Artificial Intelligence Based Meteorological Parameter Forecasting for Optimizing Response of Nuclear Emergency Decision Support System

This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.

Flood Forecasting GIS Water-Flow Visualization Enhancement (WaVE): A Case Study

Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancement (WaVE), a new framework and toolset that integrates enhanced geospatial analytics visualization (common operating picture) and decision support modular tools. WaVE enables users to: 1) dynamically generate on-the-fly, highly granular and interactive geovisual real-time and predictive flood maps that can be scaled down to show discharge, inundation, water velocity, and ancillary geomorphology and hydrology data from the national level to regional and local level;2) integrate data and model analysis results from multiple sources;3) utilize machine learning correlation indexing to interpolate streamflow proxy estimates for non-functioning streamgages and extrapolate discharge estimates for ungaged streams;and 4) have time-scaled drill-down visualization of real-time and forecasted flood events. Four case studies were conducted to test and validate WaVE under diverse conditions at national, regional and local levels. Results from these case studies highlight some of WaVE’s inherent strengths, limitations, and the need for further development. WaVE has the potential for being utilized on a wider basis at the local level as data become available and models are validated for converting satellite images and data records from remote sensing technologies into accurate streamflow estimates and higher resolution digital elevation models.

Advanced methods of cell phone localization for crisis and emergency management applications

Real-time people localization cannot be achieved through statistical methods during crisis/emergency management events.An International Mobile Subscriber Identity(IMSI)catcher was proposed as a nontraditional method for cell phone-based people localization.We verified the idea of using a cell phone as a sensor and tested the possibility of transmitting cell phone data through the Open Geospatial Consortium(OGC)Sensor Web Enablement.Four large field tests were performed and are described in detail.The main conclusions for IMSI catcher deployment were search within a limited radius from its placement and the number of localized people was not a limiting aspect;although the technology for advanced cell phone-based localization is available for crisis/emergency management applications,we do not yet have sufficient ability to handle this technology.

Coordinated Modulation Strategy Considering Multi-HVDC Emergency for Enhancing Transient Stability of Hybrid AC/DC Power Systems

This paper presents a multi-HVDC emergency coordinated modulating strategy to enhance the transient stability of hybrid AC/DC power systems.First,the main factors that affect the unbalanced energy distribution during a fault are analyzed,and the dominant generators are determined online.Next,considering the influence on both generators in the sending and receiving ends,the assessment index that evaluates the effects of DC power support is established.On the basis of this,a dynamic DC power support strategy is put forward,and the DC support sequence table is promptly updated by the changing dominant generators.The AC/DC hybrid power system with multi-DC lines is built and used as a test system.The simulation results of different scenarios demonstrate that the proposed method could follow the dominant generator dynamically and adjust the DC participating in modulation to enhance the transient stability effectively and quickly.