Agricultural Flood Vulnerability Assessment and Risk Quantification in Iowa

Yildirim, Enes; Demir, İbrahim

doi:10.31223/x5vk7p

Cited by 9 publications

(7 citation statements)

References 43 publications

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“…The geomorphic input considered in this study includes the Height Above Nearest Drainage (HAND) index and a land cover map. The HAND method is frequently used to predict flood inundation extents (Hu and Demir, 2021;Li et al, 2022) to support flood mitigation and impact analysis (Alabbad et al, 2021;Yildirim and Demir, 2022). HAND has been applied as a standalone flood mapping approach (Chaudhuri et al, 2021;Li and Demir, 2022a;Li et al, 2023), a supplementary dataset to refine the flood extent extracted from remotely sensed images (Zeng et al, 2020;He et al, 2021;Li and Demir, 2022b), and an independent data layer in flood extent extraction with machine learning approaches (Aristizabal et al, 2020;Bosch et al, 2020;Esfandiari et al, 2020;Liu et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

MA-SARNet: A one-shot nowcasting framework for SAR image prediction with physical driving forces

Li¹,

Xiang²,

Demiray³

et al. 2023

Preprint

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Remote sensing imagery is one of the most widely used data sources for large-scale earth observations with consistent spatial and temporal quality. However, the current usage scenarios of remote sensing images are largely limited to retrospective tasks as they can only capture existing phenomena. This study proposes MA-SARNet, a one-shot forecasting framework built with a modified MA-Net structure and ResNet50 as the backbone, to predict the backscatter values of Synthetic-Aperture Radar (SAR) images using the previous observations, meteorological, and geomorphic data layers as input. The model was trained, validated, and tested with SAR images collected during the catastrophic 2019 Midwest U.S. Floods that affected several states on the Missouri and Mississippi River tributaries. Compared to the benchmark performance, model predictions show an increase of 31.9% and 17.8% for the mean and median AAI (Assemble Accuracy Index) and an increase of 37.9% and 15.1% for the mean and median NSE (Nash-Sutcliffe Efficiency) on the test set. Results showed that the flood extent derived from backscatter predictions is more robust against misclassifications caused by pixel-level noise compared to the flood map derived using the real backscatters and those from two additional flood map repositories. Results from spatial and temporal robustness tests demonstrate that the model has sufficient generalization potential for real-time, physically informed, deep learning-based earth surface prediction tasks to facilitate fast response to and mitigation for future floods.

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Section: Methodsmentioning

confidence: 99%

MA-SARNet: A one-shot nowcasting framework for SAR image prediction with physical driving forces

Li¹,

Xiang²,

Demiray³

et al. 2023

Preprint

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“…While we don't have much control over meteorological events, scientists and engineers are using the tools and methods to inform the public with more accurate weather forecast models, river forecast models, disaster planning (Teague et al, 2021), flood mitigation (Carson et al, 2018;Yildirim and Demir, 2021), and advanced early warning platforms to reduce losses and help the people who may be affected by floods (Alabbad et al, 2022). It's important to use accurate flood forecasting models to figure out how much land will be flooded (Li et al, 2022;Hu and Demir, 2021), as well as how floods will affect transportation networks (Alabbad et al, 2021) and agriculture fields (Yildirim and Demir, 2022).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Streamflow Forecasting Framework, Implementation and Post-Analysis Using Deep Learning

Xiang¹,

Demir²

2022

Preprint

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Rainfall-runoff modeling and streamflow prediction using deep learning algorithms have been studied significantly in the last few years. The majority of these studies focus on the simulation and testing of historical datasets. Deployment and operation of a real-time streamflow forecast model using deep learning will face additional data and computational challenges such as inaccurate rainfall forecast data and real-time data assimilation with limited studies guiding on these difficulties. We proposed a real-time streamflow forecast framework that includes pre-event model training using deep learning, real-time data acquisition, and post-event analysis. We implemented the framework for 124 USGS gauged watersheds across Iowa to forecast 120-hour streamflow rates since April 2021. This is the first time deep learning models have been used to predict streamflow in real-time operational settings at a large scale, and we anticipate seeing more real-time implementations of deep learning models in the future.

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“…Natural disasters are projected to cost more than $300 billion per year in direct asset losses; this figure rises to $520 billion when indirect losses are taken into account (Hallegatte et al 2017;Rentschler and Salhab, 2020). In addition to the economic losses, social insecurity, food security, and production distributions that are challenging to quantify but are undeniable consequences of natural hazards (Haltas et al, 2021;Yildirim and Demir, 2022a;World Bank, 2021). To mitigate the effects of these consequences on communities, decision-makers develop flood preparedness strategies and mitigation action plans in their jurisdictions (Teague et al, 2021;Yildirim, 2017).…”

Section: Introductionmentioning

confidence: 99%

Flood Susceptibility Mapping using Fuzzy Analytical Hierarchy Process for Cedar Rapids, Iowa

Cikmaz¹,

Yildirim²,

Demir³

2022

Preprint

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Floods affect over 2.2 billion people worldwide, and their frequency is increasing at an alarming rate compared to other natural disasters. Presidential disaster declarations have issued increasingly almost every year in Iowa for the past 30 years, indicating that the state is on the rise of flood risk. While significant scientific and technological advancement is becoming available for many flood mitigation activities, their on-the-ground consequences are hampered, among other things, by the lack of tools to quickly integrate the growing data into accessible and usable flood mitigation decisions. A multi-disciplinary approach is required, in which the underlying hydrologic processes that cause floods are closely linked with watershed-level socio-economic functions using effective collaboration tools to ensure community participation in the co-production of mitigation plans while paying attention to socio-environmental justice principles. Considering the existing limitations and needs, we conducted a flood risk assessment by utilizing geophysical and socio-economic datasets for a case study in Cedar Rapids, Iowa. Flood risk outputs are generated based on three main risk groups: geophysical-based flood risk, socioeconomic risk, and combined flood risk. Our results indicate that high- and very-high-risk flood susceptibility zones are primarily located in central urban areas with lower elevations. According to overall results, a large area of Cedar Rapids consists of a medium risk level according to the flood risk map combined with the fuzzy AHP method. The results show that high and very high-risk areas are 16% of the examined area, medium, low and very low-risk areas correspond to 84%. Besides, nearly 40% of the population lives in high to very high flood risk zones.

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Agricultural Flood Vulnerability Assessment and Risk Quantification in Iowa

Cited by 9 publications

References 43 publications

MA-SARNet: A one-shot nowcasting framework for SAR image prediction with physical driving forces

MA-SARNet: A one-shot nowcasting framework for SAR image prediction with physical driving forces

Real-Time Streamflow Forecasting Framework, Implementation and Post-Analysis Using Deep Learning

Flood Susceptibility Mapping using Fuzzy Analytical Hierarchy Process for Cedar Rapids, Iowa

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