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Urban Flooding Monitoring Using Remote Sensing

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Special Issue Editors

Dr. Davide Notti

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Guest Editor

Geohazard Monitoring Group, Research Institute for Hydrogeological Prevention and Protection, National Research Council, Turin, Italy
Interests: landslide; flood mapping; subsidence; geographic information systems; DInSAR data for natural hazard monitoring; applied remote sensing
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Dr. Guido Minucci

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Guest Editor

Politecnico di Milano, Architecture and Urban Studies Department, Piazza Leonardo da Vinci, 26, 200133 Milan, Italy
Interests: flood risk assessment; post-damage assessment; disaster forensic investigation; collaborative mapping

Special Issue Information

Dear Colleagues,

About half of humanity currently lives an urban environment, and in the future, the percentage of people living in an urban area will increase, especially in developing countries (United Nations, 2018).
Floods are a major threat to urban areas, causing death and a considerable amount of damage to infrastructure. In the future, according to the IPCC report (IPCC, 2018), climate change will increase the probability of floods for many urban areas due to extreme events and a rising sea level (Scott and Strauss, 2019)
Nowadays, remote sensing data and techniques (e.g., high-resolution data from optical and SAR satellites, LIDAR, and UAV) provide essential help for mapping and studying urban floods.
The flooding of urban areas, however, remains a big challenge for remote sensing techniques and researchers, involving complex topography, rapid changes, and river management action (e.g., new embankment) that must be detected and mapped to obtain quality flood assessment.
Mapping the extension, the depth and the velocity of flooded water by remote sensing data and techniques should provide contributions to the urban managers, emergency planning managers insurers, and decision-makers. They need these research outputs to plan better science-based urban planning, early warning decisions, and mitigation and adaptation strategies.
This Special Issue seeks innovative and original studies that use remote sensing techniques and datasets to study the best data and the best methodology to map, model, and forecast flooded areas. Furthermore, the Special Issue aims to collect papers on studies describing how remote sensing data and techniques inform and support the decision-making process in the different phases of the disaster management cycle.

Dr. Davide Notti
Dr. Guido Minucci
Guest Editors

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Keywords

urban flood assessment
InSAR
UAV
LIDAR
high-resolution satellite images
flood management
damage assessment
response assistance

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Research

30 pages, 7418 KiB

Open AccessArticle

Modeling Spatial Flood using Novel Ensemble Artificial Intelligence Approaches in Northern Iran

by Alireza Arabameri, Sunil Saha, Kaustuv Mukherjee, Thomas Blaschke, Wei Chen, Phuong Thao Thi Ngo and Shahab S. Band

Remote Sens. 2020, 12(20), 3423; https://doi.org/10.3390/rs12203423 - 18 Oct 2020

Cited by 43 | Viewed by 4815

Abstract

The uncertainty of flash flood makes them highly difficult to predict through conventional models. The physical hydrologic models of flash flood prediction of any large area is very difficult to compute as it requires lot of data and time. Therefore remote sensing data based models (from statistical to machine learning) have become highly popular due to open data access and lesser prediction times. There is a continuous effort to improve the prediction accuracy of these models through introducing new methods. This study is focused on flash flood modeling through novel hybrid machine learning models, which can improve the prediction accuracy. The hybrid machine learning ensemble approaches that combine the three meta-classifiers (Real AdaBoost, Random Subspace, and MultiBoosting) with J48 (a tree-based algorithm that can be used to evaluate the behavior of the attribute vector for any defined number of instances) were used in the Gorganroud River Basin of Iran to assess flood susceptibility (FS). A total of 426 flood positions as dependent variables and a total of 14 flood conditioning factors (FCFs) as independent variables were used to model the FS. Several threshold-dependent and independent statistical tests were applied to verify the performance and predictive capability of these machine learning models, such as the receiver operating characteristic (ROC) curve of the success rate curve (SRC) and prediction rate curve (PRC), efficiency (E), root-mean square-error (RMSE), and true skill statistics (TSS). The valuation of the FCFs was done using AdaBoost, frequency ratio (FR), and Boosted Regression Tree (BRT) models. In the flooding of the study area, altitude, land use/land cover (LU/LC), distance to stream, normalized differential vegetation index (NDVI), and rainfall played important roles. The Random Subspace J48 (RSJ48) ensemble method with an area under the curve (AUC) of 0.931 (SRC), 0.951 (PRC), E of 0.89, sensitivity of 0.87, and TSS of 0.78, has become the most effective ensemble in predicting the FS. The FR technique also showed good performance and reliability for all models. Map removal sensitivity analysis (MRSA) revealed that the FS maps have the highest sensitivity to elevation. Based on the findings of the validation methods, the FS maps prepared using the machine learning ensemble techniques have high robustness and can be used to advise flood management initiatives in flood-prone areas. Full article

(This article belongs to the Special Issue Urban Flooding Monitoring Using Remote Sensing)

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