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Focused on mango cultivation in the Mediterranean region, the workflow integrates satellite-derived variables such as NDVI, soil moisture, and land surface temperature (LST) with meteorological features like wind speed and direction. A hybrid random-forest and MLP approach captures environmental variability, defining a proxy yield when field yields are unavailable, and demonstrating strong generalization on the 2022 test set.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/an-integrated-hybrid-stochastic-framework-for-agro-meteorological-prediction-under-environmental-uncertainty-read-online-free/291170/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/an-integrated-hybrid-stochastic-framework-for-agro-meteorological-prediction-under-environmental-uncertainty-read-online-free/291170.png","ImageObject",300,407,{"name":92,"@type":93},"Aurelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-07","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What datasets and features are used in the agro-meteorological prediction framework?","Question",{"text":113,"@type":114},"The workflow integrates satellite-derived variables such as NDVI, soil moisture, and land surface temperature (LST), along with meteorological features including wind speed and direction.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is yield prediction handled when direct field yield data are unavailable?",{"text":118,"@type":114},"A proxy yield is defined using key environmental factors, enabling yield estimation without direct field measurements.",{"name":120,"@type":111,"acceptedAnswer":121},"Why does the framework hybridize stochastic modeling with machine learning?",{"text":122,"@type":114},"Stochastic modeling captures random environmental variability, while machine learning (random forest and MLP) improves prediction accuracy for both proxy yield and wind components.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291170,1789643588,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":26,"preview_page_count":4},1099514068365,"https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1794309523707217728","Communication  \nAn Integrated Hybrid-Stochastic Framework for Agro  \nMeteorological Prediction Under Environmental Uncertainty  \nMohsen Pourmohammad Shahvar 1,*, Davide Valenti 1, Alfonso Collura 2, Salvatore Micciche 1, Vittorio Farina 3 and Giovanni Marsella 1  \nAcademic Editor: Wei Zhu  \nReceived: 20 March 2025  \nRevised: 21 April 2025  \nAccepted: 23 April 2025  \nPublished: 25 April 2025  \nCitation: Pourmohammad Shahvar, M.; Valenti, D.; Collura, A.; Micciche, S.; Farina, V.; Marsella, G. An Integrated Hybrid-Stochastic Framework forAgro-Meteorological Prediction Under Environmental Uncertainty. Stats 2025, 8, 30. [https://](https://)[ ](https://)[doi.org/10.3390/stats8020030](doi.org/10.3390/stats8020030)  \n[Copyright:](Copyright:) © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Dipartimento di Fisica e Chimica “E. Segrè”, Università degli Studi di Palermo, 90128 Palermo, Italy; [davide.valenti@unipa.it](davide.valenti@unipa.it) (D.V.); [salvatore.micciche@unipa.it](salvatore.micciche@unipa.it) (S.M.); [giovanni.marsella@unipa.it](giovanni.marsella@unipa.it) (G.M.)  \n2 Istituto Nazionale di Astrofisica, Osservatorio Astronomico di Palermo, 90123 Palermo, Italy; [alfonso.collura@inaf.it](alfonso.collura@inaf.it)  \n3 Dipartimento di Scienze Agrarie, Alimentari e Forestali, Università degli Studi di Palermo,  \n90128 Palermo, Italy; vittorio.farina@unipa.it  \n* [Correspondence: mohsen.pourmohammadshahvar@unipa.it](Correspondence: mohsen.pourmohammadshahvar@unipa.it)  \nAbstract: This study presents a comprehensive framework for agro-meteorological prediction, combining stochastic modeling, machine learning techniques, and environmental feature engineering to address challenges in yield prediction and wind behavior modeling. Focused on mango cultivation in the Mediterranean region, the workflow integrates diverse datasets, including satellite-derived variables such as NDVI, soil moisture, and land surface temperature (LST), along with meteorological features like wind speed and direction. Stochastic modeling was employed to capture environmental variability, while a proxy yield was defined using key environmental factors in the absence of direct field yield measurements. Machine learning models, including random forest and multi-layer perceptron (MLP), were hybridized to improve the prediction accuracy for both proxy yield and wind components (U and V that represent the east–west and north–south wind movement) . The hybrid model achieved mean squared error (MSE) values of 0.333 for U and 0.181 for V, with corresponding R2 values of 0.8939 and 0.9339, respectively, outperforming the individual models and demonstrating reliable generalization in the 2022 test set. Additionally, although NDVI is traditionally important in crop monitoring, its low temporal variability across the observation period resulted in minimal contribution to the final prediction, as confirmed by feature importance analysis. Furthermore, the analysis revealed the significant influence of environmental factors such as LST, precipitable water, and soil moisture on yield dynamics, while wind visualization over digital elevation models (DEMs) highlighted the impact of terrain features on the wind patterns. The results demonstrate the effectiveness of combining stochastic and machine learning approaches in agricultural modeling, offering valuable insights for crop management and climate adaptation strategies.  \nKeywords: stochastic modeling; hybrid machine learning; proxy yield estimation; wind behavior analysis  \n1. Introduction  \nAgro-meteorological prediction is pivotal in modern agriculture, offering insights that enhance crop productivity and mitigate climate-related risks. By integ","cbCaivoDOEN6crqB","https://ap.wps.com/l/cbCaivoDOEN6crqB","pdf",7088837,24,"English","# Introduction\n## Agro-meteorological prediction and data integration\n## Stochastic modeling to handle environmental uncertainty\n## Proxy yield and lack of direct yield measurements\n## Hybrid stochastic and machine learning framework","[{\"question\":\"What datasets and features are used in the agro-meteorological prediction framework?\",\"answer\":\"The workflow integrates satellite-derived variables such as NDVI, soil moisture, and land surface temperature (LST), along with meteorological features including wind speed and direction.\"},{\"question\":\"How is yield prediction handled when direct field yield data are unavailable?\",\"answer\":\"A proxy yield is defined using key environmental factors, enabling yield estimation without direct field measurements.\"},{\"question\":\"Why does the framework hybridize stochastic modeling with machine learning?\",\"answer\":\"Stochastic modeling captures random environmental variability, while machine learning (random forest and MLP) improves prediction accuracy for both proxy yield and wind components.\"}]","An Integrated Hybrid-Stochastic Framework for Agro-Meteorological Prediction Under Environmental Uncertainty - read online free | PDF"]