Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm

Abstract

Flood prediction and control are among the major tools for decision makers and water resources planners to avoid flood disasters. The Muskingum model is one of the most widely used methods for flood routing prediction. The Muskingum model contains four parameters that must be determined for accurate flood routing. In this context, an optimization process that self-searches for the optimal values of these four parameters might improve the traditional Muskingum model. In this study, a hybrid of the bat algorithm (BA) and the particle swarm optimization (PSO) algorithm, i.e., the hybrid bat-swarm algorithm (HBSA), was developed for the optimal determination of these four parameters. Data for the three different case studies from the USA and the UK were utilized to examine the suitability of the proposed HBSA for flood routing. Comparative analyses based on the sum of squared deviations (SSD), sum of absolute deviations (SAD), error of peak discharge, and error of time to peak showed that the proposed HBSA based on the Muskingum model achieved excellent flood routing accuracy compared to that of other methods while requiring less computational time.

Building an Intelligent Hydroinformatics Integration Platform for Regional Flood Inundation Warning Systems

“…Improving the Muskingum Routing method using various optimization methods such as hybrid bat-swarm algorithm [9], improved bat algorithm [10], and Wolf Pack Algorithm [11], or combined with a particle filter-based assimilation model [12] for streamflow forecasts; 5.…”

“…Following this track, hydroinformatics has emerged as an essential tool by combining science, technologies and social considerations into a holistic coherent framework to timely deal with collecting, modeling, visualizing, and sharing flood-related information and to improve the applicability and accuracy of flood warnings [27][28][29][30][31][32][33][34]. ML methods are efficient tools for extracting the key information from complex highly dimensional input-output patterns and are widely used in various hydrological problems such as flood forecasts in this special issue [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] as well as groundwater and water management issues [35][36][37][38][39][40][41][42][43][44][45][46]. Recently, technological advances in social media have improved data gathering and dissemination, especially under the development of world-wide-web technologies.…”

Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm

“…The sum of squared deviations (SSD): SSD index is used as the objective function in the present study. The index calculates the total of squared deviations between observed and real discharges [28,[42][43][44][45][46]:…”

“…3. Error of Peak discharge (EP): EP index measures the difference between predicted and observed discharges [43][44][45][46].…”

“…7. The agreement index (d) based on follow equation, shows the performance of the model well, so that the value of index can change from 0 to 1 [45,46].…”