Rep. Sherrill Introduces Bipartisan Package of Legislation to Address Flooding NJ,Published on: March 1st, 2021

Iowa Flood Center: Preparing Iowans for Increased Flood Risk League of Cities,Published on: February 1st, 2021

AI @ IA: Five Ways Artificial Intelligence Powers Discovery at Iowa Magazine,Published on: March 1st, 2021
IFC Director Krajewski

Krajewski elected to the National Academy of Engineering Now ,Published on: February 10th, 2021

A meteorological‐based crop coefficient model for estimation of daily evapotranspiration

Analysis of six years of micrometeorological records and data revealed strong interactions between relative humidity and evapotranspiration. Daily evapotranspiration estimates for cloudy regions need more information that relies solely on meteorological data, a primary focus of this study.

Distributed long-term hourly streamflow predictions using deep learning – A case study for State of Iowa

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 h due to the difficult nature of accurate streamflow forecasting. The proposed model outperforms the streamflow persistence, ridge regression and random forest regression on majority of the gages. The model has also shown strong predictive power and can be used for long-term streamflow predictions.

A serious gaming framework for decision support on hydrological hazards

In this study, a web-based decision support tool (DST) was developed for hydrological multi-hazard analysis while employing gamification techniques to introduce a competitive element. The serious gaming environment provides functionalities for intuitive management, visualization, and analysis of geospatial, hydrological, and economic data to help stakeholders in the decision-making process regarding hydrological hazard preparedness and response. The framework is an engaging, accessible, and collaborative serious game environment facilitating the relationship between the environment and communities.

Hydrology@Home: a distributed volunteer computing framework for hydrological research and applications

Web-based distributed volunteer computing enables scientists to constitute platforms that can be used for computational tasks by using potentially millions of computers connected to the internet. The framework provides distribution and scaling capabilities for projects with user bases of thousands of volunteers. As a case study, we tested and evaluated the proposed framework with a large-scale hydrological flood forecasting model.

A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

Researchers have been developing physical and machine learning models for decades to predict runoff using rainfall data sets, and this study presents an application of a prediction model based on LSTM and the seq2seq structure to estimate hourly rainfall‐runoff.

LSTM = Long Short-Term Memory

seq2seq = sequence to sequence modeling