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Application of machine learning models in agricultural and meteorological sciences

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Application of machine learning models in agricultural and meteorological sciencesby Mohammad Ehteram, Akram Seifi, Fatemeh Barzegari Banadkooki.
作者:	Ehteram, Mohammad.
其他作者:	Seifi, Akram.
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	xi, 196 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-981-19-9733-4
ISBN:	9789811997334$q(electronic bk.)

Application of machine learning models in agricultural and meteorological sciences
Ehteram, Mohammad.

Application of machine learning models in agricultural and meteorological sciences[electronic resource] /by Mohammad Ehteram, Akram Seifi, Fatemeh Barzegari Banadkooki. - Singapore :Springer Nature Singapore :2023. - xi, 196 p. :ill., digital ;24 cm.

The importance of agricultural and meteorological predictions -- Structure of Particle swarm optimization -- Structure of Shark optimization algorithm -- structure of sunflower optimization algorithm -- Structure of Henry gas solubility optimizer.

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.

ISBN: 9789811997334$q(electronic bk.)

Standard No.: 10.1007/978-981-19-9733-4doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .E38 2022

Dewey Class. No.: 006.31

Application of machine learning models in agricultural and meteorological sciences
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