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Analysis & forecasting of irrigation decision behavior: Practical machine learning algorithms and guidelines for their implementation - Tapa blanda
Sinopsis
Farmers play an important role in food production. Farmers must make many decisions during the course of a growing season about the allocation of inputs to production. For farmers in arid regions, one of these decisions is whether to irrigate. It is hence vital to investigate the reasons that drive a farmer to make irrigation decisions and use those reasons/factors to forecast future irrigation decisions. This study can help water managers and canal operators to estimate short-term irrigation demands, thereby gaining information that might be useful in management of irrigation supply systems. We introduce three approaches to study farmer irrigation behavior: Bayesian belief networks (BBNs), decision trees, and hidden Markov models (HMMs). All three models are in the class of evolutionary algorithms, which are often used to analyze problems in dynamic and uncertain environments. These algorithms learn the connections between observed input and output data and can make predictions about future events.
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Reseña del editor
Farmers play an important role in food production. Farmers must make many decisions during the course of a growing season about the allocation of inputs to production. For farmers in arid regions, one of these decisions is whether to irrigate. It is hence vital to investigate the reasons that drive a farmer to make irrigation decisions and use those reasons/factors to forecast future irrigation decisions. This study can help water managers and canal operators to estimate short-term irrigation demands, thereby gaining information that might be useful in management of irrigation supply systems. We introduce three approaches to study farmer irrigation behavior: Bayesian belief networks (BBNs), decision trees, and hidden Markov models (HMMs). All three models are in the class of evolutionary algorithms, which are often used to analyze problems in dynamic and uncertain environments. These algorithms learn the connections between observed input and output data and can make predictions about future events.
Biografía del autor
I have expertise in machine learning techniques and their application in the field of water resources engineering and management. I completed my Ph.D. in Civil and Environmental Engineering with a major in Water Resources Engg. and Hydrology. Before that my Masters degree was in Water Resources Engg. and Bachelor's degree was in Agriculture Engg.
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Analysis & forecasting of irrigation decision behavior
Publicado por
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659500186
ISBN 13: 9783659500183
Nuevo
Tapa blanda
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Andriyas SanyogitaI have expertise in machine learning techniques and their application in the field of water resources engineering and management. I completed my Ph.D. in Civil and Environmental Engineering with a major in Water Res. Nº de ref. del artículo: 5160344
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Analysis & forecasting of irrigation decision behavior
Publicado por
LAP LAMBERT Academic Publishing Dez 2013, 2013
ISBN 10: 3659500186
ISBN 13: 9783659500183
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Farmers play an important role in food production. Farmers must make many decisions during the course of a growing season about the allocation of inputs to production. For farmers in arid regions, one of these decisions is whether to irrigate. It is hence vital to investigate the reasons that drive a farmer to make irrigation decisions and use those reasons/factors to forecast future irrigation decisions. This study can help water managers and canal operators to estimate short-term irrigation demands, thereby gaining information that might be useful in management of irrigation supply systems. We introduce three approaches to study farmer irrigation behavior: Bayesian belief networks (BBNs), decision trees, and hidden Markov models (HMMs). All three models are in the class of evolutionary algorithms, which are often used to analyze problems in dynamic and uncertain environments. These algorithms learn the connections between observed input and output data and can make predictions about future events. 124 pp. Englisch. Nº de ref. del artículo: 9783659500183
Cantidad disponible: 2 disponibles
Imagen del vendedor
Analysis & forecasting of irrigation decision behavior : Practical machine learning algorithms and guidelines for their implementation
Publicado por
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659500186
ISBN 13: 9783659500183
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Farmers play an important role in food production. Farmers must make many decisions during the course of a growing season about the allocation of inputs to production. For farmers in arid regions, one of these decisions is whether to irrigate. It is hence vital to investigate the reasons that drive a farmer to make irrigation decisions and use those reasons/factors to forecast future irrigation decisions. This study can help water managers and canal operators to estimate short-term irrigation demands, thereby gaining information that might be useful in management of irrigation supply systems. We introduce three approaches to study farmer irrigation behavior: Bayesian belief networks (BBNs), decision trees, and hidden Markov models (HMMs). All three models are in the class of evolutionary algorithms, which are often used to analyze problems in dynamic and uncertain environments. These algorithms learn the connections between observed input and output data and can make predictions about future events. Nº de ref. del artículo: 9783659500183
Cantidad disponible: 1 disponibles
Imagen del vendedor
Analysis & forecasting of irrigation decision behavior
Publicado por
LAP LAMBERT Academic Publishing Dez 2013, 2013
ISBN 10: 3659500186
ISBN 13: 9783659500183
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Farmers play an important role in food production. Farmers must make many decisions during the course of a growing season about the allocation of inputs to production. For farmers in arid regions, one of these decisions is whether to irrigate. It is hence vital to investigate the reasons that drive a farmer to make irrigation decisions and use those reasons/factors to forecast future irrigation decisions. This study can help water managers and canal operators to estimate short-term irrigation demands, thereby gaining information that might be useful in management of irrigation supply systems. We introduce three approaches to study farmer irrigation behavior: Bayesian belief networks (BBNs), decision trees, and hidden Markov models (HMMs). All three models are in the class of evolutionary algorithms, which are often used to analyze problems in dynamic and uncertain environments. These algorithms learn the connections between observed input and output data and can make predictions about future events.Books on Demand GmbH, Überseering 33, 22297 Hamburg 124 pp. Englisch. Nº de ref. del artículo: 9783659500183
Cantidad disponible: 1 disponibles