UIHILab Projects

Selected research and educational projects from our research group are listed below:

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A Comprehensive Flood Risk Assessment for the Railroad Network: Case Study for Iowa

Understanding the flood risk to critical infrastructure is important to ensuring the resilience and reliability of essential services during disasters. One of those infrastructures is the railroad network, which is a key aspect of providing goods and services in the United States. Due to a lack of railway assessment during flooding within Iowa, this research aims to analyze the railway network, railway bridges, intersection points of rail and public roads (rail crossings), and rail facilities by using 100 and 500 years of flooding at the state level using GIS-based analysis.

[project website]

Performance of ChatGPT on the US Fundamentals of Engineering Exam: Comprehensive Assessment of Proficiency and Potential Implications for Professional Environmental Engineering Practice

This study investigates the feasibility and effectiveness of using ChatGPT, a GPT-4 based model, in achieving satisfactory performance on the Fundamentals of Engineering (FE) Environmental Exam. This study further shows a significant improvement in the model's accuracy when answering FE exam questions through noninvasive prompt modifications, substantiating the utility of prompt modification as a viable approach to enhance AI performance in educational contexts. Furthermore, the findings reflect remarkable improvements in mathematical capabilities across successive iterations of ChatGPT models, showcasing their potential in solving complex engineering problems. Our paper also explores future research directions, emphasizing the importance of addressing AI challenges in education, enhancing accessibility and inclusion for diverse student populations, and developing AI-resistant exam questions to maintain examination integrity.

[project website]

Platform-Independent and Curriculum-Oriented Intelligent Assistant for Higher Education

We developed an AI-augmented intelligent educational assistance framework based on a powerful language model (i.e., GPT-3) that automatically generates course-specific intelligent assistants regardless of discipline or academic level. The virtual intelligent teaching assistant (TA) system, which is at the core of our framework, serves as a voice-enabled helper capable of answering a wide range of course-specific questions, from curriculum to logistics and course policies. By providing students with easy access to this information, the virtual TA can help to improve engagement and reduce barriers to learning. At the same time, it can also help to reduce the logistical workload for instructors and TAs, freeing up their time to focus on other aspects of teaching and supporting students.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 largWe developed an AI-augmented intelligent educational assistance framework based on a powerful language model (i.e., GPT-3) that automatically generates course-specific intelligent assistants regardless of discipline or academic level. The virtual intelligent teaching assistant (TA) system, which is at the core of our framework, serves as a voice-enabled helper capable of answering a wide range of course-specific questions, from curriculum to logistics and course policies. By providing students with easy access to this information, the virtual TA can help to improve engagement and reduce barriers to learning. At the same time, it can also help to reduce the logistical workload for instructors and TAs, freeing up their time to focus on other aspects of teaching and supporting students.

[project website]

MA-SARNet: A One-Shot Forecasting Framework for SAR Image Prediction with Physical Driving Forces

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.

[project website]

Comparative Analysis of Performance and Mechanisms of Flood Inundation Map Generation using Height Above Nearest Drainage

The National Water Center (NWC) implemented Height Above Nearest Drainage (HAND) for nationwide flood mapping in the continental United States. Although having a large coverage and high accuracy, the implementation (NWCH) relies heavily on the NHDPlus dataset which limits its potential to handle user defined datasets. Comparison of the NWCH model accuracy and computational performance against the original HAND is missing in the literature. This study evaluated the flood maps generated using NWCH and a web-based implementation of the original HAND (WBH). An in-depth sensitivity analysis was conducted for WBH. Results suggest that WBH can generate comparable inundation extent with few inputs in regions where the water depths from the synthetic and catchment rating curves are consistent. Multi-depth approaches help resolve underestimations of WBH. This study demonstrated the original HAND's efficacy in flood mapping and its potential for applications for fast predictions with acceptable accuracy with limited computational resources.

[project website]

HydroLang: An open-source web-based programming framework for hydrological sciences

HydroLang is an open-source web framework designed for hydrology and water resources research and education. It employs client-side web technologies to enable various routines such as acquiring, managing, transforming, analyzing, and visualizing hydrological datasets. HydroLang comprises four modules for retrieving, manipulating, and transforming raw hydrological data, statistical operations, hydrological analysis, and model creation, generating graphical and tabular data representations, and mapping and geospatial data visualization. HydroLang was a winner for the CUAHSI hydroinformatics innovation fellowship and has integrated many data sources from around the world, allowing for easier usage and better integration with current technological trends in hydrology and environmental sciences.

[project website]

River Morphology Information System (RIMORPHIS): A Web-based Cyberinfrastructure for Advancing River Morphology Research

This research puts forth RIMORPHIS, a publicly available research portal for river morphology studies that makes customized and interactive bathymetry-related GIS functionality and data available on the web on-demand with intuitive map-based 2D and 3D visualizations and user interfaces. In order to establish a national watershed reference system to associate gathered bathymetry data, NHDPlus High Resolution (HR) is utilized. Association of localized river morphology data (e.g., processed river cross-section points) with a national reference system enables complex and large watershed level queries for big picture assessments through the RIMORPHIS platform.

[project website]

Towards Progressive Geospatial Information Processing on Web Systems: A Case Study for Watershed Analysis in Iowa

This project presents RasterJS, a web-based geospatial processing library that is built using client-side web technologies and concepts of Progressive Web Application. It transfers data storage, management and processing responsibility to the web-client as opposed to the conventional server-based architecture. The use of modern web technologies like Service Workers API, Fetch API, Cache API, IndexedDB API, Notifications API, Push API, and Web Workers API gives RasterJS the capability to function like both web and native applications. It provides three modes to suit different use cases: Online, Offline, and Web Bundle modes.

[project website]

Watershed-Level Multi-Criteria Quantification of Agricultural Sustainability for Iowa

Agricultural sustainability is a phenomenon that consists of environmental, social, and economic factors. From an environmental standpoint alone, it can be easily seen that modeling sustainable agriculture is a very complex and difficult task, as water distribution, land features, crop diversity, and geographic conditions on watersheds are not uniform both in time and space. In this regard, in this project, we aim to: (1) identify the most effective factors that affect sustainable agriculture, namely climate-resilient agriculture, in Iowa farmland; (2) determine representative subregions of Iowa; and (3) develop a multi-criteria, multi-index, adaptive agricultural sustainability quantification methodology based on identified factors and indicators for these subregions (4) generate an agricultural sustainability index for the Middle Cedar Watershed as a pilot study.

[project website]

Identification of Dominant Factors and Modeling Harmful Algal Bloom Dynamics for Major Lakes in Iowa

Harmful algal blooms (HABs) are a major environmental concern in the US. HABs have a variety of negative effects on public health, recreational services, ecological balance, wildlife, fisheries, microbiota, the economy, and water quality. In this project, we plan to (1) collect data from several sources to investigate which factors are dominant to HABs’ occurrence in Iowa’s major lakes; (2) develop a model to estimate HAB trend for selected lake; and (3) develop a model-driven web-based interactive platform, which consists of sliders for users who can change dominant factors and see the HAB trend.

[project website]

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

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.

[project website]

Agricultural flood vulnerability assessment and risk quantification in Iowa

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. The results show that over $230 million average annualized losses estimated at statewide considering studied crop types. The crop frequency layers and corn suitability rating datasets are investigated to reveal regions with lower or higher productivity ratings.

[project website]

WaterBench: A Large-scale Benchmark Dataset for Data-Driven Streamflow Forecasting

This study proposes a comprehensive benchmark dataset for streamflow forecasting, WaterBench, that follows FAIR data principles that is prepared with a focus on convenience for utilizing in data-driven and machine learning studies, and provides benchmark performance for state-of-art deep learning architectures on the dataset for comparative analysis. By aggregating the datasets of streamflow, precipitation, watershed area, slope, soil types, and evapotranspiration from federal agencies and state organizations (i.e., NASA, NOAA, USGS, and Iowa Flood Center), we provided the WaterBench for hourly streamflow forecast studies.

[project website]

Urban Flood Impact Assessment and Hazard Vulnerability Analysis: Iowa Case Study

The flood impact assessment is considered as a key component of flood risk management strategies, such as benefit-cost analysis for mitigation planning. This study aims to provide a comprehensive assessment of socio-economic impacts for the 100 and 500-year flood scenarios for major Iowa communities. The analysis includes impact on essential facilities, businesses, and vehicles, loss of life, amount of debris transported downstream, displaced population, and floodplain vulnerability classification. Due to the differences in the topography and the spatial distribution of buildings and infrastructure, and the absence of sufficient flood prevention measures within the study area, our findings illustrate that some communities will suffer significant damage and losses during flooding.

[project website]

Flood mitigation data analytics and decision support framework: Iowa Middle Cedar Watershed case study

This research presents a web-based decision support framework called Mitigation and Damage Assessment System (MiDAS) that analyzes flood risk impacts and mitigation strategies at the community and property-level with the goal of informing communities on the consequences of flooding and mitigation alternatives and encouraging them to participate in the community rating system.

[project website]

Flood Markup Language – A Standards-based Exchange Language for Flood Risk Communication

This markup language improves the consistency for flood risk communication across different institutions by establishing a standardized format. It also improves the readability of alerts by providing structured semantics for the flood forecast data. Researchers can easily compare various forecast products for the purpose of (re)analysis. When a flood alert is issued, the emergency department can also obtain information more quickly in order to prepare for a flood. It increases processing efficiency by connecting forecast data and alerts, as well as the speed with which critical information is distributed to the public through automated data processing.

[project website]

High-resolution fully-distributed rainfall-runoff modeling using graph neural network

When compared to baseline models, GNRRM has less over-fitting and significantly improves model performance. Our findings support the importance of hydrological data in deep learning-based rainfall-runoff modeling, and we encourage researchers to include more domain knowledge in their models.

[project website]

Regional semi-distributed deep learning streamflow forecasting model in the state of Iowa

In this project, we developed a generalized model with a multi-site structure for hourly streamflow hindcasts on 125 USGS gauged watersheds in the state of Iowa. Considering watershed-scale features including drainage area, time of concentration, slope, and soil types, the proposed models have acceptable performance and slightly higher median NSE value than training individual models for each USGS station. This study demonstrates the potential of deep learning studies in hydrology where more domain knowledge and physical features can support further generalization.

[project website]

Wayfinding and Accessibility Analysis for Critical Amenities in Iowa During Flood Events

This project provides a comprehensive analysis of flood impacts on city-scale transportation system topology and accessibility in Iowa using graph-theoretic methods, such as single-source shortest path analyses. Accessibility of a road network is evaluated on a digraph by analyzing the ability (alternative routes) to reach the amenities (hospital, fire department, and police station) under various flood return periods as well as the assessment of the difficulty (an increase of shortest distance) of reaching the amenities in the network.

[project website]

Center for Health Effects of Environmental Contamination (CHEEC) Data Exploration System

We have developed a Data Exploration System to improve the productivity of researchers at CHEEC. This system gives access to the data in the CHEEC database through an interactive and easy to use User Interface. With the help of this interface, researchers can navigate, filter, and download the data as needed. Individual Data Tables and most used data queries are two sources of the data which are accessible by the user.

[project website]

Next-generation Stage Measurement at Ungauged Locations Using Internet of Things

This project presents a novel methodology for water level measurement that utilize prevalent sensors commonly found in smart devices. The presented methodology creates a distinct opportunity for a low-cost camera-based embedded system that will measure water levels and share surveys to support environmental monitoring and decision making.

[project website]

A Semantic Web Framework for Automated Smart Assistants: COVID-19 Case Study

This study presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e. chatbots) for any web platform regardless of the underlying domain and technology. The component allows non-technical domain experts to effortlessly incorporate an operational assistant with voice recognition capability into their websites.

[project website]

GeospatialVR: A Web-based Virtual Reality Framework for Collaborative Environmental Simulations

This research introduces GeospatialVR, an open-source collaborative virtual reality framework to dynamically create 3D real-world environments that can be served on any web platform and accessed via desktop and mobile devices and virtual reality headsets. The framework can generate realistic simulations of desired locations entailing the terrain, elevation model, infrastructures, dynamic visualizations (e.g. water and fire simulation), and information layers (e.g. disaster damages and extent, sensor readings, occupancy, traffic, weather).

[project website]

An Ethical Decision-Making Framework with Serious Gaming

We propose the Water Ethics Web Engine, (WE)², an integrated and generalized web framework to incorporate voting-based ethical and normative preferences into water resources decision-support schemes. We then demonstrate the framework with a proof-of-concept use case where decision models are learned and deployed to respond to flooding scenarios. Results indicate the framework can capture group “wisdom” in learned models and use this to make decisions. We share our generalized framework and its cyber components openly with the research community.

[project website]

Easy-to-implement approaches for better flood extents predictions based on HAND

This study demonstrates that the Height Above the Nearest Drainage (HAND) is a useful tool for flood extents predictions. We studied the impacts of three key model parameters (drainage threshold, water depth, and the resolution of data) on the prediction accuracy of the HAND model. Based on the results, we present novel approaches in improving the performance of inundation mapping in a simple and practicable way.

[project website]

Long-term hourly Streamflow Forecasting in the State of Iowa using Deep Learning Models

This study proposes a new deep recurrent neural network approach, Neural Runoff Model (NRM), which has been applied on 125 USGS streamflow gages in the State of Iowa for predicting the next 120 hours. We use a semi-distributed model structure with observation and forecast data from the model output of upstream stations as additional input for downstream gages.

[project website]

Short-term Streamflow Forecasting using Long-short Term Memory and Sequence-to-sequence learning models

We have proposed a deep learning-based prediction model using Long-short Term Memory (LSTM) and the seq2seq structure to estimate hourly rainfall‐runoff for the next 24 hours. The results show that the LSTM‐seq2seq model outperforms linear regression, Lasso regression, Ridge regression, support vector regression, Gaussian processes regression, and LSTM in all stations from these two watersheds. The LSTM‐seq2seq model shows sufficient predictive power and could be used to improve forecast accuracy in short‐term flood forecast applications.

[project website]

Realistic River Image Synthesis using Deep Generative Adversarial Networks

This study investigates an application of image generation for river satellite imagery. Specifically, we propose a generative adversarial network (GAN) model capable of generating high-resolution and realistic river images that can be used to support models in surface water estimation, river meandering, wetland loss and other hydrological research studies.

[project website]

Interactive and Real-time Flood Inundation Mapping on Client-Side Web Systems

We have created a real-time flood inundation map system on the web using height above the nearest drainage (HAND) method. The framework doesn’t require any server-side GIS or database processing. The framework allows users to select an area on the elevation map and generates the corresponding inundation map on the client-side.

[project website]

Building Damage Estimation Using Deep Neural Networks and Satellite Imagery

A great number of disasters have been occurred around the world every year and cause extensive damage in various perspectives. When a disaster happens, it is crucial to determine its region, causes and severity of damage in order to improve the effectiveness of the required response. In this project, we aim to determine the severity of damage and its causes with the help of high-resolution aerial images via deep learning methods fed by pre and post images of affected regions.

[project website]

Enhancing Lidar Data using Generative Adversarial Networks

In this research, power of the GANs are explored to build intelligent systems that understand the issues within DEMs and improve the quality of the LIDAR datasets. Besides the correction, capabilities of GANs in improving the resolution of given DEM or removing noise and objects from DEMs to get bare earth products are studied.

[project website]

Deep Neural Networks Based Augmentation of Rainfall Data

This study aims to employ three neural network architectures, namely, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs) and Gated Recurrent Unit (GRU) Networks to improve radar and satellite-based rainfall products.

[project website]

Decentralized Flood Forecasting Using Deep Neural Networks

While providing models that can be used in forecasting stream stage, this study presents a dataset that focuses on the connectivity of data points on river networks.

[project website]

Web-based Geospatial Platform for Analysis and Forecasting of Sedimentation at Culverts

The “Iowa DOT Culverts” platform aims at informing on the potential for inception and development of sedimentation at culverts. Insights provided through the data-driven framework can be applied to support decisions for culvert management and sedimentation mitigation, as well as to provide suggestions on parameter selections for the design of these structures.

[project website]

Routing and Accessibility Analysis System For Flooding

In this study, a generalized routing and accessibility analysis system is developed for flooding events. The system helps people find alternative routes and understand which areas in the disaster zone have limited travel accessibility. Our system can support emergency response activities with a real-time and dynamic routing framework for evacuation, rescuing civilians, delivering supplies, and deciding emergency center placement.

[project website]

Iowa Well Forecasting System

The Iowa Well Forecasting System provides a user-friendly and interactive system for forecasting aquifer depths using historic well depth information.

[project website]

HSEMD Data Analytics and Visualization Framework

A new data analytics framework is developed for Homeland Security & Emergency Management Department for historical hazard mitigation and public assistance project datasets.

[project website]

Crowdsourcing Water Monitoring with Citizen Science

The goals of the Citizen Science project are determining a mobile app’s ability to accurately monitor water quality throughout a watershed, assessing user interest and engagement in using a mobile app for water quality monitoring, and helping stakeholders identify “hot spots” for potential implementation of water-quality improvement projects.

[project website]

Flood AI Alpha: Artificial Intelligence for Flood Preparedness

Flood Damage and Loss Analysis on an Interactive Web-based System

This web-based tool is aimed to provide a web-based interactive flood damage and loss estimation platform utilizing HAZUS and Census datasets.

[project website]

Flood Action VR

This projects presents a Samsung Gear VR and Oculus Go application that regenerates real world and weather conditions worldwide to increase disaster awareness and to prepare responders by employing gamification techniques.

[project website]

Camera-Based Intelligent Stream Stage Sensing for Decentralized Environmental Monitoring

This project presents a novel method and an AR-based mobile application to accurately measure water levels using smartphones.

[project website]

Iowa Watershed Approach Information System (IWAIS)

he Iowa Watershed Approach (IWA) is a vision for Iowa’s future that voluntarily engages stakeholders throughout the watershed to achieve common goals, while moving toward a more resilient state.

[project website]

Decision Support Tool for the Texas Multi-Hazard Tournament

The web-based MHT described here is different from conventional decision-making approaches where everyone works on the solution together.

[project website]

IowaDSS: Collaborative Decision-Support System for Multi-Hazard Mitigation

We have developed a prototype DSS used for a Multi-Hazard Tournament (MHT) organized by the US Army Corps of Engineers’ Institute for Water Resources in the state of Iowa (U.S.A.).

[project website]

Crowdsourcing Water Quality Monitoring with Smartphones

his project includes utilization of smartphones for monitoring water quality. Volunteers use a nitrate test strip and quantify the environmental variable using a smartphone camera. The data will be send instantly to a central server to visualize and share using a web-based interface and Iowa Water Quality Information System (IWQIS).

[project website]

Holographic Flood Loss and Damage Application

An interactive holographic application is developed to visualize cities in 3D structure during flooding with flood loss and damage estimates from HAZUS datasets for individual buildings.

[project website]

Clear Creek Watershed Information System

We developed an integrated platform, along with properly embedded interfaces, that allows users who lack specialized computer skills to test alternative land-use changes, explore and compare socio-economic analyses, and gauge public action-reaction.

[project website]

Hydrology@Home: A Distributed Voluntary Computing Framework

A generic web-based distributed voluntary computing framework with the aim of easing scientific computing tasks.

[project website]

Mobile Museum - Interactive Digital Exhibit

Interactive rainfall, water flow, watershed delineation, and inundation simulators are created to bring cutting-edge hydrological research to Iowa's commmunities statewide.

[project website]

Overhead Power Line Sag Monitoring through Augmented Reality

This project presents a novel and inexpensive method for overhead power line sag monitoring using augmented reality and image recognition.

[project website]

An Information-Centric Flood Ontology

This project presents an information-centric comprehensive flood ontology that can be utilized in cyberinfrastructure systems for natural hazard preparedness, monitoring, response and recovery.

[project website]

Client-Side GPGPU Web Application for Catchment Delineation

A client-side GPGPU (General Purpose Graphical Processing Unit) algorithm to analyze high-resolution terrain data for watershed delineation which allows parallelization using GPU is developed.

[project website]

NASA SMAP Information System

A field experiment is conducted in Iowa by NASA in collaboration with Iowa Flood Center during the spring of 2016 for SMAP Satellite mission. A web-based interface is developed to integrate data and resources for NASA SMAP satellite mission field campaign in Iowa with rich spatial layers on a map interface.

[project website]

Big Sioux River Flood Information System (BSRFIS)

The Big Sioux River Flood Information System (BSRFIS) provides a user-friendly and interactive platform for Big Sioux River regarding flood conditions, flood forecasts and flood-related data, information and applications.

[project website]

Google Glass Flood Alert Mixed Reality Application

We have imagined how heads up displays can affect our interactions with environmental information, and what we can do with a Google Glass (#ifihadglass) with flood related data and information. Here is a prototype design for a Google Glass Flood App with real-time flood warning and road conditions.

[project website]

Big Data Analytics Using Social Media

A cloud computing infrastructure for acquisition, processing and analyzing social media feeds in real-time for improving flood monitoring and prediction, and supporting flood preparedness, recovery and response. The project utilizes latest web technologies, cloud computing, natural language processing and machine intelligence techniques.

[project website]

Realistic Flood Visualizer

A web-based 3D visualization system is developed for generating realistic flood scence for any location in Iowa.

[project website]

Hydrological Simulation System with VR and AR

A web-based virtual and augmented reality application for simulation of hydrological system using desktop markers and camera via WebRTC and WebGL technologies.

[project website]

Disdrometer Visualization Interface

A web-based interface for visualizing drop size and velocity of rain drops using disdrometer data in real-time for NASA IFLOODS Project.

[project website]

Augmented Reality Layers for Iowa

Augmented Reality (AR) combines real world and simulated information or reality in real-time to enhance perception of reality. Several augmented reality layers are created for the Iowa Flood Center bridge sensor data, and data sources in IFIS. These layers are accessible within the Augmented Reality application, LAYAR, for android and iOS devices.

[project website]

3D Interactive Book about IFIS

A web-based 3D interactive children's book is designed about IFIS and hydrological concepts for educational purposes.

[project website]

UCF CHAMPS Hydrologic Information System

A web-based information system on coastal inundation and modeling data, information and visualizations.

[project website]

Interactive Streamflow Visualization Interface

A web-based interface is developed for visualization of stream flow data on Google Maps using HTML 5 and canvas.

[project website]

NASA IFLOODS Information System

A web-based interface is developed to integrate data and resources for NASA Iowa Flood Studies experiment with rich spatial layers on a map interface.

[project website]

NASA IFLOODS Planning Tool

A web-based interface is developed for positioning radars and rainfall instruments with adjustable range and location, watersheds, and rich spatial layers on a map interface.

[project website]

Flood Inundation Map Flight Simulator

A real-time web-based flight simulator is developed for navigating over the flood inundation map of Des Moines, Iowa. The web-based interface utilizes Google Earth and Google Maps API and allows users to control a helicopter of the flood map using arrow keys. A recent update allows users to control the helicopter using their iOS devices (iPhone, iPad & iPod Touch).

[project website]

Flood Simulator Game

A web-based flood simulator game is developed to teach flood related concepts interactively. The web-based interface utilizes HTML 5 and Canvas and allows users to control a block using mouse or arrow keys to protect city from flooding.

[project website]

Rainfall and Flood Forecast Simulation

A real-time web-based simulation is developed in 2D and 3D using HTML 5 and Canvas for rainfall and flood forecast for Cedar Rapids, Iowa. The web-based interface simulates 552 hours of rainfall and flood conditions in 15 minutes intervals for 8436 spatial locations on the map.

[project website]

Real-time Watershed Delineator

A web-based interface for generating watershed for any location in Iowa.

[project website]

Real-time Rainfall Drainage Tracker

A visualization interface for real-time rainfall drainage tracker is developed. Users are able to click on any location in Iowa and see how a single rain drop drains to the outlet.

[project website]

Rainfall Data Browser

An interactive rainfall data browser is developed for visualizing and browsing real-time and historical rainfal products generated using HYDRO NEXRAD radar data including HRAP, Q2, IFC and HUC 0708. The interface allows users to visualize selected rainfall products, navigate and zoom to areas of interest using mapping interface, and browse through multiple meta-data to identify certain characteristics of the products.

[project website]

Level II Radar Data Browser

An interactive radar raw data browser is developed for visualizing and browsing real-time and historical HYDRO NEXRAD data. The interface allows users to visualize the full volume scan from radar, and vertical and horizontal sectors from the 3D volume scan. Mapping interface allows users to navigate in the spatial domain and zoom to areas of interest.

[project website]

Fund-A-Sensor Initiative

The FUND-A-SENSOR project was established by the Iowa Flood Center (IFC) to crowd-fund stream sensors to improve flood monitoring in Iowa. IFC has already deployed 121 sensors, and is receiving stream levels in real-time, and sharing the data with the public through the Iowa Flood Information System.

[project website]

IFIS Knowledge Engine

IFIS Knowledge Engine is a computational engine to provide answers to questions grouped into several categories using comprehensive set of tools and data resources available in IFIS. IFIS Knowledge Engine connects to distributed sources of real-time stream gauges, and in-house data sources, analysis and visualization tools. The Iowa Flood Information System (IFIS) is a web-based platform with advanced data integration components, analysis and modeling tools, map layers, and rich geo-spatial visualization interfaces.

[project website]

Iowa Flood Information System (IFIS)

The Iowa Flood Information System (IFIS) is a web platform developed by the Iowa Flood Center (IFC) at the University of Iowa. IFIS provides a user-friendly and interactive environment for over 500 communities in Iowa regarding flood conditions, flood forecasts, data visualizations, and flood-related data, information and applications.

[project website]

Global Water Platform (GWP)

The Global Water Platform is developed to (i) make advanced use of information technology to promote information-sharing and collaboration; (ii) provide global leadership in sustainable water use; and (iii) increase awareness of sustainability implications of all water-related programs and activities.

[project website]

Georgia Watershed Information Systems (GWIS)

The Georgia Watershed Information System (GWIS) is a comprehensive online system to manage, access, visualize and analyze large amounts of water quality data and information. GWIS will provide a platform for integration of state-wide efforts in environmental information collection, collation, storage, retrieval and dissemination to all concerned. GWIS provides several data management, modeling, visualization, mapping and resources tools for watersheds.

[project website]

Hydroinformatics Projects