Real -Time Geospatial Analysis for Optimizing Response to Dynamic Incidents with Machine Learning

With the increasing interconnectivity and dynamism of the world, the ability to respond swiftly and effectively to emergencies has become paramount. Natural and human-made disasters not only cause extensive damage to public assets but also pose significant risks to human lives. This research aims to optimize the response to dynamic incidents through spatial data analysis and the use of real-time machine learning techniques. To achieve this goal, geospatial data, including satellite imagery, geographic maps, IoT sensor data, and demographic information, were collected alongside temporal data, such as date and time of incidents, weather conditions, traffic data, and real-time social media information. Following data collection, data cleansing and normalization were performed, followed by exploratory data analysis (EDA) and dimensionality reduction techniques, such as Principal Component Analysis (PCA), to identify key features. Machine learning models, including Random Forest and XGBoost, were trained and evaluated using hyperparameter optimization through grid search. Results demonstrated that the XGBoost model, with an accuracy of ۹۲%, outperformed the Random Forest model, which achieved an accuracy of ۸۵%. Feature importance analysis indicated that spatial factors, such as latitude and longitude, weather conditions, and traffic density, had the greatest influence on incident occurrence. Additionally, integrating machine learning models with a Geographic Information System (GIS) enabled the generation of highly accurate early warnings, allowing emergency managers and responders to make effective, timely decisions. This study highlights that combining real-time geospatial analytics with machine learning techniques can significantly enhance the prediction and response to dynamic incidents, thereby reducing human and financial losses.

کلیدواژه ها:

Firefighting ، Crisis Management ، Geospatial Analysis ، Early Warning ، Geographic Information System