[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123783-en":3,"doc-seo-123783-105":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},123783,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Estimation and Forecasting of Evapotranspiration by Machine Learning - Dissertation","Population growth and economic development increase the pressure to produce more food, while freshwater constraints threaten irrigated agriculture. Reference crop evapotranspiration (ETo) serves as the key standard for irrigation scheduling, yet accurate ETo computation depends on sensor measurements over a reference grass surface and varies with region and climate. This dissertation uses publicly available data and modern machine-learning methods to estimate and forecast ETo, with California selected as a hydrologically altered case study.","UC Davis  \nUC Davis Electronic Theses and Dissertations  \nTitle  \nEstimation and Forecasting of Evapotranspiration by Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/6j16g1sx](https://escholarship.org/uc/item/6j16g1sx)  \nAuthor  \nAhmadi, Arman  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nEstimation and Forecasting of Evapotranspiration by Machine Learning  \nBy  \nARMAN AHMADI  \nDISSERTATION  \nSubmitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY  \nin  \nBiological Systems Engineering  \nin the  \nOFFICE OF GRADUATE STUDIES  \nof the  \nUNIVERSITY OF CALIFORNIA  \nDAVIS  \nApproved:  \n\n| Andre Daccache, Chair |\n| --- |\n| Richard L. Snyder |\n\nKosana Suvocarev Committee in Charge 2023  \nAcknowledgments  \nI would like to acknowledge the support of my advisor Dr. Andre Daccache, whose continuous help and trust facilitated this work. I would also like to thank Dr. Richard L. Snyder and Dr. Kosana Suvocarev, other advisory committee members, for their kind help and support. I would like to thank my qualifying exam committee members, Dr. Stavros Vougioukas, Dr. Isaya Kisekka, and Dr. Ali Moghimi. I also acknowledge the sincere hard work of all my educators at UC Davis, Dr. Yufang Jin, Dr. Shrinivasa K. Upadhyaya, Dr. Ruihong Zhang, Dr. Kyaw Tha Paw U, Dr. Erwan Monier, Dr. Kenneth A. Shackel, Dr. David Slaughter, and Dr. Kem Saichaie.  \nDedication  \nTo my family and friends for their love and sacrifice. Especially to my beautiful, loving wife, Ghazaleh, my number one supporter. I know this dissertation is such a relief for her. She is now done listening to me grumbling about my research, at least for some time!  \nAbstract  \nPopulation growth and economic development call for increased food production. Irrigated agriculture is one of the most vital food sources for billions of people worldwide. However, increasing demand for agricultural production and diminished freshwater resources imperil irrigated agriculture's sustainability. Reference crop evapotranspiration (ETo) is the gold standard for farm-level irrigation scheduling. Although reliable, ETo calculations require measurements from different sensors over a reference grass surface.  \nMoreover, the dynamics of ETo and its meteorological driving factors are region-and climatespecific, and change because of climate change. This dissertation aims to leverage the wealth of publicly available data, advances in data science, and state-of-the-art machine learning models to estimate and forecast ETo. California, as one of the most hydrologically altered and agriculturally productive regions of the world, is chosen as the case study of this dissertation.  \nThe first project (Chapter 2) of this dissertation uses three feature importance measures to explore the relative influence of meteorological driving forces of ETo in different climatic zones of California. Moreover, this chapter analyzes the trends of ETo and its driving factors in California from 1986 to 2022. The findings ofthis project suggest that solar radiation and vapor pressure deficit are the most influential driving forces of ETo in California. The trend analysis also demonstrates that California's irrigation-oriented regions are getting hotter and drier, especially during the summer.  \nThe second project (Chapter 3) focuses on monthly ETo forecasting. An accurate monthly ETo forecast is essential for larger-scale water resources management. This chapter employs various  \nforecasting models and strategies to investigate their forecasting accuracy, data efficiency, and computational cost. The findings ofthis project show that statistical forecasting models like Holt-Winters exponential smoothing work as accurately as the cutting-edge deep learning algorithms for monthly ETo forecasting. Moreover, this chapter reveals the lower data efficiency of most deep ","cbCaissQ4FwBv4Qa","https://ap.wps.com/l/cbCaissQ4FwBv4Qa","pdf",6646364,1,165,"English","en",105,"# Acknowledgments\n# Dedication\n# Abstract\n# List of Tables\n# List of Figures\n# Chapter 1: Introduction\n## 1.1. Problem Statement","[{\"question\":\"Why is accurate reference crop evapotranspiration (ETo) important?\",\"answer\":\"ETo is the gold standard for farm-level irrigation scheduling. Reliable scheduling supports sustainable water use under increasing demand and limited freshwater resources.\"},{\"question\":\"How does the dissertation estimate and forecast ETo?\",\"answer\":\"It leverages publicly available datasets, data science methods, and state-of-the-art machine learning models to estimate and forecast ETo across California.\"},{\"question\":\"What do the dissertation’s findings indicate about ETo driving forces and forecasting models?\",\"answer\":\"Solar radiation and vapor pressure deficit are identified as the most influential ETo driving forces in California. For monthly forecasting, statistical models such as Holt-Winters exponential smoothing achieve accuracy comparable to deep learning while using data more efficiently.\"}]","Estimation and Forecasting of Evapotranspiration by Machine Learning - Dissertation | PDF",1785818538,416,{"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},"estimation-and-forecasting-of-evapotranspiration-by-machine-learning-dissertation","",{"@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/estimation-and-forecasting-of-evapotranspiration-by-machine-learning-dissertation/123783/",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},"Why is accurate reference crop evapotranspiration (ETo) important?","Question",{"text":75,"@type":76},"ETo is the gold standard for farm-level irrigation scheduling. Reliable scheduling supports sustainable water use under increasing demand and limited freshwater resources.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation estimate and forecast ETo?",{"text":80,"@type":76},"It leverages publicly available datasets, data science methods, and state-of-the-art machine learning models to estimate and forecast ETo across California.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the dissertation’s findings indicate about ETo driving forces and forecasting models?",{"text":84,"@type":76},"Solar radiation and vapor pressure deficit are identified as the most influential ETo driving forces in California. 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