Geographical Machine Learning for Temperature Forecasting

In this short communication we present the results taken from the real-time weather dataset of Tabouk City, Saudi Arabia. In the results we have applied machine learning techniques to predict the future air temperature of the region. This dataset's results have informed in the creation of determinants driving agricultural and urban expansion contribute to the analysis of the main causes of land use change. This kind of datasets can assist policymakers to understand the importance of a wide range of depleting resources (e.g., groundwater) on the associated agricultural and urban land use change, particularly in arid regions of Saudi Arabian Peninsula.

在本篇简短通讯中，我们展示了来自沙特阿拉伯塔波克市实时气象数据集的结果。在这些结果中，我们应用了机器学习技术来预测该地区未来的空气温度。该数据集的结果已为农业和城市扩张驱动力分析以及土地利用变化主要原因的研究提供了信息。此类数据集有助于政策制定者理解广泛耗竭资源（例如地下水）对相关农业和城市土地利用变化的重要性，尤其是在沙特阿拉伯半岛的干旱地区。