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http://hdl.handle.net/10609/126626
Title: | Aplicación de algoritmos predictivos para la eficiencia en la gestión del riego |
Author: | Pousada González, María del Mar |
Tutor: | Crespo García, David |
Others: | Monzon Baeza, Victor |
Abstract: | Efficient management of water resources due to forecast of short-term water shortage has meant an important change, especially in agricultural sector. For this reason, the concepts of precision agriculture and intelligent irrigation have emerged whose purpose is the optimal irrigation management through technology. Based on this, this document presents the design of an intelligent irrigation system from data acquisition with sensors to its treatment. The study focuses on the analysis of a data model established by predictive methods. With this premise, it is possible to determine the need for irrigation for the efficient use of water in irrigation systems. The result establishes the design of an autonomous system that guarantees efficient water management, since it provides added value to conventional irrigation controllers because it includes the analysis of ground conditions, atmospheric conditions, etc. Finally, after proposing the data model, the tests and simulations obtained, it is observed that through predictive models the system models the irrigation management from the training data set, allowing automation, cost savings and the management of an efficient use of water and, consequently, the environmental impact. |
Keywords: | business intelligence IOT efficiency in water management irrigation smart sustainability environment machine learning |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | 10-Jan-2021 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Trabajos finales de carrera, trabajos de investigación, etc. |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Presentación_Algoritmos_Predictivos_Sistema_Riego_Inteligente.pdf | Presentación | 1,29 MB | Adobe PDF | View/Open |
mpousadagTFM0121memoria.pdf | Memoria del TFM | 1,56 MB | Adobe PDF | View/Open |
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