Sökning: "Flood risk cost"
Visar resultat 1 - 5 av 22 uppsatser innehållade orden Flood risk cost.
1. Assessing the Effectiveness of Urban Trees with Skeletal Soils in Flood Risk Mitigation
Master-uppsats, KTH/Hållbar utveckling, miljövetenskap och teknikSammanfattning : With increased urbanization and climate change, heavy rainfall events and urban floods are becoming more frequent. To meet the demands for effective climate adaptation strategies and mitigation measures, Nature-Based Solutions (NBS) have received increased attention. LÄS MER
2. Dam Failure Analysis of the Parteboda Hydropower Plant : A deterministic approach
Master-uppsats, KTH/Hållbar utveckling, miljövetenskap och teknikSammanfattning : Despite all the benefits a dam can provide, hundreds of years of experience have shown the destructive forces that can be unleashed by dam failure. To carry out risk management and execute mitigative measures for a dam facility, it is first necessary to assess the incremental consequences of a potential dam failure. LÄS MER
3. Estimation of flood risk and cost-effective mitigations : A case study in Tierp
Master-uppsats, KTH/Hållbar utveckling, miljövetenskap och teknikSammanfattning : Climate change is predicted to alter the rainfall patterns in the future, and extreme rain events with large rainfall volumes will become more frequent and intense which increases the flood risk. A clear trend can be seen, where more and more people decide to relocate from rural to urban areas. LÄS MER
4. Kommunernas planering och arbete med översvämningsrisker i lågstråk vid befintlig bebyggelse
Kandidat-uppsats, Jönköping University/JTH, Byggnadsteknik och belysningsvetenskapSammanfattning : Introduktion: Många städer i Sverige har fler lågpunkter i sin terräng och är drabbadevid kraftigt regn. Förtätning i städerna förvärrar ofta problemet ytterligare då mängdenhårdgjorda ytor utökas samtidigt som tillgänglig mark för dagvattenhantering minskar. LÄS MER
5. An evaluation of deep learning models for urban floods forecasting
Master-uppsats, KTH/GeoinformatikSammanfattning : Flood forecasting maps are essential for rapid disaster response and risk management, yet the computational complexity of physically-based simulations hinders their application for efficient high-resolution spatial flood forecasting. To address the problems of high computational cost and long prediction time, this thesis proposes to develop deep learning neural networks based on a flood simulation dataset, and explore their potential use for flood prediction without learning hydrological modelling knowledge from scratch. LÄS MER