• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer

Center for Artificial Intelligence and Cybersecurity – AIRI

  • Home
  • About Us
    • Center Activities
    • Vision, Mission and Goals
    • Center Faculty
    • Steering Committee
    • Press
  • Research
    • Scientific Projects
    • Research Papers
  • Laboratories
    • Machine Learning
    • Natural Speech & Language Processing
    • Blockchain Technology
    • Information Processing & Pattern Recognition
    • AI in Medicine
    • Data Mining
    • Computer Vision
    • Complex Networks
    • Human-Computer Interaction
    • Maritime Cybersecurity
    • Autonomous Navigation
    • AI in Mechatronics
    • AI in Education
    • Hybrid Computational Methods
    • Drug Design
    • Legal Aspects of AI
    • Ethically Aligned AI
    • Cultural Complexity
    • Trustworthy and Explainable AI
  • Collaboration
    • Industry Collaboration
    • Industry Projects
    • International Collaboration
  • News
  • Contact
  • Login

Evaluation of Design Storms and Critical Rainfall Durations for Flood Prediction in Partially Urbanized Catchments

21.07.2020

This study investigates and compares several design storms for flood estimation in partially urbanized catchments. Six different design storms were considered: Euler II, Alternating Block Method, Average Variability Method, Huff’s curves, and uniform rainfall. Additionally, two extreme historical storms have been included for comparison. A small, ungauged, partially urbanized catchment in Novigrad (Croatia) was chosen as a study area to account for the infiltration impact on the rainfall-runoff process. The performance of each design storm was assessed based on the flood modelling results, namely the water depth, water velocity, flow rate, and overall flood extent. Also, several rainfall durations were considered to identify a critical scenario. The excess rainfall was computed using the Soil Conservation Service’s Curve Number method, and two-dimensional flooding simulations were performed by the HEC-RAS model. The results confirmed that the choice of the design storm and the rainfall duration have a significant impact on the flood modelling results. Overall, design storms constructed only from IDF curves overestimated flooding in comparison to historical events, whereas design storms derived from the analysis of observed temporal patterns matched or slightly underestimated the flooding results. Of the six considered design storms, the Average Variability Method showed the closest agreement with historical storms.

Authors:
Nino Krvavica, Josip Rubinić
Journal:
Water
Publishing date:
18.07.2020
View original article Download the paper

Primary Sidebar

Latest Projects

Advanced Data Analysis Using Digital Signal Processing and Machine Learning Techniques

Compound Flooding in Coastal Rivers in Present and Future Climate

Data Processing on Graphs

North Adriatic Hydrogen Valley

Data Governance and Intellectual Property Governance in Common European Data Spaces – DGIP-CEDS

Latest Research Papers

Forecasting the Trajectory of Personal Watercrafts Using Models Based on Recurrent Neural Networks

A System for Real-Time Detection of Abandoned Luggage

Enhancing Biophysical Muscle Fatigue Model in the Dynamic Context of Soccer

Pravna tehnologija (Legal Tech) i njezina (ne)prikladnost za zamjenu pravne struke

Regression-Based Machine Learning Approaches for Estimating Discharge from Water Levels in Microtidal Rivers

Latest News

Arian Skoki defended his doctoral thesis “Data-Driven Assessment of Player Performance and Recovery in Soccer”

Anna Maria Mihel defended her PhD dissertation topic

Prof. dr. sc. Renato Filjar participated at the meeting of the 31st National Space-Based Positioning, Navigation and Timing US Advisory Board

Presentation of the NPOO project Peoplet

Ana Vranković Lacković defended her doctoral thesis

We provide the expertise for solving real world problems using AI

If your company wants to implement artificial intelligence in your products or services, or increase your level of cybersecurity, our multidisciplinary team of scientists is your ideal partner.

Contact us

Footer

Center for Artificial Intelligence and Cybersecurity
  • jlerga@airi.uniri.hr
  • +385 51 406 500

University of Rijeka

University of Rijeka

About the Center

  • About Us
  • News
  • Privacy Policy
  • Contact

Center Activities

  • Laboratories
  • Scientific Projects
  • Industry Projects
  • Research Papers
  • Industry Collaboration
  • International Collaboration

Footer bottom left

© 2020 Center for Artificial Intelligence and Cybersecurity, all rights reserved.

Designed & developed by Nela Dunato Art & Design