[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123398-en":3,"doc-seo-123398-105":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":4,"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},123398,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Exploration of Climate Data and Temperature Forecasting using Machine Learning","Exploration of climate-related Tabuk weather data supports machine-learning modeling aimed at forecasting future temperature. Meteorological records spanning 31 years include daily maximum and minimum temperature, mean temperature, rainfall, wind speed, relative humidity, pressure, and vapor. After applying feature visualization and sinusoidal pattern checks with FFT and pair-plot KDE, multiple regression models are trained. Results highlight effective stacking-based prediction while SVM regression underperforms, emphasizing regression formulation over classification and motivating more domain-specific algorithms and human-machine interfaces.","Exploration 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  \nas shown in Fig. 2. To this end, a more subtle verification method was applied using Seaborn Python’s builtin in utility Pair-plot as shown in the Fig. 3. Having said that we prepared the data of these four feature sets available from the Tabouk weather station. The idea was to train the temperature values using different machine learning methods, e.g. Decision Trees (DT), K-Nearest neighbours (KNN), Support Vector Machines (SVMs) and other Deep Learning methods like Multi-Linear Perceptron (MLP) or Artificial Neural Network (ANN) . The intent of this exercise is to realise that whether the features also vary w.r.t the temperature values and also whether their is a similar pattern which exists among the individual features, too.  \nFigure 1 – Five hundred days temperature data points. This can be seen that the data follows sinusoidal curve i.e. a sine wave pattern.  \nFigure 2 – Fast Fourier transformation (FFT) was applied to individual features of the data.  \nFigure 3 – Pair plot of the major features of the temperature data set. The relative kernel density estimation (KDE) was used to find the inter-relation between the features.  \nIn order to verify all these hypothesis we developed different machine learning models, explained in more details in the results section.  \n3. Results  \nThe results are presented after building various machine learning models using different algorithmic techniques, e.g. DT, KNN, SVM and multi-layer perceptron (MLP) which treated the data as a regression problem. The results show that SVM as a regression technique (SVR) for this dataset did not perform well as can be seen in the Fig. 4(a) . The error ","cbCaiebX7jDcItgM","https://ap.wps.com/l/cbCaiebX7jDcItgM","pdf",445633,1,7,"English","en",105,"# Introduction\n# Data Collection and Data Patterns\n# Results\n## Regression models and forecasting performance","[{\"question\":\"What climate data and features are used for temperature forecasting?\",\"answer\":\"The study uses 31 years of Tabuk weather station data 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 task predicts continuous temperature values, so there are no natural classes; converting temperatures to classes is not meaningful, so regression is used instead.\"},{\"question\":\"Which modeling approach shows the strongest forecasting potential?\",\"answer\":\"Stacking-based methods are presented as capable of predicting future temperatures for the Tabuk region, while SVR (SVM regression) does not perform well for this dataset.\"}]","Exploration of Climate Data and Temperature Forecasting using Machine Learning | PDF",1785816280,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"exploration-of-climate-data-and-temperature-forecasting-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/exploration-of-climate-data-and-temperature-forecasting-using-machine-learning/123398/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What climate data and features are used for temperature forecasting?","Question",{"text":75,"@type":76},"The study uses 31 years of Tabuk weather station data 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 task predicts continuous temperature values, so there are no natural classes; converting temperatures to classes is not meaningful, so regression is used instead.",{"name":82,"@type":73,"acceptedAnswer":83},"Which modeling approach shows the strongest forecasting potential?",{"text":84,"@type":76},"Stacking-based methods are presented as capable of predicting future temperatures for the Tabuk region, while SVR (SVM regression) does not perform well for this dataset.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]