Predicting flood for any location at times of extreme storms is a longstanding problem that has utmost importance in emergency management. Conventional methods that aim to predict water levels in streams use advanced hydrological models still lack of giving accurate forecasts everywhere. This study aims to explore artificial deep neural networks' performance on flood prediction. While providing models that can be used in forecasting stream stage, this paper presents a dataset that focuses on the connectivity of data points on river networks. It also shows that neural networks can be very helpful in time-series forecasting as in flood events, and support improving existing models through data assimilation.
In this paper, we propose a flood prediction benchmark dataset for future applications in machine learning as well as a scalable approach to forecasting river stage for individual survey points on rivers. The approach takes into account the historical stage data of survey points on selected upstream locations, as well as precipitation data. This approach is both decentralized and doesn't need any historical data from unrelated survey points and can be used in real-time. Recurrent Neural Networks (RNNs), in particular, Gated recurrent unit (GRU) Networks are utilized throughout this study. We also show that this approach presents satisfiable results when it's applied for the state of Iowa as a proof of concept. The data are gathered from the Iowa Flood Center (IFC) and United States Geological Survey (USGS) sensors on the rivers within the state of Iowa. Findings of this project and the deep neural network model will benefit operational cyber platforms @cite_6 , intelligent knowledge systems @cite_13 , and watershed information and visualization tools @cite_27 @cite_14 @cite_15 with enhanced river stage forecasting.
@cite_14 @cite_6 @cite_27 @cite_15 @cite_13
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This paper addresses the challenge of social legitimacy issues for the technical solutions to environmental problems, and the role of Information Systems to resolve such issues. The paper outlines the Georgia Watershed Information System (GWIS), a comprehensive environmental information system, and one of its scientific visualization interfaces. This paper presents a novel scientific visualization tool based on unique components and features of GWIS. The visualization tool uses data and mapping services of GWIS to create dynamic visualizations and animation of water quality observations. A case study is demonstrated for visualizing water quality observations for dry and wet weather conditions on urban Weracoba Creek (Colombus) and its BMP (Best Management Practice), which might help to deal with issues of storm water (storm sewage) pollution control and management. The results show that the scientific visualization interface might support the prospective role of Information Systems in trying to resolve issues of "social legitimacy" surrounding the technical proposals with respect to re-engineering the city's infrastructure. ABSTRACTThis article presents the vision, implementation, and case studies of the Iowa Flood Information System (IFIS) towards the vision for next-generation decision support systems for flooding. ... AbstractIn the spring of 2013, NASA conducted a field campaign known as Iowa Flood Studies (IFloodS) as part of the Ground Validation (GV) program for the Global Precipitation Measurement (GPM) mission. The purpose of IFloodS was to enhance the understanding of flood-related, space-based observations of precipitation processes in events that transpire worldwide. NASA used a number of scientific instruments such as ground-based weather radars, rain and soil moisture gauges, stream gauges, and disdrometers to monitor rainfall events in Iowa. This article presents the cyberinfrastructure tools and systems that supported the planning, reporting, and management of the field campaign and that allow these data and models to be accessed, evaluated, and shared for research. The authors describe the collaborative informatics tools, which are suitable for the network design, that were used to select the locations in which to place the instruments. How the authors used information technology tools for instrument moni... Abstract. In principle, water quality can be managed through an integrative perspective on watersheds, facilitated by a web-based information system. This paper describes a prototype of a web-based Information System for Georgia watersheds called ‘GWIS: Georgia Watershed Information System’. The principal functionality of the system is to provide a platform for integrating state-wide efforts in environmental information collection, collation, storage, analysis, retrieval, and dissemination to all potential stakeholders. Several data management, modeling, visualization, mapping and resource management tools for watersheds, as well as interfaces for integration across diverse and dispersed data sources, are included in the system. As its initial point of departure — to provide substantial and specific content — GWIS has been populated with the high-volume high-quality (HVHQ; near continuous) water quality data acquired during field monitoring campaigns over the past 11 years with the Environmental Process Control Laboratory (EPCL) of the University of Georgia. Information Systems (EIS) modeling with artificial in Abstract Communities are at risk from extreme events and natural disasters that can lead to dangerous situations for residents. Improving resilience by helping people learn how to better prepare for, recover from, and adapt to disasters is critical to reduce the impacts of these extreme events. This project presents an intelligent system, Flood AI, designed to improve societ al preparedness for flooding by providing a knowledge engine that uses voice recognition, artificial intelligence, and natural language processing based on a generalized ontology for disasters with a primary focus on flooding. The knowledge engine uses flood ontology to connect user input to relevant knowledge discovery channels on flooding by developing a data acquisition and processing framework using environmental observations, forecast models, and knowledge bases. The framework’s communication channels include web-based systems, agent-based chatbots, smartphone applications, automated web workflows, and smart home devices, opening the knowledge discovery for flooding to many unique use cases.