards in Kashmir put into context with millennium-long historical and tree-ring records. Sci Total Environ 722:137875. https://doi.org/10.1016/j.scitotenv.2020.137875
Bazartseren B, Hildebrandt G, Holz KP (2003) Short-term water level prediction using neural networks and neuro-fuzzy approach. Neurocomputing 55:439–450. https://doi.org/10.1016/S0925-2312(03)00388-6
Bhat MS, Ahmad B, Alam A, Farooq H, Ahmad S (2019a) Flood hazard assessment of the Kashmir Valley using historical hydrology. J Flood Risk Manag 12:1–13. https://doi.org/10.1111/jfr3.12521
Bhat MS, Alam A, Ahmad B, Kotlia BS, Farooq H, Taloor AK, Ahmad S (2019b) Flood frequency analysis of river Jhelum in Kashmir basin. Quat Int 507:288–294. https://doi.org/10.1016/j.quaint.2018.09.039
Bhutiyani MR, Kale VS, Pawar NJ (2010) Climate change and the precipitation variations in the northwestern Himalaya:1866 – 2006. Int J Climatol 548:535–548. https://doi.org/10.1002/joc.1920
Bhuvan-ISRO (2018) Bhuvan-Indian Geo-platform of ISRO. https://bhuvan.nrsc.gov.in/bhuvan_links.php. Accessed 25 Aug 2020
Cai H, Lye LM, Khan A (2009) Flood forecasting on the Humber river using an Artificial Neural Network approach. Can Soc Civ Eng Proc 2:611–620
Chan FKS, Chuah CJ, Ziegler AD, Dąbrowski M, Varis O (2018) Towards resilient flood risk management for Asian coastal cities: lessons learned from Hong Kong and Singapore. J Clean Prod 187:576–589. https://doi.org/10.1016/j.jclepro.2018.03.217
Chang FJ, Chang YT (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29:1–10. https://doi.org/10.1016/j.advwatres.2005.04.015
Chang FJ, Chiang YM, Chang LC (2007) Multi-step-ahead neural networks for flood forecasting. Hydrol Sci J 52:114–130. https://doi.org/10.1623/hysj.52.1.114
Chang F, Chiang Y, Tsai M et al (2014) Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information. J Hydrol 508:374–384. https://doi.org/10.1016/j.jhydrol.2013.11.011
Chapi K, Singh VP, Shirzadi A, Shahabi H, Bui DT, Pham BT, Khosravi K (2017) A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ Model Softw 95:229–245. https://doi.org/10.1016/j.envsoft.2017.06.012
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. Sci Total Environ 651:2087–2096. https://doi.org/10.1016/j.scitotenv.2018.10.064
Cloke HL, Pappenberger F (2009) Ensemble flood forecasting: A review. J Hydrol 375:613–626. https://doi.org/10.1016/j.jhydrol.2009.06.005
Dariane AB, Azimi S (2018) Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection. J Hydroinf 20:520–532. https://doi.org/10.2166/hydro.2017.076
Dawson CW, Wilby R (1998) An Artificial Neural Network approach to rainfall-runoff modeling. Hydrol Sci J 43:47–66. https://doi.org/10.1080/02626669809492102
Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25:80–108. https://doi.org/10.1177/030913330102500104
Dawson CW, Abrahart RJ, Shamseldin AY, Wilby RL (2006) Flood estimation at ungauged sites using artificial neural networks. J Hydrol 319:391–409. https://doi.org/10.1016/j.jhydrol.2005.07.032
Ding X, Hua D, Jiang G, Bao Z, Yu L (2017) Two-stage interval stochastic chance-constrained robust programming and its application in flood management. J Clean Prod 167:908–918. https://doi.org/10.1016/j.jclepro.2017.07.205
Dodangeh E, Choubin B, Eigdir AN, Nabipour N, Panahi M, Shamshirband S, Mosavi A (2020) Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Sci Total Environ 705:135983. https://doi.org/10.1016/j.scitotenv.2019.135983
Duncan AP, Chen AS, Keedwell EC et al (2012) Urban flood prediction in real-time from weather radar and rainfall data using Artificial Neural Networks. IAHS-AISH Publ 351:568–573
Firat M (2008) Comparison of Artificial Intelligence Techniques for river flow forecasting. Hydrol Earth Syst Sci 12:123–139. https://doi.org/10.5194/hess-12-123-2008
Fotovatikhah F, Herrera M, Shamshirband S, Chau KW, Faizollahzadeh Ardabili S, Piran MJ (2018) Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work. Eng Appl Comput Fluid Mech 12:411–437. https://doi.org/10.1080/19942060.2018.1448896
Gautam DK, Holz KP (2001) Rainfall-runoff modelling using adaptive neuro-fuzzy systems. J Hydroinf 3:3–10
Gnanaprakkasam S, Ganapathy GP (2019) Evaluation of regional flood quantiles at ungauged sites by employing nonlinearity-based clustering approaches. Environ Sci Pollut Res 26:22856–22877. https://doi.org/10.1007/s11356-019-05473-8
Goodarzi L, Banihabib ME, Roozbahani A, Dietrich J (2019) Bayesian Network Model for flood forecasting based on Atmospheric Ensemble Forecasts. Nat Hazards Earth Syst Sci Discuss 1–19. https://doi.org/10.5194/nhess-2019-44
Haykin S (1999) Neural Networks. A Comprehensive Foundation, 2nd edn. Pearson Publication, Ontario, Canada
Hong H, Panahi M, Shirzadi A, Ma T, Liu J, Zhu AX, Chen W, Kougias I, Kazakis N (2017) Flood susceptibility assessment in Hengfeng area coupling Adaptive Neuro-Fuzzy Inference System with Genetic Algorithm and Differential Evolution. Sci Total Environ 621:1124–1141. https://doi.org/10.1016/j.scitotenv.2017.10.114
Hu M, Zhang X, Li Y, Yang H, Tanaka K (2019) Flood mitigation performance of low impact development technologies under different storms for retrofitting an urbanized area. J Clean Prod 222:373–380. https://doi.org/10.1016/j.jclepro.2019.03.044
Hua P, Yang W, Qi X, Jiang S, Xie J, Gu X, Li H, Zhang J, Krebs P (2020) Evaluating the effect of urban flooding reduction strategies in response to design rainfall and low impact development. J Clean Prod 242:118515. https://doi.org/10.1016/j.jclepro.2019.118515
I&FC (2018) Irrigation and Flood Control Department-Srinagar. https://ifckashmir.com/. Accessed 21 Feb 2018
Illahi U, Mir MS (2020a) Sustainable Transportation Attainment Index: multivariate analysis of indicators with an application to selected states and National Capital Territory (NCT) of India. Springer, Netherlands
Illahi U, Mir MS (2020b) Modeling sustainable mobility using fuzzy logic: an application to selected Indian States. In: Gunjan VK, Kumar A, Gao X-Z (eds) Advances in cybernetics, for communication machine learning cognition, and technologies. Springer, pp 107–114
IMD (2018) Indian Metereological Department- Ministry of Earth Sciences, Government of India. https://mausam.imd.gov.in/. Accessed 22 Apr 2018
IWP (2018) India Water Portal. https://www.indiawaterportal.org/met_data/. Accessed 28 Jun 2018
Jacquin AP, Shamseldin AY (2006) Development of rainfall-runoff models using takagi-sugeno fuzzy inference systems. J Hydrol 329:154–173. https://doi.org/10.1016/j.jhydrol.2006.02.009
Jang JR (1993) ANFIS : Adaptive-Network-Based Fuzzy Inference System. IEEE Trans Syst Man Cybern 23:665–685. https://doi.org/10.1109/21.256541
Jayawardena AW, Perera EDP, Zhu B, Amarasekara JD, Vereivalu V (2014) A comparative study of fuzzy logic systems approach for river discharge prediction. J Hydrol 514:85–101. https://doi.org/10.1016/j.jhydrol.2014.03.064
Kamp RG, Savenije HHG (2007) Hydrological model coupling with ANNs. Hydrol Earth Syst Sci 11:3629–3653. https://doi.org/10.5194/hess-11-1869-2007
Kant A, Suman PK, Giri BK, Tiwari MK, Chatterjee C, Nayak PC, Kumar S (2013) Comparison of multi-objective evolutionary neural network, adaptive neuro-fuzzy inference system and bootstrap-based neural network for flood forecasting. Neural Comput & Applic 23:231–246. https://doi.org/10.1007/s00521-013-1344-8
Karl AK, Lohani AK (2010) Development of Flood forecasting system using statistical and ANN techniques in the downstream catchment of Mahanadi Basin, India. J Water Resour Prot 02:880–887. https://doi.org/10.4236/jwarp.2010.210105
Kourgialas NN, Karatzas GP (2017) A national scale flood hazard mapping methodology: the case of Greece – Protection and adaptation policy approaches. Sci Total Environ 601–602:441–452. https://doi.org/10.1016/j.scitotenv.2017.05.197
Latt ZZ, Wittenberg H (2014) Improving flood forecasting in a developing country: a comparative study of stepwise Multiple Linear Regression and Artificial Neural Network. Water Resour Manag 28:2109–2128. https://doi.org/10.1007/s11269-014-0600-8
Lavers T, Charlesworth S (2018) Opportunity mapping of natural flood management measures: a case study from the headwaters of the Warwickshire-Avon. Environ Sci Pollut Res 25:19313–19322. https://doi.org/10.1007/s11356-017-0418-z
Liu D, Fan Z, Fu Q, Li M, Faiz MA, Ali S, Li T, Zhang L, Khan MI (2020) Random forest regression evaluation model of regional flood disaster resilience based on the whale optimization algorithm. J Clean Prod 250. https://doi.org/10.1016/j.jclepro.2019.119468
Mahabir C, Hicks FE, Fayek AR (2003) Application of fuzzy logic to forecast seasonal runoff. Hydrol Process 17:3749–3762. https://doi.org/10.1002/hyp.1359
Mahmoud SH, Gan TY (2018) Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East. J Clean Prod 196:216–229. https://doi.org/10.1016/j.jclepro.2018.06.047
Mekanik F, Imteaz MA, Talei A (2016) Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals. Clim Dyn 46:3097–3111. https://doi.org/10.1007/s00382-015-2755-2
Meraj G, Romshoo SA, Yousuf AR, Altaf S, Altaf F (2015) Assessing the influence of watershed characteristics on the flood vulnerability of Jhelum basin in Kashmir Himalaya. Nat Hazards 78:1–5. https://doi.org/10.1007/s11069-015-1861-0
Minns AW, Hall MJ (1996) Artificial neural networks as rainfall-runoff models. Hydrol Sci J 41:399–417. https://doi.org/10.1080/02626669609491511
Mosavi A, Ozturk P, Chau KW (2018) Flood prediction using machine learning models: literature review. Water (Switzerland) 10:1–40. https://doi.org/10.3390/w10111536
Mpallas L, Tzimopoulos C, Evangelides C (2011) Comparison between neural networks and adaptive neuro-fuzzy inference system in modelling lake kerkini water level fluctuation lake management using artificial intelligence. J Environ Sci Technol 4:366–376. https://doi.org/10.3923/jest.2011.366.376
Mukerji A, Chatterjee C, Singh Raghuwanshi N (2009) Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models. J Hydrol Eng 14:647–652. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000040
Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291:52–66. https://doi.org/10.1016/j.jhydrol.2003.12.010
Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2005) Short-term flood forecasting with a neuro-fuzzy model. Water Resour Res 41:1–16. https://doi.org/10.1029/2004WR003562
Nguyen P, Thorstensen A, Sorooshian S, Hsu K, AghaKouchak A, Sanders B, Koren V, Cui Z, Smith M (2015) A high resolution coupled hydrologic – hydraulic model ( HiResFlood-UCI ) for flash flood modeling. J Hydrol 541:401–420. https://doi.org/10.1016/j.jhydrol.2015.10.047
Noymanee J, Nikitin NO, Kalyuzhnaya AV (2017) Urban pluvial flood forecasting using open data with machine learning techniques in Pattani Basin. Procedia Comput Sci 119:288–297. https://doi.org/10.1016/j.procs.2017.11.187
Othman F, Naseri M (2011) Reservoir inflow forecasting using artificial neural network. Int J Phys Sci 6:434–440. https://doi.org/10.5897/IJPS10.649
Özger M (2009) Comparison of fuzzy inference systems for streamflow prediction. Hydrol Sci J 54:261–273. https://doi.org/10.1623/hysj.54.2.261
Patel D, Parekh F (2014) Flood Forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS). Int J Eng Trends Technol 12:510–514
Perera EDP, Lahat L (2015) Fuzzy logic based flood forecasting model for the Kelantan River basin, Malaysia. J Hydro-Environ Res 9:542–553. https://doi.org/10.1016/j.jher.2014.12.001
Petit-Boix A, Sevigné-Itoiz E, Rojas-Gutierrez LA, Barbassa AP, Josa A, Rieradevall J, Gabarrell X (2017) Floods and consequential life cycle assessment: Integrating flood damage into the environmental assessment of stormwater Best Management Practices. J Clean Prod 162:601–608. https://doi.org/10.1016/j.jclepro.2017.06.047
Pourghasemi HR, Gayen A, Panahi M, Rezaie F, Blaschke T (2019) Multi-hazard probability assessment and mapping in Iran. Sci Total Environ 692:556–571. https://doi.org/10.1016/j.scitotenv.2019.07.203
Qasem SN, Ebtehaj I, Madavar HR (2017) Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms. J Appl Res Water Wastewater 7:290–298
Rao S, Brevern P, El-Tayeb NSM, Vengkatesh VC (2009) GUI based mamdani fuzzy inference system modeling to predict surface roughness in laser machining. Int J Electr Comput Sci IJECS-IJENS 9:37–43
Rashetnias S (2016) Flood vulnerability assessment by applying a fuzzy logic method : a case study from Melbourne. Victoria University, Melbourne, Australia
Rezaeianzadeh M, Tabari H, Arabi Yazdi A, Isik S, Kalin L (2014) Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput & Applic 25:25–37. https://doi.org/10.1007/s00521-013-1443-6
Rezaeian-Zadeh M, Tabari H, Abghari H (2013) Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions. Arab J Geosci 6:2529–2537. https://doi.org/10.1007/s12517-011-0517-y
Roodsari BK, Chandler DG, Kelleher C, Kroll CN (2019) A comparison of SAC-SMA and Adaptive Neuro-fuzzy Inference System for real-time flood forecasting in small urban catchments. J Flood Risk Manag 12:1–12. https://doi.org/10.1111/jfr3.12492
Rossi R, Gastaldi M, Gecchele G (2013) Comparison of fuzzy-based and AHP methods in sustainability evaluation: A case of traffic pollution-reducing policies. Eur Transp Res Rev 5:11–26. https://doi.org/10.1007/s12544-012-0086-5
Saleh SF, Rather FF, Jabbar MJ (2017) Floods and mitigation techniques with reference to Kashmir. Int J Eng Sci Comput 7:6359–6363
Shu C, Ouarda TBMJ (2008) Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. J Hydrol 349:31–43. https://doi.org/10.1016/j.jhydrol.2007.10.050
Solgi A, Zarei H, Nourani V, Bahmani R (2017) A new approach to flow simulation using hybrid models. Appl Water Sci 7:3691–3706. https://doi.org/10.1007/s13201-016-0515-z
Sun Y, Tang D, Sun Y, Cui Q (2016) Comparison of a fuzzy control and the data-driven model for flood forecasting. Nat Hazards 82:827–844. https://doi.org/10.1007/s11069-016-2220-5
Surampudi S, Yarrakula K (2020) Mapping and assessing spatial extent of floods from multitemporal synthetic aperture radar images: a case study on Brahmaputra River in Assam State, India. Environ Sci Pollut Res 27:1521–1532. https://doi.org/10.1007/s11356-019-06849-6
Tabbussum R, Dar AQ (2020a) Analysis of Bayesian Regularization and Levenberg–Marquardt Training Algorithms of the Feedforward Neural Network Model for the Flow Prediction in an Alluvial Himalayan River. 43–50. https://doi.org/10.1007/978-981-15-1632-0_5
Tabbussum R, Dar AQ (2020b) Comparative analysis of neural network training algorithms for the flood forecast modelling of an alluvial Himalayan river. 1–18. https://doi.org/10.1111/jfr3.12656
Tabbussum R, Dar AQ (2020c) Comparison of fuzzy inference algorithms for stream flow prediction. Neural Comput & Applic. https://doi.org/10.1007/s00521-020-05098-w
Tareghian R, Kashefipour S (2007) Applications of fuzzy systems and artificial neural networks for flood forecasting. J Appl Sci 7:3451–3459
Tsakiri K, Marsellos A, Kapetanakis S (2018) Artificial neural network and multiple linear regression for flood prediction in Mohawk River, New York. Water (Switzerland) 10. https://doi.org/10.3390/w10091158
Wang Y, Hong H, Chen W, Li S, Panahi M, Khosravi K, Shirzadi A, Shahabi H, Panahi S, Costache R (2019) Flood susceptibility mapping in Dingnan county (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm. J Environ Manag 247:712–729. https://doi.org/10.1016/j.jenvman.2019.06.102
Werbos PJ (1988) Generalization of backpropagation with application to a recurrent gas market model. Neural Netw 1:339–356. https://doi.org/10.1016/0893-6080(88)90007-X
Wu CL, Chau KW (2010) Data-driven models for monthly streamflow time series prediction. Eng Appl Artif Intell 23:1350–1367. https://doi.org/10.1016/j.engappai.2010.04.003
Xiong L, Shamseldin AY, O’Connor KM (2001) A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi-Sugeno fuzzy system. J Hydrol 245:196–217. https://doi.org/10.1016/S0022-1694(01)00349-3
Yadav D, Naresh R, Sharma V (2011) Stream flow forecasting using Levenberg-Marquardt algorithm approach. Environ Eng 3:30–40
Zeleňáková M, Fijko R, Labant S, Weiss E, Markovič G, Weiss R (2019) Flood risk modelling of the Slatvinec stream in Kružlov village, Slovakia. J Clean Prod 212:109–118. https://doi.org/10.1016/j.jclepro.2018.12.008
Zhong M, Jiang T, Li K, Lu Q, Wang J, Zhu J (2020) Multiple environmental factors analysis of flash flood risk in Upper Hanjiang River, southern China. Environ Sci Pollut Res 27:37218–37228. https://doi.org/10.1007/s11356-019-07270-9
Zounemat-Kermani M, Kisi O, Rajaee T (2013) Performance of radial basis and LM-feed forward Artificial Neural Networks for predicting daily watershed runoff. Appl Soft Comput J 13:4633–4644. https://doi.org/10.1016/j.asoc.2013.07.007
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We would like to thank the Ministry of Human Resources and Development, India, for funding the research.
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This research is funded by the Ministry of Human Resources and Development—India, through the scholarship granted to Ruhhee Tabbussum.
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Tabbussum, R., Dar, A.Q. Performance evaluation of artificial intelligence paradigms—artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction. Environ Sci Pollut Res 28, 25265–25282 (2021). https://doi.org/10.1007/s11356-021-12410-1
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DOI: https://doi.org/10.1007/s11356-021-12410-1