Application of machine learning models in agricultural and meteorological sciences / Mohammad Ehteram, Akram Seifi, Fatemeh Barzegari Banadkooki.
Ehteram, Mohammad, author.; Seifi, Akram, author.; Banadkooki, Fatemeh Barzegari, author.
2022
Q325.5 .E38 2022
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Title Application of machine learning models in agricultural and meteorological sciences / Mohammad Ehteram, Akram Seifi, Fatemeh Barzegari Banadkooki.
Author Ehteram, Mohammad, author.
ISBN 9789811997334 electronic book
9811997330 electronic book
9811997322
9789811997327
DOI https://doi.org/10.1007/978-981-19-9733-4
Published Singapore : Springer, [2022]
Language English
Description 1 online resource (xi, 196 pages) : illustrations (chiefly color).
Item Number 10.1007/978-981-19-9733-4 doi
Call Number Q325.5 .E38 2022
Dewey Decimal Classification 006.3/1
Summary This book is a comprehensive guide for agricultural and meteorological predictions. It presents advanced models for predicting target variables. The different details and conceptions in the modelling process are explained in this book. The models of the current book help better agriculture and irrigation management. The models of the current book are valuable for meteorological organizations. Meteorological and agricultural variables can be accurately estimated with this book's advanced models. Modelers, researchers, farmers, students, and scholars can use the new optimization algorithms and evolutionary machine learning to better plan and manage agriculture fields. Water companies and universities can use this book to develop agricultural and meteorological sciences. The details of the modeling process are explained in this book for modelers. Also this book introduces new and advanced models for predicting hydrological variables. Predicting hydrological variables help water resource planning and management. These models can monitor droughts to avoid water shortage. And this contents can be related to SDG6, clean water and sanitation. The book explains how modelers use evolutionary algorithms to develop machine learning models. The book presents the uncertainty concept in the modeling process. New methods are presented for comparing machine learning models in this book. Models presented in this book can be applied in different fields. Effective strategies are presented for agricultural and water management. The models presented in the book can be applied worldwide and used in any region of the world. The models of the current books are new and advanced. Also, the new optimization algorithms of the current book can be used for solving different and complex problems. This book can be used as a comprehensive handbook in the agricultural and meteorological sciences. This book explains the different levels of the modeling process for scholars.
Note 13 Predicting Temperature Using Optimized Adaptive Neuro-fuzzy Interface System and Bayesian Model Averaging
Bibliography, etc. Note Includes bibliographical references.
References -- 3 Structure of Shark Optimization Algorithm -- 3.1 Introduction -- 3.2 The Structure of Shark Algorithm -- 3.3 Application of SSO in Climate Studies -- 3.4 Application of SSO in Agricultural Studies -- 3.5 Application of SSO in Other Studies -- 3.6 Conclusion -- References -- 4 Sunflower Optimization Algorithm -- 4.1 Introduction -- 4.2 Applications of SFO in the Different Fields -- 4.3 Structure of Sunflower Optimization Algorithm -- References -- 5 Henry Gas Solubility Optimizer -- 5.1 Introduction -- 5.2 Application of HGSO in Different Fields
Access Note Access limited to authorized users.
Source of Description Description based on online resource; title from digital title page (viewed on April 25, 2023).
Added Author Seifi, Akram, author.
Banadkooki, Fatemeh Barzegari, author.
Available in Other Form Application of Machine Learning Models in Agricultural and Meteorological Sciences
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Intro
Preface
Contents
1 The Importance of Agricultural and Meteorological Predictions Using Machine Learning Models
1.1 Introduction
1.2 The Necessity of Meteorological Variables Prediction
1.3 The Necessity of Agricultural Factors Prediction
1.4 Conclusion
References
2 Structure of Particle Swarm Optimization (PSO)
2.1 Introduction
2.2 Structure of Particle Swarm Optimization
2.3 The Application of PSO in Meteorological Field
2.4 The Application of PSO in Agricultural Studies
2.5 The Application of PSO in Other Related Studies
2.6 Conclusion

5.3 Structure of Henry Gas Solubility
References
6 Structure of Crow Optimization Algorithm
6.1 Introduction
6.2 The Application of the COA
6.3 Mathematical Model of COA
References
7 Structure of Salp Swarm Algorithm
7.1 Introduction
7.2 The Application of the Salp Swarm Algorithm in Different Fields
7.3 Structure of Salp Swarm Algorithm
References
8 Structure of Dragonfly Optimization Algorithm
8.1 Introduction
8.2 Application of Dragonfly Optimization Algorithm
8.3 Structure of Dragonfly Optimization Algorithm
References

9 Rat Swarm Optimization Algorithm
9.1 Introduction
9.2 Applications of Rat Swarm Algorithm
9.3 Structure of Rat Swarm Optimization Algorithms
References
10 Antlion Optimization Algorithm
10.1 Introduction
10.2 Mathematical Model of ALO
10.3 Mathematical Model of ALO
References
11 Predicting Evaporation Using Optimized Multilayer Perceptron
11.1 Introduction
11.2 Review of the Previous Works
11.3 Structure of MULP Models
11.4 Hybrid MULP Models
11.5 Case Study
11.6 Results and Discussion
11.6.1 Choice of Random Parameters

11.6.2 Investigation the Accuracy of Models
11.6.3 Discussion
11.7 Conclusion
References
12 Predicting Rainfall Using Inclusive Multiple Model and Radial Basis Function Neural Network
12.1 Introduction
12.2 Structure of Radial Basis Function Neural Network (RABFN)
12.3 RABFN Models
12.4 Structure of Inclusive Multiple Model
12.5 Case Study
12.6 Results and Discussion
12.6.1 Choice of Random Parameters
12.6.2 Investigation the Accuracy of Models
12.6.3 Discussion
12.7 Conclusion
References

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