Jorge Leandro

University Professor

University of Siegen

Germany

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About

Jorge Leandro is a University Professor at Universität Siegen in Germany. His research primarily focuses on water management, including quantitative microbial risk assessment, flood forecasting, and the impacts of climate change on water availability. Recent publications delve into transboundary water disputes and advanced modeling techniques for infrastructure resilience and turbulent flows.

Articles (19)

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2025

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2025

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2021

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2020

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2020

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2020

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2020

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2020

Collaborators (4)

Felix Schmid

University of Siegen

GERMANY

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Thomas Rötzer

Technical University of Munich

GERMANY

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Brigitte Helmreich

Professor

Technical University of Munich

GERMANY

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Demetris Koutsoyiannis

Professor Emeritus

Ethniko Metsobio Polytechneio

GREECE

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Jorge Leandro

Research Interests

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About

Articles (19)

A quantitative microbial risk assessment framework for cholera infection from post-flood ponds

Enhancing SWMM-UrbanEVA for continuous long-term water balance analysis of green infrastructure

Stochastic–Dynamic Modeling of Chute Slabs Under Spillway Flows

Factors Affecting Transboundary Water Disputes: Nile, Indus, and Euphrates–Tigris River Basins

Influence of age, soil volume, and climate change on water availability at urban tree sites

A Data-Driven Multi-Step Flood Inundation Forecast System

Quantifying hazard resilience by modeling infrastructure recovery as a resource-constrained project scheduling problem

Measuring Turbulent Flows: Analyzing a Stochastic Process with Stochastic Tools

A Feature-Informed Data-Driven Approach for Predicting Maximum Flood Inundation Extends

Development of Rating Curves: Machine Learning vs. Statistical Methods

Expected annual probability of infection: A flood-risk approach to waterborne infectious diseases

Inclusion of Narrow Flow Paths between Buildings in Coarser Grids for Urban Flood Modeling: Virtual Surface Links

Optimization of Artificial Neural Network (ANN) for Maximum Flood Inundation Forecasts

Editorial—Modelling of Floods in Urban Areas

Multistep Flood Inundation Forecasts with Resilient Backpropagation Neural Networks: Kulmbach Case Study

A scalable flood-resilience-index for measuring climate change adaptation: Munich city

Building hazard maps with differentiated risk perception for flood impact assessment

Exploring the relation between flood risk management and flood resilience

Prediction of Maximum Flood Inundation Extents With Resilient Backpropagation Neural Network: Case Study of Kulmbach

Collaborators (4)

Felix Schmid

Thomas Rötzer

Brigitte Helmreich

Demetris Koutsoyiannis

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