[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124714-en":3,"doc-seo-124714-105":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},124714,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",6,"Technology","Smart Reference Evapotranspiration Using Internet of Things and Hybrid Ensemble Machine Learning Approach","Reference evapotranspiration (ETo) is central to efficient water use in agriculture, yet the standard Penman–Montieth (PM) method is difficult to apply because it relies on a large set of climatic inputs. Existing simplifications often do not adhere to the PM formulation. This work proposes a hybrid ensemble machine learning approach using IoT-sensed crop-field data, combining an artificial neural network (ANN) with regression models to predict daily ETo and adjust it using wind speed, humidity, and sunshine duration.","Internet of Things 24 (2023) 100962  \n| Research article\u003Cbr>Smart reference evapotranspiration using Internet of Things and hybrid ensemble machine learning approach\u003Cbr>Rab Nawaz Bashir a, Mahlaqa Saeed b, Mohammed Al-Sarem c, Rashiq Marie c, Muhammad Faheemd,∗, Abdelrahman Elsharif Karrar c, Bahaeldein Elhussein e\u003Cbr>a Department of Computer Science, COMSATS University Islamabad, Vehari Campus, Vehari, 6100, Pakistan b Department of Computer Science, University of South Asia, Raiwind road, Lahore, 54000, Pakistan c College of Computer Science and Engineering, Taibah University, Madinah, 41411, Saudi Arabia d School of Technology and Innovations, University of Vaasa, Vaasa, 65200, Finland\u003Cbr>e College of Computer and Information Technology, University of Bisha, Bisha, 61922, Saudi Arabia |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O\u003Cbr>Keywords:\u003Cbr>Internet of Things (IoT)\u003Cbr>Hybrid ensemble machine learning model Reference Evapotranspiration (ETo ) Smart irrigation\u003Cbr>Agriculture | A B S T R A C T\u003Cbr>Reference Evapotranspiration (ET􀁯 ) is the cornerstone of efficient water utilization for sustainability in agriculture. The standard Penman–Montieth (PM) approach of Reference Evapotranspiration (ET􀁯 ), is complex due to the involvement of an extensive set of climatic conditions. The existing solutions of simplification of ET􀁯 predictions are not in accordance with the Penman– Montieth approach. A hybrid ensemble machine learning approach for simplification of ET􀁯 prediction is proposed using the Internet of Things(IoT) based crop field sensed climatic data. The proposed hybrid ensemble model is implemented with an Artificial Neural Network (ANN) and regression models. The proposed solution is unique for its utilization of flexible climatic conditions and in accordance with the standard Penman–Montieth (PM) approach. The proposed solution is able to predict daily ET􀁯 from only temperature and also can adjust ET􀁯 according to wind speed, humidity, and sunshine duration. The assessment of the proposed model exhibits a high coefficient of determination (R2) of 0.94 compared to 0.91 from the basic ANN model. The proposed hybrid ensemble model also exhibits a low RMSE of 0.86, MAE of 0.75 mm day−1, and MAPE of 15.05%, compared to 0.91, 0.75 mm day−1, and 20.40% from the basic ANN model. The ET􀁯 predictions by the proposed hybrid ensemble model also exhibit a higher Pearson correlation coefficient of 0.917 with the ET􀁯 by the Penman–Montieth (PM) approach, compared to 0.778 by the basic ANN model. The statistics reveal the accuracy and goodness offit of the proposed hybrid ensemble machine learning model. |  |\n\n1. Introduction  \nAgriculture is the main supplier of human livelihood [1]. The scarcity of natural resources has created serious concerns to feed the world’s increasing population [2]. Agriculture productivity needs to be improved, to serve the basic needs of the large human population [3]. Water scarcity has become a major issue across the world [4,5]. More than sixty-nine (69%) percent of available fresh water on earth is used for agricultural purposes [3]. Around seventy percent (70%) of the water used for agricultural activities, is wasted due to poorly managed agricultural activities. The core cause of wastage of scarce water in agricultural activities is the  \n∗ Corresponding author.  \nE-mail addresses: [rabnawaz@cuivehari.edu.pk](rabnawaz@cuivehari.edu.pk) (R.N. Bashir), [mahlaqa.saeed@usa.edu.pk](mahlaqa.saeed@usa.edu.pk) (M. Saeed), [msarem@taibahu.edu.sa](msarem@taibahu.edu.sa) (M. Al-Sarem),  \n[rmarie@taibahu.edu.sa](rmarie@taibahu.edu.sa) (R. Marie), [muhammad.faheem@uwasa.fi](muhammad.faheem@uwasa.fi) (M. Faheem), [akarrar@taibahu.edu.sa](akarrar@taibahu.edu.sa) (A.E. Karrar), [bmelamin@ub.edu.sa](bmelamin@ub.edu.sa) (B. Elhussein).  \n[https://doi.org/10.1016/j.iot.2023.100962](https://doi.org/10.1016/j.iot.2023.100962)  \nReceived 24 July 2023; Received in revised form 25 September 2023; Accepted 3 October 2","cbCailfx5aySOa9c","https://ap.wps.com/l/cbCailfx5aySOa9c","pdf",2162285,1,16,"English","en",105,"# Introduction\n# Related Work and Motivation\n# Proposed Hybrid Ensemble Methodology\n## IoT-Based Data Collection and Features\n## ANN and Regression Ensemble Design\n# Experimental Setup and Evaluation\n## Accuracy Metrics and Comparisons\n# Results and Discussion\n# Conclusion","[{\"question\":\"Why is reference evapotranspiration prediction difficult with the Penman–Montieth method?\",\"answer\":\"Because the Penman–Montieth approach requires an extensive set of climatic conditions, making it complex and hard to use for precise irrigation scheduling.\"},{\"question\":\"How does the proposed method use Internet of Things (IoT) data?\",\"answer\":\"It leverages IoT-based crop field sensed climatic data to drive a hybrid ensemble model that learns to predict daily reference evapotranspiration.\"},{\"question\":\"What performance improvements does the hybrid ensemble model achieve?\",\"answer\":\"Compared with a basic ANN model, the hybrid ensemble reports a higher R² (0.94 vs 0.91) and lower errors (RMSE 0.86 vs 0.91; MAE 0.75 vs 0.75 mm/day; MAPE 15.05% vs 20.40%), with stronger correlation to PM-based ETo.\"}]","Smart Reference Evapotranspiration Using Internet of Things and Hybrid Ensemble Machine Learning Approach | PDF",1785894061,40,{"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},"smart-reference-evapotranspiration-using-internet-of-things-and-hybrid-ensemble-machine-learning-approach","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/smart-reference-evapotranspiration-using-internet-of-things-and-hybrid-ensemble-machine-learning-approach/124714/",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","2026-08-05",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},"Why is reference evapotranspiration prediction difficult with the Penman–Montieth method?","Question",{"text":75,"@type":76},"Because the Penman–Montieth approach requires an extensive set of climatic conditions, making it complex and hard to use for precise irrigation scheduling.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method use Internet of Things (IoT) data?",{"text":80,"@type":76},"It leverages IoT-based crop field sensed climatic data to drive a hybrid ensemble model that learns to predict daily reference evapotranspiration.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance improvements does the hybrid ensemble model achieve?",{"text":84,"@type":76},"Compared with a basic ANN model, the hybrid ensemble reports a higher R² (0.94 vs 0.91) and lower errors (RMSE 0.86 vs 0.91; 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