[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127622-en":3,"doc-seo-127622-105":30,"detail-sidebar-cat-0-en-105":96},{"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},127622,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Comparison of Machine Learning Methods in the Selection of Predictors of Atmospheric-Ocean General Circulation Models - Research Article","Climate change challenges water-resource exploitation and management by altering temperature behavior and precipitation patterns, making temperature variability a critical driver for climatic zoning and classification. This study compares four data-mining algorithms for selecting predictors in statistical downscaling of maximum temperature for Birjand synoptic station over 1961–2019. Using 70% training and 30% validation data and evaluating rNSE, VE, and KGE, results indicate SPSA best reproduces monthly maximum temperatures, while GBM performs strongly under volumetric and efficiency criteria.","Journal of Water and Soil  \n[https://jsw.um.ac.ir](https://jsw.um.ac.ir)  \nResearch Article  \nVol. 37, No. 1, Mar.-Apr. 2023, p. 129-143  \nComparison of Machine Learning Methods in the Selection of Predictors of Atmospheric-Ocean General Circulation Models  \nM. Amirabadizadeh 1*, M. Forozanmehr2, M. Yaghoobzadeh3, S. Hoseinabadi4  \n| Received: 11-05-2022\u003Cbr>Revised: 29-09-2022\u003Cbr>Accepted: 30-11-2022\u003Cbr>Available Online: 30-11-2022 | How to cite this article:\u003Cbr>Amirabadizade, M., Forozanmehr, M., Yaghoobzadeh, M., & Hoseinabadi, S. (2023). Comparison of Machine Learning Methods in the Selection of Predictors of Atmospheric-Ocean General Circulation Models. Journal of Water and Soil 37(1): 129-143. (In Persian with English abstract) .\u003Cbr>[https://doi.org/10.22067/jsw.2022.76605.1166](https://doi.org/10.22067/jsw.2022.76605.1166) |\n| --- | --- |\n\nIntroduction  \nNowadays, climate change is one of the human challenges in the exploitation and management of water resources. Temperature along with precipitation is one of the most important climatic elements and is one of the main factors in zoning and climatic classification. Due to location of Iran within the drought belt and proximity to the high-pressure tropical zone, this country has an arid and semi-arid climate and suffers from drought in majority of years. Therefore, temperature fluctuations and variability are important issues, and make the study of temperature changes a necessity. In the current study, four data mining algorithms in selecting predictors for downscaling of maximum temperature in Birjand synoptic station have been studied , compared and the superior algorithm has been introduced. As the number of large scale features are high, selection of machine learning algorithm will play as an important role in statistical downscaling of climatic variables such as maximum temperature.  \nMaterials and Methods  \nToday, the data set is such that many variables are used to describe the climatic phenomenon in environmental studies. As the number of data is huge, choosing the predictors is one of the most important steps in preprocessing machine learning. In this study, four machine learning methods including stochastic approximation of simultaneous turbulence (SPSA), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Gradient Boosting Method (GBM) in selecting important features in downscaling of maximum temperature in Birjand synoptic station during the statistical period of 1961-2019 were studied and compared. It is a mechanism to find a combination of predictors that with a minimum number of predictors can produce an acceptable evaluation index in estimating the variable under study. For the present study, the weather information of Birjand Synoptic Meteorological Station has been prepared by the Meteorological Organization of Iran. In order to calibrate and validate the machine learning algorithms, 70% and 30% of the available monthly data, respectively, were allocated for this purpose. To conduct this research, coding in R-Studio environment and Caret and Fscaret packages were used. In this study, to evaluate the performance of the algorithms, three indices includes relative Nash-Sutcliffe Efficiency (rNSE), Volume Efficiency (VE) and KlingGupta Efficiency (KGE) were used.  \nResults and Discussion  \nBefore using the algorithms in selecting large-scale predictors, the correlation between these variables and the maximum observational temperature at Birjand station was investigated. Large scale variables mslp, P1_v, P8_v, P8_u, P850 Temp, with a maximum correlation temperature of 0.6 showed that the correlation is acceptable given the complexity of the climate change phenomenon. In addition, these results show that all the  \n1 and 3-Assistant Professor and Associate Professor, Department of Water Engineering, University of Birjand, Birjand, Iran & Members of Drought and Climate Change Research Group  \n(*-[Corresponding Author Email:](Corresponding Author Em","cbCaipQ9cTUcNZFM","https://ap.wps.com/l/cbCaipQ9cTUcNZFM","pdf",2498014,1,15,"English","en",105,"# Introduction\n# Materials and Methods\n# Results and Discussion\n# Conclusion","[{\"question\":\"Which algorithms are compared for predictor selection in maximum temperature downscaling?\",\"answer\":\"The study compares SPSA, LASSO, Ridge, and Gradient Boosting Method (GBM) to select important features for downscaling maximum temperature.\"},{\"question\":\"How is the dataset split for calibrating and validating the models?\",\"answer\":\"Seventy percent of monthly data are allocated for calibration, and thirty percent are used for validation.\"},{\"question\":\"What evaluation indices are used to assess algorithm performance?\",\"answer\":\"Performance is assessed using relative Nash–Sutcliffe Efficiency (rNSE), Volume Efficiency (VE), and Kling–Gupta Efficiency (KGE).\"},{\"question\":\"Which algorithm shows the best overall performance and in what sense?\",\"answer\":\"SPSA shows higher performance in reproducing monthly maximum temperature values and better regenerating mean and variance at the 5% significance level, while GBM is more successful under volumetric efficiency and relative Nash–Sutcliffe criteria.\"}]","Comparison of Machine Learning Methods in the Selection of Predictors of Atmospheric-Ocean General Circulation Models - Research Article | PDF",1785940326,38,{"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":91,"head_meta":93,"extra_data":95,"updated_unix":28},"comparison-of-machine-learning-methods-in-the-selection-of-predictors-of-atmospheric-ocean-general-circulation-models-research-article","",{"@graph":36,"@context":90},[37,54,69],{"@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/comparison-of-machine-learning-methods-in-the-selection-of-predictors-of-atmospheric-ocean-general-circulation-models-research-article/127622/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"Which algorithms are compared for predictor selection in maximum temperature downscaling?","Question",{"text":76,"@type":77},"The study compares SPSA, LASSO, Ridge, and Gradient Boosting Method (GBM) to select important features for downscaling maximum temperature.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the dataset split for calibrating and validating the models?",{"text":81,"@type":77},"Seventy percent of monthly data are allocated for calibration, and thirty percent are used for validation.",{"name":83,"@type":74,"acceptedAnswer":84},"What evaluation indices are used to assess algorithm performance?",{"text":85,"@type":77},"Performance is assessed using relative Nash–Sutcliffe Efficiency (rNSE), Volume Efficiency (VE), and Kling–Gupta Efficiency (KGE).",{"name":87,"@type":74,"acceptedAnswer":88},"Which algorithm shows the best overall performance and in what sense?",{"text":89,"@type":77},"SPSA shows higher performance in reproducing monthly maximum temperature values and better regenerating mean and variance at the 5% significance level, while GBM is more successful under volumetric efficiency and relative Nash–Sutcliffe criteria.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,125,128,133,136,140],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"Exam",70,"exam",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},5,"Comic",60,"comic",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},6,"Technology",50,"technology",{"id":121,"doc_module":4,"doc_module_name":46,"category_name":122,"show_sort_weight":123,"slug":124},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":126,"slug":127},30,"research-report",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":131,"slug":132},9,"Religion & Spirituality",20,"religion-spirituality",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":131,"slug":135},"World Cup","world-cup",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":137,"slug":139},10,"Lifestyle","lifestyle",{"id":141,"doc_module":4,"doc_module_name":46,"category_name":142,"show_sort_weight":111,"slug":143},19,"General","general"]