Application of Machine Learning Models in Agricultural and Meteorological Sciences

Description

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. 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