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

Abstract

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.

Aplikasi Program HEC-RAS 5.0.7 untuk Pemodelan Banjir di Sub-sub DAS Martapura Kabupaten Banjar

“…Air sungai yang seharusnya bermanfaat bagi umat manusia, akan berdampak buruk apabila mengakibatkan banjir. Banjir yang membawa endapan sedimen dapat bersifat sangat merusak terhadap lingkungan yang dilewati [2].…”

Simulation and Prediction Algorithm for the Whole Process of Debris Flow Based on Multiple Data Integration

“…There have been many scholars studying debris-flow prediction. Banihabib et al used an artificial intelligence model to overcome the shortcomings in the impact of sediment concentration on debris flow [12] since sediment concentration is an effective factor that can be used to evaluate the peak flow of debris flow. The proposed artificial intelligence model is used to estimate the sediment concentration of floods, and the average watershed elevation, average watershed slope, watershed area, daily rainfall, and early rainfall are specified to achieve an effective prediction of debris flow.…”

Evaluation of Debris Flows for Flood Plain Estimation in a Small Ungauged Tropical Watershed for Hurricane Otto

“…The impact of debris flow is aggravated if there is not a preliminary identification of floodplains, making local risk management and early warnings impossible to apply, especially at ungauged small watersheds. Several methodologies have been utilized to perform debris flood hazard assessments for gauged watersheds and focus on calibrated hydrological and two-dimensional hydraulic models and rheological parameters quantified from field measurements during the event [3][4][5][6][7]. In ungauged watersheds, some authors have applied regionalization for peak flow event estimation [8] and continuous flow modeling [9,10].…”