Accurate streamflow forecasting has always been a challenge. Although there were many novel approaches using deep learning models, accuracy of these models is often limited to a short lead time. 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. 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. The proposed model outperforms the streamflow persistence, ridge regression and random forest regression on majority of the gages. Our model has shown strong predictive power and can be used for long-term streamflow predictions. This study also shows that the semi-distributed structure in NRM can improve the streamflow predictions by integrating water level data from upstream stream gauges.
•Developed Neural Runoff Model (NRM) using deep learning for 120 h streamflow forecasts.•NRM on 125 USGS stations in Iowa outperforms other machine learning methods.•NRM shows effectiveness in integrating water level data for streamflow forecasts.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Fermatean fuzzy soft set is the most powerful and effective extension of Fermatean fuzzy sets which deals with the parametrized values of the alternatives. It is also a generalization that provides ...better and more precise information in the decision-making process compared to Pythagorean and intuitionistic fuzzy soft sets. In this study, “OR” and “AND” operations and some different algebraic operations will be given. Then, some basic properties have been established for the Fermatean fuzzy soft set employing the improved operations. Furthermore, a decision-making approach has been suggested for the Fermatean fuzzy soft set based on a score matrix. To demonstrate the validity of the proposed approach, a numerical example has been presented.
The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety and velocity of water-related data are increasing due to large-scale sensor networks and increased ...attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research that incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources.
Abstract
Sensors and control technologies are being deployed at unprecedented levels in both urban and rural water environments. Because sensor networks and control allow for higher-resolution ...monitoring and decision making in both time and space, greater discretization of control will allow for an unprecedented precision of impacts, both positive and negative. Likewise, humans will continue to cede direct decision-making powers to decision-support technologies, e.g. data algorithms. Systems will have ever-greater potential to effect human lives, and yet, humans will be distanced from decisions. Combined these trends challenge water resources management decision-support tools to incorporate the concepts of ethical and normative expectations. Toward this aim, 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. We demonstrate this framework with a ‘proof-of-concept’ use case where decision models are learned and deployed to respond to flooding scenarios. Findings indicate that the framework can capture group ‘wisdom’ within learned models to use in decision making. The methodology and ‘proof-of-concept’ system presented here are a step toward building a framework to engage people with algorithmic decision making in cases where ethical preferences are considered. We share our framework and its cyber components openly with the research community.
The height above nearest drainage (HAND) model is frequently used to calculate properties of the soil and predict flood inundation extents. HAND is extremely useful due to its lack of reliance on ...prior data, as only the digital elevation model (DEM) is needed. It is close to optimal, running in linear or linearithmic time in the number of cells depending on the values of the heights. It can predict watersheds and flood extent to a high degree of accuracy. We applied a client-side HAND model on the web to determine extent of flood inundation in several flood prone areas in Iowa, including the city of Cedar Rapids and Ames. We demonstrated that the HAND model was able to achieve inundation maps comparable to advanced hydrodynamic models (i.e., Federal Emergency Management Agency approved flood insurance rate maps) in Iowa, and would be helpful in the absence of detailed hydrological data. The HAND model is applicable in situations where a combination of accuracy and short runtime are needed, for example, in interactive flood mapping and supporting mitigation decisions, where users can add features to the landscape and see the predicted inundation.
We introduce an analytical framework for analyzing tweets to (1) identify and categorize fine-grained details about a disaster such as affected individuals, damaged infrastructure and disrupted ...services; (2) distinguish impact areas and time periods, and relative prominence of each category of disaster-related information across space and time. We first identify disaster-related tweets by generating a human-labeled training dataset and experimenting a series of deep learning and machine learning methods for a binary classification of disaster-relatedness. We employ LSTM (Long Short-Term Memory) networks for the classification task because LSTM networks outperform other methods by considering the whole text structure using long-term semantic word and feature dependencies. Second, we employ an unsupervised multi-label classification of tweets using Latent Dirichlet Allocation (LDA), and identify latent categories of tweets such as affected individuals and disrupted services. Third, we employ spatially-adaptive kernel smoothing and density-based spatial clustering to identify the relative prominence and impact areas for each information category, respectively. Using Hurricane Irma as a case study, we analyze over 500 million keyword-based and geo-located collection of tweets before, during and after the disaster. Our results highlight potential areas with high density of affected individuals and infrastructure damage throughout the temporal progression of the disaster.
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BFBNIB, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The Iowa Flood Information System (IFIS) is a web-based platform developed at the Iowa Flood Center (IFC) in order to provide access to flood inundation maps, real-time flood conditions, flood ...forecasts, flood-related data, information, applications, and interactive visualizations for communities in Iowa. The IFIS provides community-centric watershed and river characteristics, rainfall conditions, and stream-flow data and visualization tools. Interactive interfaces allow access to inundation maps for different stage and return period values as well as to flooding scenarios with contributions from multiple rivers. Real-time and historical data of water levels, gauge heights, hourly and seasonal flood forecasts, and rainfall conditions are made available by integrating data from NEXRAD radars, IFC stream sensors, and USGS and National Weather Service (NWS) stream gauges. The IFIS provides customized flood-related data, information, and visualization for over 1000 communities in Iowa. To help reduce the damage from floods, the IFIS helps communities make better-informed decisions about the occurrence of floods and alerts communities in advance using NWS and IFC forecasts. The integrated and modular design and structure of the IFIS allows easy adaptation of the system in other regional and scientific domains. This paper provides an overview of the design and capabilities of the IFIS that was developed as a platform to provide one-stop access to flood-related information.
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•A comprehensive one-stop web platform is developed for flood related data and information.•Community centric approach provides a customized experience to communities.•The IFIS provides flood warnings, forecasts, inundation maps, and rainfall products.•The IFIS helps communities make better-informed decisions on the occurrence of floods.•The IFIS alerts communities in advance to help them reduce the damage of floods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
An earthquake with a magnitude of 7.7 occurred in Pazarcık District of Turkey at 04.17 on February 6, 2023 and another earthquake of 7.6 occurred at 13.24 on the same day. This is the second largest ...earthquake to have occurred in Turkey. The aim of this study is to investigate the earthquake-related level of knowledge, attitudes and behaviours, general health and psychological status of survivors who were affected by the 2023 Kahramanmaraş Earthquake and who were living in Nurdağı District of Gaziantep after the earthquake.
Data of 2317 individuals older than 18 years of age who were living in earthquake neighbourhoods, tents and containers in Nurdağı District of Gaziantep were examined. Variables were evaluated to find out the demographic characteristics and general health status of earthquake victims. General Health Questionnaire (GHQ-12) was used to find out psychological states of earthquake victims.
The rate of injuries was 14.2% and leg and foot injuries were the most common with 44.2%. The relationship between injury status; and age, marital status, and being trapped under debris was revealed (p < 0.05). Mean GHQ-12 score of the survivors was 3.81 ± 2.81 and 51.9% experienced psychological distress. In the evaluation with logistic regression, it was found that female gender, being injured in the earthquake, loss of first degree and second degree relatives (with a higher rate in loss of first degree relative), having a severely damaged -to be demolished house and having a completely destroyed house were correlated with higher level of psychological distress (p < 0.05).
General characteristics, injury prevalence and affecting factors of earthquake survivors were evaluated in the present study. Psychological distress was found in victims. For this reason, providing protective and assistive services to fight the destructive effects of earthquake is vital. Accordingly, increasing the awareness of people residing in earthquake zones regarding earthquakes is exceptionally important.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
COVID‐19 is a disease characterized by acute respiratory failure and is a major health problem worldwide. Here, we aimed to investigate the role of CD39 expression in Treg cell subsets in COVID‐19 ...immunopathogenesis and its relationship to disease severity. One hundred and ninety COVID‐19 patients (juveniles, adults) and 43 volunteers as healthy controls were enrolled in our study. Flow cytometric analysis was performed using a 10‐color monoclonal antibody panel from peripheral blood samples. In adult patients, CD39+ Tregs increased with disease severity. In contrast, CD39+ Tregs were decreased in juvenile patients in an age‐dependent manner. Overall, our study reveals an interesting profile of CD39‐expressing Tregs in adult and juvenile cases of COVID‐19. Our results provide a better understanding of the possible role of Tregs in the mechanism of immune response in COVID‐19 cases.
Research Highlights
CD39+ Tregs increased with disease severity in adult COVID‐19 cases. In addition, significant changes were also observed in other Treg subsets.
Treg subsets in the juvenile COVID‐19 cases showed age‐related variability but were significantly lower than in the healthy control group.
Consistent correlations were found between laboratory findings in adult COVID‐19 cases and Treg subsets.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds ...and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual assistants by public health departments and organizations. This paper 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. Instant Expert is capable of automatically parsing, processing, and modeling Frequently Asked Questions pages as an information resource as well as communicating with an external knowledge engine for ontology-powered inference and dynamic data use. The presented framework uses advanced web technologies to ensure reusability and reliability, and an inference engine for natural-language understanding powered by deep learning and heuristic algorithms. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented to demonstrate the framework’s usage and benefits.
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