United Arabic Emirates Weather Stations: A Spatial Analysis with myGeoffice©  

作　　者：Joao Negreiros Mohammad Kuhail Joao Negreiros;Mohammad Kuhail(Department of Information Systems and Technology Management (ISTM), College of Technological Innovation, Zayed University, Abu Dhabi, United Arabic Emirates)

机构地区：[1]Department of Information Systems and Technology Management (ISTM), College of Technological Innovation, Zayed University, Abu Dhabi, United Arabic Emirates

出　　处：《Journal of Geoscience and Environment Protection》2024年第12期373-387,共15页地球科学和环境保护期刊(英文)

摘　　要：This paper presents a spatial analysis of weather data from ten stations in the United Arab Emirates (UAE) using myGeoffice©, a web-based Geographical Information System (GIS) tool. This study investigates patterns in rainfall, station connectivity, and the impact of various factors on rainfall prediction. Cluster analysis was applied to classify regions based on rainfall patterns, and algorithms such as Dijkstra’s shortest path and Kruskal’s minimum spanning tree were used to evaluate connectivity among stations. Geographically Weighted Regression (GWR) was employed to model the effects of temperature, humidity, and wind on rainfall. The results indicate that temperature is the dominant factor negatively affecting rainfall, with variations observed across different locations. The study also uses probabilistic models, such as Binomial and Poisson distributions, to predict the likelihood of rainfall and flood occurrences. Overall, the analysis demonstrates the utility of GIS statistical methods in uncovering spatial weather patterns to support more informed decision-making in weather-related studies for the UAE.This paper presents a spatial analysis of weather data from ten stations in the United Arab Emirates (UAE) using myGeoffice©, a web-based Geographical Information System (GIS) tool. This study investigates patterns in rainfall, station connectivity, and the impact of various factors on rainfall prediction. Cluster analysis was applied to classify regions based on rainfall patterns, and algorithms such as Dijkstra’s shortest path and Kruskal’s minimum spanning tree were used to evaluate connectivity among stations. Geographically Weighted Regression (GWR) was employed to model the effects of temperature, humidity, and wind on rainfall. The results indicate that temperature is the dominant factor negatively affecting rainfall, with variations observed across different locations. The study also uses probabilistic models, such as Binomial and Poisson distributions, to predict the likelihood of rainfall and flood occurrences. Overall, the analysis demonstrates the utility of GIS statistical methods in uncovering spatial weather patterns to support more informed decision-making in weather-related studies for the UAE.

关 键 词：United Arabic Emirates Weather Stations Spatial Analysis myGeoffice© 

分 类 号：O15[理学—数学]