Sökning: "Urban flood models"
Visar resultat 1 - 5 av 31 uppsatser innehållade orden Urban flood models.
1. Hur avrinningskoefficienten varierar med återkomsttid - En jämförelse mellan fyra olika områden i Sverige
Master-uppsats, Lunds universitet/Avdelningen för Teknisk vattenresursläraSammanfattning : As climate change progresses, heavy rainfall in Sweden is becoming more frequent and intense, coinciding with an increase in impermeable surfaces in urban areas. This raises the risk of flooding as drainage networks may exceed their capacity due to increased runoff. LÄS MER
2. Identifiering och validering av potentiella översvämningsriskområden med hjälp av GIS-baserad multikriterieanalys
M1-uppsats, Karlstads universitet/Institutionen för miljö- och livsvetenskaper (from 2013)Sammanfattning : Floods are natural disasters that often have significant socio-economic consequences. Urban areas with uncontrolled urban development, rapid population growth, an unregulated municipal system and an unplanned change in land use belong to the very sensitive areas where floods cause devastating economic and social losses. LÄS MER
3. Evaluating the Bluespot model with the August 2021 flood in Gävle, Sweden
Master-uppsats, Stockholms universitet/Institutionen för naturgeografiSammanfattning : Floods are one of the most common types of natural disasters. They annually affect vast amounts of people and cause severe economic losses. While fluvial, coastal, and flash floods are well studied, pluvial floods (rain related) have received modest attention from researchers and decision-makers in comparison. LÄS MER
4. The influence of infiltration and rain intensity on an urban pluvial flood model : A case study of a catchment in Malmö municipality
Master-uppsats, KTH/Hållbar utveckling, miljövetenskap och teknikSammanfattning : Urban pluvial floods have become more and more common in Europe. These floods have resulted in high costs for society, increasing the interest in conducting hydraulic flood models. This in order to prepare and minimise the consequences of these flood events. 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