Problems of Local Flooding in Functional Urban Areas in Poland

Mrozik, Karol

doi:10.3390/w14162453

Cited by 13 publications

(10 citation statements)

References 52 publications

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“…Land cover appears to be a crucial variable that influences the occurrence of flooding. The absence of vegetation creates flooding areas, whereas urban areas covered by impervious materials increase the flow of surface runoff [86][87][88]. In our case, the predicted flooded areas are characterized by the presence of non-irrigated arable land (35%), complex cultivation patterns (27%), annual crops associated with permanent crops (13%), discontinuous urban fabric (8%), and olive groves (5%).…”

Section: Discussionmentioning

confidence: 79%

Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece

et al. 2023

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The main scope of the study is to evaluate the prognostic accuracy of a one-dimensional convolutional neural network model (1D-CNN), in flood susceptibility assessment, in a selected test site on the island of Euboea, Greece. Logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and a deep learning neural network (DLNN) model are the benchmark models used to compare their performance with that of a 1D-CNN model. Remote sensing (RS) techniques are used to collect the necessary flood related data, whereas thirteen flash-flood-related variables were used as predictive variables, such as elevation, slope, plan curvature, profile curvature, topographic wetness index, lithology, silt content, sand content, clay content, distance to faults, and distance to river network. The Weight of Evidence method was applied to calculate the correlation among the flood-related variables and to assign a weight value to each variable class. Regression analysis and multi-collinearity analysis were used to assess collinearity among the flood-related variables, whereas the Shapley Additive explanations method was used to rank the features by importance. The evaluation process involved estimating the predictive ability of all models via classification accuracy, sensitivity, specificity, and area under the success and predictive rate curves (AUC). The outcomes of the analysis confirmed that the 1D-CNN provided a higher accuracy (0.924), followed by LR (0.904) and DLNN (0.899). Overall, 1D-CNNs can be useful tools for analyzing flood susceptibility using remote sensing data, with high accuracy predictions.

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Section: Discussionmentioning

confidence: 79%

Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece

et al. 2023

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“…Between 1980 and 2023, the U.S.A. was affected by 44 catastrophic floods that caused a total of $196.6 billion (consumer price index adjusted) in direct losses (NOAA 2023 ). In upcoming years, flood-related property damage may increase due to climate change (Sastry 2022 ) and sea level rise (Mrozik 2022 ). Consequently, researchers across the globe have focused on flood risk and loss assessments.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Boundaries of Annual Flood Risk for Single-Family Homes Within the 100-Year Floodplain

Al Assi,

Mostafiz,

Friedland

et al. 2024

Int J Environ Res

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Special flood hazard areas (SFHAs), defined as having an annual probability of occurrence of 1 percent or above, are used by U.S. Federal Emergency Management Agency (FEMA) to demarcate areas within which flood insurance purchase is required to secure a mortgage. However, quantifying flood risk within SFHAs can be challenging due to the lack of modeled flood depth data for all return periods. To address this issue, this research quantifies flood risk indicated by average annual loss (AAL) within the A Zone—the subset of the SFHA where wave heights can potentially range from 0 to 3 feet. The methodology resolves the Gumbel quantile function for four distinct flooding cases (i.e., locations flooded at return periods exceeding 1.58-, 10-, 25-, and 50-year return period events) and generates synthetic flood hazard parameters for these cases within the 100-year floodplain, as well as with additional elevation above the base flood elevation (BFE), known as freeboard, for single-family homes with different attributes. The results indicate that for single-family homes in the A Zone, with the lowest floor elevated to the BFE, the AAL ranges from 0.3 to 1 percent of the building replacement cost value. Adding one foot of freeboard reduces flood risk by over 90% if the annual flood risk is between the minimum and 25th percentiles and the 100-year flood depth is less than two feet. The demonstrated approach helps enhance flood resilience in the A Zone, demonstrating the feasibility of proactive measures to protect communities.

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“…The very geometry of these streams, which is often constrained by dense urban development, adds to the complexity of the situation. Further complicating matters is the transformation of many of these natural streams into closed pipe systems within cities [32]. While this may serve immediate urban planning needs, it often results in a hydraulic capacity that is insufficient for handling the large volumes of stormwater brought on by heavy downpours [33,34].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

Starzec,

Kordana-Obuch

2024

Sustainability

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The consequences of climate change include extreme weather events, such as heavy rainfall. As a result, many places around the world are experiencing an increase in flood risk. The aim of this research was to assess the usefulness of selected machine learning models, including artificial neural networks (ANNs) and eXtreme Gradient Boosting (XGBoost) v2.0.3., for predicting peak stormwater levels in a small stream. The innovation of the research results from the combination of the specificity of small watersheds with machine learning techniques and the use of SHapley Additive exPlanations (SHAP) analysis, which enabled the identification of key factors, such as rainfall depth and meteorological data, significantly affect the accuracy of forecasts. The analysis showed the superiority of ANN models (R2 = 0.803–0.980, RMSE = 1.547–4.596) over XGBoost v2.0.3. (R2 = 0.796–0.951, RMSE = 2.304–4.872) in terms of forecasting effectiveness for the analyzed small stream. In addition, conducting the SHAP analysis allowed for the identification of the most crucial factors influencing forecast accuracy. The key parameters affecting the predictions included rainfall depth, stormwater level, and meteorological data such as air temperature and dew point temperature for the last day. Although the study focused on a specific stream, the methodology can be adapted for other watersheds. The results could significantly contribute to improving real-time flood warning systems, enabling local authorities and emergency management agencies to plan responses to flood threats more accurately and in a timelier manner. Additionally, the use of these models can help protect infrastructure such as roads and bridges by better predicting potential threats and enabling the implementation of appropriate preventive measures. Finally, these results can be used to inform local communities about flood risk and recommended precautions, thereby increasing awareness and preparedness for flash floods.

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Problems of Local Flooding in Functional Urban Areas in Poland

Cited by 13 publications

References 52 publications

Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece

Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece

Theoretical Boundaries of Annual Flood Risk for Single-Family Homes Within the 100-Year Floodplain

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

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