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Visar resultat 1 - 5 av 49 uppsatser som matchar ovanstående sökkriterier.

1. 1. Combining Continuous and Event-based Hydrological Modeling in Kävlinge River Basin with HEC-HMS

   Master-uppsats, Lunds universitet/Avdelningen för Teknisk vattenresurslära

   Författare :Wenli Hu; [2022]
   Nyckelord :HEC-HMS; SCS-CN; Climate Change; GIS; Flood; Technology and Engineering;

   Sammanfattning : Prediction of flood and evaluation of the impact of climate change in a catchment needs to be correctly estimated, which can be accomplished by hydrological modelling in HEC-HMS. In this paper, the objectives were to gain an understanding of the rainfall-runoff process and estimate the flood frequency curve in the Kävlinge River Basin. LÄS MER

3. 2. Inverse Atmospheric Dispersion Modeling in Complex Geometries

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för fysik

   Författare :Charlie Pelland; [2022]
   Nyckelord :atmospheric dispersion modeling; dispersion modeling; invers dispersion modeling; atmospheric dispersion; invers atmospheric dispersion; CFD; computational fluid dynamics; radioactive release; openfoam; CRBN; dry deposition; dry deposition velocity; scavenging effect; wet deposition; optimization model; Reynolds Averaged Navier-Stokes; adjoint mode; time-dependent rain simulation; time-dependent rain; time dependent rain simulation; time dependent rain;

   Sammanfattning : In the event of a radioactive release in an urban environment the consequent response mustbe swift and precise. As soon as first responders have correct information, they can make anaccurate risk assessment. However, if the position, release rate and time of the radioactiverelease is unknown it is hard to know how the pollutant will spread. LÄS MER

4. 3. An evaluation of deep learning models for urban floods forecasting

   Master-uppsats, KTH/Geoinformatik

   Författare :Yang Mu; [2022]
   Nyckelord :Urban flooding forecasting; Convolutional neural networks; Deep learning; Physically-based simulation; Recurrent neural network; Stadsöversvämningsprognoser; konvolutionella neurala nätverk; djupinlärning; fysiskt baserad simulering; återkommande neurala nätverk;

   Sammanfattning : 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

5. 4. Hydrological modelling of a recycling facility and landfill

   Master-uppsats, Lunds universitet/Avdelningen för Teknisk vattenresurslära

   Författare :Mazen Tawfik; [2022]
   Nyckelord :Parsimonious approach; Lumped modelling; Surface water; Landfill; Leachate water; Storage reservoirs.; Technology and Engineering;

   Sammanfattning : A parsimonious lumped model for simulating a catchment composed of a landfill and recycling facility were developed. The modelling results showed an acceptable conformity with the observed values with NSE of 0.54 and R2 of 0.56 in hourly simulations, though the results were characterized by a general underestimation. LÄS MER

7. 5. Flood modelling in urban areas : A comparative study of MIKE 21 and SCALGO Live

   Master-uppsats, KTH/Hållbar utveckling, miljövetenskap och teknik

   Författare :Evelina Andersson; [2021]
   Nyckelord :SCALGO Live; MIKE 21; Stormwater Model; Flood Model; Cloudburst; Inundation; Comparative;

   Sammanfattning : Pluvial flooding originating from extreme rainfall is problematic and an increasing issue in Sweden. Higher requirements on adapting cities in urban areas to these challenges have been placed on both municipalities and the county administration. Thus, an increased need for water modelling, both in existing and planned areas have emerged. LÄS MER