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.
Author(s): Mohammad Ehteram, Akram Seifi, Fatemeh Barzegari Banadkooki
Publisher: Springer
Year: 2023
Language: English
Pages: 200
City: Singapore
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
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
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
13 Predicting Temperature Using Optimized Adaptive Neuro-fuzzy Interface System and Bayesian Model Averaging
13.1 Introduction
13.2 Structure of ANFIS Models
13.3 Hybrid ANFIS Models
13.4 Bayesian Model Averaging (BMA)
13.5 Case Study
13.6 Results and Discussion
13.6.1 Determination of the Size of Data
13.6.2 Determination of Random Parameters Values
13.6.3 Evaluation of the Accuracy of Models
13.6.4 Discussion
13.7 Conclusion
References
14 Predicting Evapotranspiration Using Support Vector Machine Model and Hybrid Gamma Test
14.1 Introduction
14.2 Review of Previous Papers
14.3 Structure of Support Vector Machine
14.4 Hybrid SVM Models
14.5 Theory of Gamma Test
14.6 Case Study
14.7 Results and Discussion
14.7.1 Choice of the Algorithm Parameters
14.7.2 The Input Scenarios
14.7.3 Assessment of the Performance of Models
14.7.4 Discussion
14.8 Conclusion
References
15 Predicting Infiltration Using Kernel Extreme Learning Machine Model Under Input and Parameter Uncertainty
15.1 Introduction
15.2 Structure of Kernel Extreme Learning Machines (KELM)
15.3 Hybrid KELM Model
15.4 Uncertainty of Input and Model Parameters
15.5 Case Study
15.6 Results and Discussion
15.6.1 Selection of Size of Data
15.6.2 Choice of Random Parameters of Optimization Algorithms
15.6.3 Evaluation of the Accuracy of Models
15.6.4 Discussion
15.7 Conclusion
References
16 Predicting Solar Radiation Using Optimized Generalized Regression Neural Network
16.1 Introduction
16.2 Structure of Generalized Regression Neural Network (GRNN)
16.3 Structure of Hybrid GRNN
16.4 Case Study
16.5 Results and Discussions
16.5.1 Selection of Random Parameters
16.5.2 Investigation of the Accuracy of Models
16.5.3 Discussion
16.6 Conclusion
References
17 Predicting Wind Speed Using Optimized Long Short-Term Memory Neural Network
17.1 Introduction
17.2 Structure of Long Short-Term Memory (LSTM)
17.3 Hybrid Structure of LSTM Models
17.4 Case Study
17.5 Results and Discussion
17.5.1 Selection of Random Parameters
17.5.2 Choice of Inputs
17.5.3 Investigation of the Accuracy of Models
17.5.4 Discussion
17.6 Conclusion
References
18 Predicting Dew Point Using Optimized Least Square Support Vector Machine Models
18.1 Introduction
18.2 Structure of the LSSVM Model
18.3 Hybrid Structure of the LSSVM Model
18.4 Case Study
18.5 Results and Discussion
18.5.1 Selection of Random Parameters
18.5.2 Selection of the Best Input Combination
18.5.3 Evaluation of the Accuracy of Models
18.6 Conclusion
References
