Real-time Flood Mapping on Client-side Web Systems using HAND model

Hu, Anson; Demir, İbrahim

doi:10.31223/x5m02r

Cited by 8 publications

(3 citation statements)

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“…Terrain-based flood model outputs were generated for a pilot region located in southeastern Iowa using a client-side rapid inundation mapping system (Hu and Demir, 2021;Li and Demir, 2021) based on Height Above Nearest Drainage (HAND) model. The model outputs were generated based on 100-year and 500-year return period flood scenarios.…”

Section: Dem-based Flood Map Evaluationmentioning

confidence: 99%

Agricultural Flood Vulnerability Assessment and Risk Quantification in Iowa

Yildirim¹,

Demir²

2022

Preprint

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Agricultural lands are often impacted by flooding, which results in economic losses and causes food insecurity across the world. Due to the world’s growing population, land-use alteration is frequently practiced to meet global demand. However, land-use changes combined with climate change have resulted in extreme hydrological changes (i.e., flooding and drought) in many areas. The state of Iowa has experienced several flooding events over the last couple of decades (e.g., 1993, 2008, 2014, 2016, 2019). Also, agribusiness is conducted across 85 percent of the state. In this research, we present a comprehensive assessment for agricultural flood risk in the state of Iowa utilizing most up-to-date flood inundation maps and crop layer raster datasets. The study analyzes the seasonal variation of the statewide agricultural flood risk by focusing on corn, soybean, and alfalfa crops. It also investigates the crop frequency layers and corn suitability rating datasets to reveal regions with lower or higher productivity ratings. Additionally, a terrain-based flood model is used to analyze performance against the FEMA maps. The research discusses the potential mitigation activities for the most vulnerable watersheds in the state. The analysis shows that nearly a half-million acres of cornfields and soybean fields are located in the 2-year flood zone. We also found that terrain-based flood maps are a reliable alternative for agricultural flood risk assessment based on their dynamic structure, rapid update capability, and performance compared to FEMA maps.

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Section: Dem-based Flood Map Evaluationmentioning

confidence: 99%

Agricultural Flood Vulnerability Assessment and Risk Quantification in Iowa

Yildirim¹,

Demir²

2022

Preprint

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“…Information systems play an important role in many disciplines, including hydrology (Raj and Lakshmikantha, 2012;Contreras et al, 2014;Pacheco et al, 2021). Traditional data and information sharing systems are limited in their ability to integrate remote sources, visualize, analyze, and communicate modeling results (Hu and Demir, 2021). Because they are usually computation-oriented rather than communication-oriented, these systems deliver either static reports or maps and allow little interaction between data and users.…”

Section: Terminology and Foundationmentioning

confidence: 99%

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

Baydaroğlu¹,

Yeșilköy²,

Sermet³

et al. 2022

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Open science presents a new approach for knowledge discovery, dissemination, and integrity. The idea behind open science is to reinforce research activities and create open knowledge networks by exploring, organizing, and sharing scientific data, as well as making research results transparent, open and integrated. Big data derived from remote sensing, ground-based measurements, models and simulations, social media and crowdsourcing, and wide variety of structured and unstructured sources requires significant efforts for data and knowledge management. Innovations and developments in information technology for the last couple of decades have made data and knowledge management possible for insurmountable amount of data collected and generated over the last decades. This enabled open knowledge networks to be built that lead to new ideas in scientific research and business world. To design and develop open knowledge networks, ontologies are essential since they form the backbone of conceptualization of a given knowledge domain. In this article, a systematic literature review is conducted to examine research involving ontologies related to hydrological processes and water resources management. The hydrologic cycle (water cycle) is a multi-component and multi-process system that is shaped by various dynamic factors. Because all components of the hydrologic cycle interact with one another and water distribution on Earth is not uniform in time and space, modeling the hydrologic cycle and management of hydrologic cycle components are complex and difficult endeavors. Ontologies in the hydrology domain support the comprehension, monitoring, and representation of the hydrologic cycle’s complex structure, as well as the predictions of its processes. They contribute to the development of necessary ontology-based information and decision support systems, the comprehension of environmental and atmospheric phenomena, the development of climate and water resiliency concepts, the creation of educational tools with artificial intelligence, and the strengthening of related cyberinfrastructures. This review provides an explanation of key issues and challenges in ontology development based on hydrologic processes to guide development of next generation artificial intelligence applications. The authors discuss future research prospects in combination with the artificial intelligence and hydroscience.

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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

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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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Real-time Flood Mapping on Client-side Web Systems using HAND model

Cited by 8 publications

References 0 publications

Agricultural Flood Vulnerability Assessment and Risk Quantification in Iowa

Agricultural Flood Vulnerability Assessment and Risk Quantification in Iowa

A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

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

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