Performance of conceptual and black-box models in flood warning systems

Banihabib, Mohammad Ebrahim

doi:10.1080/23311916.2015.1127798

Cited by 10 publications

(6 citation statements)

References 36 publications

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“…In all of the above cases, the NN used was a Multilayer Perceptron (MLP). Banihabib (2016) compares the performance of a NN with a conceptual model for the determination of flood warning lead-time (FWLT) in Tajrish watershed that is a steep urbanized watershed located in the north of metropolitan city, Tehran, Iran, and the main flash flooder watershed in north of Tehran. Dynamics artificial NN (DANN) with time delay units by recurrent connections was used.…”

Section: Introductionmentioning

confidence: 99%

Feasibility study on operational use of neural networks in a flash flood early warning system

Lima

Scofield

2021

RBRH

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Issuing early and accurate warnings for flash floods is a challenge when the rains that deflagrate these natural hazards occur on very short space-time scales. This article reports a case study in which a neural network-based hydrological model is designed to forecast one hour in advance if the water level in a small mountain watershed with short time to peak, situated in the city of Campos do Jordão in Brazil, will exceed its attention quota. This model can be a powerful auxiliary tool in a flash flood early warning system, since with it decision-making becomes semi-automated, making it possible to improve the warnings advance-accuracy tradeoff. A deep-learning neural network using Exponential Linear Unit activation functions was designed based on 3-years rainfall and water level data from 11 hydrometeorological stations of the National Centre for Monitoring and Early Warning of Natural Disasters. In the training of the neural network, two combinations of input variables were tested. The tuples in the test set were classified through voting with 60 classifiers. The first results obtained in Matlab environment with high percentages of true positives indicate that it is feasible to use the neural model operationally.

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Section: Introductionmentioning

confidence: 99%

Feasibility study on operational use of neural networks in a flash flood early warning system

Lima

Scofield

2021

RBRH

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“…Its distributive modelling capability, its possibility to be linked with other software, and its parameter calibration are the most significant advantages of this model [5][6][7][8]. Some studies have applied hydrological models to estimate the peak flow of typical floods (non-debris floods) and have determined the lead-time of the flood warning system [9][10][11]. Zelelew and Melesse [12] assessed the applicability of the HEC-HMS model to estimate the runoff in the Abbay river basin.…”

Section: Introductionmentioning

confidence: 99%

“…Wang, et al [18] indicated the random forest model's ability to predict the typical flood and the debris flood processes in Beijing's mountainous area. Banihabib [10] compared the efficiency of Dynamic Artificial Neural Network and HEC-HMS model to determine Flood Warning Lead Time (FWLT). The comparison showed that DANN can estimate FWLT longer than the HEC-HMS model [10].…”

Section: Introductionmentioning

confidence: 99%

“…Banihabib [10] compared the efficiency of Dynamic Artificial Neural Network and HEC-HMS model to determine Flood Warning Lead Time (FWLT). The comparison showed that DANN can estimate FWLT longer than the HEC-HMS model [10]. The previous research did not link the results of the conceptual model with the artificial intelligence models, while this research highlights the significance of hybridization of HEC-HMS and the artificial intelligence models to improve HEC-HMS's deficiency in reflecting sediment concentration impact on the debris flood prediction.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Intelligence Model for the Prediction of the Peak Flow of Debris Floods

Banihabib

Jurík

Kazemi

et al. 2020

Water

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Debris floods, as one of the most significant natural hazards, often threaten the lives and property of many people worldwide. Predicting models are essential for flood warning systems to minimize casualties of debris floods. Since HEC-HMS (Hydrologic Engineering Center’s Hydrological Modelling System) cannot simulate debris flow, this study proposes a new hybrid model that uses artificial intelligence models to overcome HEC-HMS’s insufficiency in reflecting the sediment concentration effect on the debris floods. A sediment concentration is an effective factor for evaluating debris flood peak flows. This led to the proposal of new hybrid models for predicting the debris flood peak flows on the basis of hybridization of the artificial intelligence models (Bayesian Network (BN) and Support Vector Regression–Particle Swarm Optimization (SVR-PSO)) and HEC-HMS. To estimate the sediment concentration of floods by using the proposed artificial intelligence models, we nominated an average basin elevation, an average basin slope, a basin area, the current day rainfall, the antecedent rainfall of the past 3 days, and the streamflow of the previous day the previous day as the effective variables. In the validation stage, the average of the Mean Absolute Relative Error (MARE) of the estimated values were 0.024, 0.038, and 0.024 for the typical floods that occurred in the Navrood, Kasilian, and the Amameh basins in the north of Iran, respectively. Similarly, we obtained values of 0.038, 0.073, and 0.040 for the debris flood events for the three respective locations. After predicting the debris flood peak flows by the proposed hybrid HMS-BN and HMS-SVR-PSO models, the average of the MAREs for all debris flood events was reduced to 0.013 and 0.014, respectively. The comparison of MAREs of the examined hybrid models shows that the HMS-BN model results in higher accuracy than the HMS-SVR-PSO model in the prediction of the debris flood peak flows. Generally, the absolute error of prediction by the proposed hybrid model is reduced to one-third of the HEC-HMS. The prediction of the debris flood peak flows using the proposed hybrid model can be examined in the debris flood warning systems to reduce the potential damages and casualties in similar basins.

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“…Karunanithi et al 1994;Wilby 1998, 1999;Imrie et al 2000;Varoonchotikul 2003;Cigizoglu 2005;Anctil and Rat 2005;Shamseldin 2010;Araujo et al 2011;Adamowski et al 2012;Elsafi 2014;Shoaib et al 2014;Banihabib 2016).…”

Section: Related Workmentioning

confidence: 99%

An operational dynamical neuro-forecasting model for hydrological disasters

Lima

Santos

Carvalho

et al. 2016

Model. Earth Syst. Environ.

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In the last decades, artificial neural network has been increasingly applied in hydrological modeling given its potential to process the complex nonlinear relationships of the associated physical-environmental variables and produce a suitable solution (for instance, a forecasting model) in a relatively short time. In this scope, this work reports the design methodology and the operational results obtained with an artificial neural network-based model developed to forecast, with 2 h in advance, the level of a river in the mountainous region of Rio de Janeiro state in Brazil. This is an area susceptible to natural disasters with recent records of floods and landslides that caused environmental and socio-economic damage of large proportions. The proposed neural network uses an innovative learning algorithm (the quasi-Newton optimization method is applied to the slopes of each hidden activation function) and, as input features, values of rainfall and river level data collected from 8 monitoring stations located on studied watershed between 2013 and 2014. The results of the neural model, with NASH index greater than 0.86, are promising making possible its operational use on an issuing flood alerts system.

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Performance of conceptual and black-box models in flood warning systems

Cited by 10 publications

References 36 publications

Feasibility study on operational use of neural networks in a flash flood early warning system

Feasibility study on operational use of neural networks in a flash flood early warning system

A Hybrid Intelligence Model for the Prediction of the Peak Flow of Debris Floods

An operational dynamical neuro-forecasting model for hydrological disasters

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