<p>An approach to incorporate the effect of flood routing through a channel reach for use with the analytical probabilistic stormwater models (APSWM) was developed earlier in 2005. That earlier approach relied on adding the Muskingum K value of the channel reach into the catchment time of concentration and treating the whole drainage area including the channel reach as a lumped catchment. This is insufficient since other factors such as the X value in the Muskingum routing method also affects the routing results. In this study, a new approach to incorporate the routing effect of channel reaches in APSWM was developed where not only the K value but also the X value of a channel reach are considered. A number of continuous simulations were conducted to verify the proposed approach. It was demonstrated that the proposed approach performs better than the earlier one.</p> / Thesis / Master of Applied Science (MASc)

This diploma thesis deals with the real use of operation research method in conjunction with logistics Cross-Docking technology. It is possible to solve this conjunction with either routing problems along with its modifications or simulations methods. The theoretical part describes routing problems, routing problems with time windows, simulations methods with their possibilities of real use and logistics technologies. A model of a Cross-Docking terminal that was created in the program Simul8 can be found in the practical part of this thesis. The goal of the simulation was to ensure smooth flow of products through the terminal without any long queues in the terminal. Routing problems designed to minimize the length of its routes can be found in the second half of the practical part. Other possible real application is routing problems on the real problem. The solution for this task was found using the modelling system MPL for Windows.

The primary objective of this thesis is to develop hydrologic and hydraulic models for the Soap Creek Watershed, IA for the evaluation of alternative flood mitigation strategies and the analysis of the differences between hydrologic and hydraulic routing methods. In 2008, the state of Iowa suffered a disastrous flood that caused extensive damage to homes, agricultural lands, commercial property, and public infrastructures. To reduce the flood damage across Iowa, the U.S. Department of Housing and Urban Development (HUD) awarded funds to the Iowa Flood Center and IIHR-Hydroscience &Engineering at the University of Iowa to conduct the Iowa Watersheds Project. The Soap Creek Watershed was selected as one of the study areas because this region has suffered frequent severe floods over the past century and because local landowners have organized to construct over 130 flood detention ponds within it since 1985. As part of the Iowa Watersheds Project, we developed a hydrologic model using the U.S. Army Corps of Engineers’ (USACE) Hydrologic Center’s hydrologic Modeling System (HEC-HMS). We used the hydrologic model to evaluate the effectiveness of the existing flood mitigation structures with respect to discharge and to identify the high runoff potential areas. We also investigated the potential impact of two additional flood mitigation practices within the Soap Creek Watershed by utilizing the hydrologic model, which includes changing the land use and improving the soil quality. The HEC-HMS model simulated 24-hour design storms with different return periods, including 10, 25, 50, and 100 year. The results from modeling four design storms revealed that all three practices can reduce the peak discharge at different levels. The existing detention ponds were shown to reduce the peak discharge by 28% to 40% depending on the choice of observed locations and design storms. However, changing the land use can reduce the peak discharge by an average of only 1.0 %, whereas improving the soil quality can result in an average of 15 % reduction. Additionally, we designed a hydraulic model using the United States Army Corps of Engineers’ (USACE) Hydrologic Engineering Center’s River Analysis System (HEC- RAS) to perform a comparative evaluation of hydrologic and hydraulic routing methods. The hydrologic routing method employed in this study is the Muskingum Routing method. We compare the historical and design storms between HEC-HMS, HEC-RAS, and observed stage hydrographs and take the hydrograph timing, shape, and magnitude into account. Our results indicate that the hydraulic routing method simulates the hydrograph shape more effectively in this case.

Wireless sensor networks (WSNs) have become pivotal in precision agriculture, environmental monitoring, and smart healthcare applications. However, the challenges of energy consumption and security, particularly concerning the reliance on large battery-operated nodes, pose significant hurdles for these networks. Energy-harvesting wireless sensor networks (EH-WSNs) emerged as a solution, enabling nodes to replenish energy from the environment remotely. Yet, the transition to EH-WSNs brought forth new obstacles in ensuring reliable and secure data transmission. In our initial study, we tackled the intermittent connectivity issue prevalent in EH-WSNs due to the dynamic behavior of energy harvesting nodes. Rapid shifts between ON and OFF states led to frequent changes in network topology, causing reduced link stability. To counter this, we introduced the hybrid routing method (HRM), amalgamating grid-based and opportunistic-based routing. HRM incorporated a packet fragmentation mechanism and cooperative localization for both static and mobile networks. Simulation results demonstrated HRM's superior performance, enhancing key metrics such as throughput, packet delivery ratio, and energy consumption in comparison to existing energy-aware adaptive opportunistic routing approaches. Our second research focused on countering emerging threats, particularly the malicious energy attack (MEA), which remotely powers specific nodes to manipulate routing paths. We developed intelligent energy attack methods utilizing Q-learning and Policy Gradient techniques. These methods enhanced attacking capabilities across diverse network settings without requiring internal network information. Simulation results showcased the efficacy of our intelligent methods in diverting traffic loads through compromised nodes, highlighting their superiority over traditional approaches. In our third study, we developed a deep learning-based two-stage framework to detect MEAs. Utilizing a stacked residual network (SR-Net) for global classification and a stacked LSTM network (SL-Net) to pinpoint specific compromised nodes, our approach demonstrated high detection accuracy. By deploying trained models as defenses, our method outperformed traditional threshold filtering techniques, emphasizing its accuracy in detecting MEAs and securing EH-WSNs. In summary, our research significantly advances the reliability and security of EH-WSN, particularly focusing on enhancing the network layer. These findings offer promising avenues for securing the future of wireless sensor technologies.