Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Developing Feasible Structural Flood Mitigation Measures for Arid Regions Using Hydrological and Hydraulic Modelling: a Case of Baledweyne City, Somalia(Springer Int Publ Ag, 2025) Ibrahim, Abdullahi Abdulwahid; Tayfur, Gokmen; Safari, Mir Jafar SadeghFlood mitigation measures are essential in several aspects of environmental sustainability, such as environmental protection and development, infrastructure resilience, and climate change adaptation strategies. This study investigates several remedial measures such as flood and disaster risk reduction strategies to protect Baledwayne City, Somalia. The developed hydrological-hydraulic model is successfully calibrated using the 2019 flood event. Inundation maps for different mitigation measures are developed to determine the most appropriate one(s) that may reduce flood impact and protect the city, especially the residential areas, against peak discharge of a 500-year return period. The investigated mitigation measures include (1) operating the Warabole Diversion Canal, (2) building two detention ponds at the upstream sections of both right and left floodplains, (3) building levees (dikes) along both sides of Shabelle River, and (4) restoration of the river. Results show that the remedial measures for the Warabole Diversion Channel and the river restoration are insufficient to protect the city. Building ponds on the right- and left-hand sides of the river substantially protects the city from flooding by reducing the flooded area from 88.6 to 31.5 km2, which is equivalent to a reduction of 35.5%. Construction of dikes on both sides of the river does not protect the city by overflowing an area of 75.2 km2 (84.9%). A combination of several measures is also investigated and it is found that ponds must be built along with any alternative measure. When all four measures are combined, it becomes clear that the city's residential areas would be fully protected, significantly reducing the overall flooded area of the city to 27.9 km2 (31.5%). These flooded areas are behind the detention pond, which is designed to hold floodwater and gradually drain it back to the river.Article Citation - WoS: 3Citation - Scopus: 3Ensemble and Optimized Hybrid Algorithms Through Runge Kutta Optimizer for Sewer Sediment Transport Modeling Using a Data Pre-Processing Approach(Elsevier, 2023) Safari, Mir Jafar Sadegh; Gül, Enes; Dursun, Ömer Faruk; Tayfur, GökmenUncontrolled sediment deposition in drainage and sewer systems raises unexpected maintenance expenditures. To this end, implementation of an accurate model relying on effective parameters involved is a reliable benchmark. In this study, three machine learning techniques, namely extreme learning machine (ELM), multilayer perceptron neural network (MLPNN), and M5P model tree (M5PMT); and three optimization approaches of Runge Kutta (RUN), genetic algorithm (GA), and particle swarm optimization (PSO) are applied for modeling. The optimization and ensemble hybridization approaches are applied in the modeling procedure. For the case of hybrid optimized models, the ELM and MLPNN models are hybridized with RUN, GA, and PSO algorithms to develop six hybrid models of ELM-RUN, ELM-GA, ELM-PSO, MLPNN-RUN, MLPNN-GA, and MLPNN-PSO. Ensemble hybrid models are developed through coupling the ELM and MLPNN models with the M5PMT algorithm. The data pre-processing approach is applied to find the best randomness characteristic of the utilized data. Results illustrate that the RUN-based hybrid models outperform the GA- and PSO-based counterparts. Although the MLPNN-RUN and MLPNN-M5PMT hybrid models generate better results than their alternatives, MLPNN-M5PMT slightly outperforms MLPNN-RUN model with a coefficient of determination of 0.84 and a root mean square error of 0.88. The current study shows the superiority of the ensemble-based approach to the optimization techniques. Further investigation is needed by considering alternative optimization techniques to enhance sediment transport modeling. © 2023 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion Research