[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122989-en":3,"doc-seo-122989-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122989,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Exploration of Climate Data and Temperature Forecasting using Machine Learning","A short communication presents a concept for modeling geographical data to predict future temperatures in Tabuk. Weather-station measurements are used to build a machine-learning prediction model, with preliminary results described as promising. The study frames the task strictly as a regression problem rather than classification, explaining why convolutional neural networks are unsuitable for the absence of temperature classes. The approach targets identifying likely unknown temperature rise trends for the region.","Publication status: Not informed by the submitting author  \nExploration of Climate Data and Temperature Forecasting using  \nMachine Learning  \nEman AlBalawi  \n[https://doi.org/10.1590/SciELOPreprints.9174](https://doi.org/10.1590/SciELOPreprints.9174)  \nSubmitted on: 2024-06-29  \nPosted on: 2024-07-11 (version 1)(YYYY-MM-DD)  \nSciELO Preprints-This document is a preprint and its current status is available at: [https://doi.org/10.1590/SciELOPreprints.9174](https://doi.org/10.1590/SciELOPreprints.9174)  \n[This manuscript is a non-peer reviewed version of a conference paper. Yet to be submitted for](This manuscript is a non-peer reviewed version of a conference paper. Yet to be submitted for)[ ](This manuscript is a non-peer reviewed version of a conference paper. Yet to be submitted for)[publication in](publication in) “Environmental Design, Material Science, and Engineering Technologies 22-25 April 2024, Abu Dhabi University, Dubai Campus, UAE”.  \nExploration of Climate Data and Temperature Forecasting using Machine  \nLearning  \nEman Khalid Al-Balawi*  \nDepartment of Geography, Umm ul Qura University,  \nMakkah, Saudi Arabia  \n*Corresponding author  \n [https://orcid.org/0000-0002-8149-449X](https://orcid.org/0000-0002-8149-449X)  \nAbstract.  \nIn this short communication, a concept has been presented to model geographical data to predict future temperature of Tabuk, region. Machine learning has been applied to the weather station data to develop a prediction model. The preliminary results are promising and encouraging and are envisaging to further this research towards the determination of unknown temperature rise in the region. This is important to mention here, that the problem has been formulated as a Regression problem, NOT as a classification problem. Hence, applying Convolutional neural networks is not possible, due to the non-existence of classes or converting the temperature values to classes does not make any sense. Hence, this is defined as a regression problem which achieved encouraging desirable results.  \nKeywords: Machine learning, Geographical data, Temperature prediction.  \n1. Introduction  \nMachine learning is widely used as a tool to predict or estimate unknown values or conditional attributes for an environment given the historical data. This technique has widely used in different technologies [e.g. computer](e.g. computer) vision, robotics, signal processing, biomedical, aerospace and their associated frameworks [1, 3, 4, 6, 8 , 11, 12] . Many researchers have used machine learning techniques to predict the future temperature values [2, 5, 7, 9, 10] .  \n2. Data Collection and Data Patterns  \nMeteorological data uploaded here covers 31 years of meteorological information obtained from the Tabuk weather radar station (ID 40375), located at 28° 36’ N, 36° 63’E. The data includes Daily Maximum Temperature (Tmax), Minimum Temperature (Tmin), Temperature (mean), Rainfall (RF), Wind Speed (WS), Relative Humidity (RH), Pressure (Press), and Vapor (V), at the station level.  \nThe data was plotted and is shown in the Fig. 1. This can be observed in the data that it follows a sinusoidal wave pattern as shown in Fig. 1. Moreover, the Fast fourier transformation (FFT) was applied on the data features and a very similar pattern can be seen  \nSciELO Preprints-This document is a preprint and its current status is available at: [https://doi.org/10.1590/SciELOPreprints.9174](https://doi.org/10.1590/SciELOPreprints.9174)  \n[This manuscript is a non-peer reviewed version of a conference paper. Yet to be submitted for](This manuscript is a non-peer reviewed version of a conference paper. Yet to be submitted for)[ ](This manuscript is a non-peer reviewed version of a conference paper. Yet to be submitted for)[publication in](publication in) “Environmental Design, Material Science, and Engineering Technologies 22-25 April 2024, Abu Dhabi University, Dubai Campus, UAE”.  \nas shown in Fig. 2. To this end, a more subtle verification m","cbCaiqvSsG18FNmn","https://ap.wps.com/l/cbCaiqvSsG18FNmn","pdf",602336,1,9,"English","en",105,"# Introduction\n# Data Collection and Data Patterns","[{\"question\":\"What geographic and weather data are used in the study?\",\"answer\":\"The model uses 31 years of meteorological station data for Tabuk, including daily maximum and minimum temperature, mean temperature, rainfall, wind speed, relative humidity, pressure, and vapor.\"},{\"question\":\"Why is the problem formulated as regression rather than classification?\",\"answer\":\"The document states that there are no meaningful temperature classes, and converting temperature values into classes would not make sense, so the task is defined as regression only.\"},{\"question\":\"Which machine learning methods are considered for training temperature values?\",\"answer\":\"Training is intended using several approaches including decision trees, K-nearest neighbors, support vector machines, and deep learning options such as MLP and artificial neural networks.\"}]","Exploration of Climate Data and Temperature Forecasting using Machine Learning | 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geographic and weather data are used in the study?","Question",{"text":75,"@type":76},"The model uses 31 years of meteorological station data for Tabuk, including daily maximum and minimum temperature, mean temperature, rainfall, wind speed, relative humidity, pressure, and vapor.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is the problem formulated as regression rather than classification?",{"text":80,"@type":76},"The document states that there are no meaningful temperature classes, and converting temperature values into classes would not make sense, so the task is defined as regression only.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods are considered for training temperature values?",{"text":84,"@type":76},"Training is intended using several approaches including decision trees, K-nearest neighbors, support vector machines, and deep learning options such as MLP and artificial neural 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