Analysis & forecasting of irrigation decision behavior

Sanyogita Andriyas

ISBN 10: 3659500186 / ISBN 13: 9783659500183

Published by LAP LAMBERT Academic Publishing Dez 2013, 2013

About this Item

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.

Seller Inventory # 9783659500183

Bibliographic Details

• Title: Analysis & forecasting of irrigation decision behavior
• Publisher: LAP LAMBERT Academic Publishing Dez 2013
• Publication Date: 2013
• Language: English
• ISBN 10: 3659500186
• ISBN 13: 9783659500183
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 8D millimeters 220 millimeters width by 150 millimeters height by 8 millimeters depth
• Weight: 203 grams

About this title

Synopsis

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.

About the Author

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.