LEADER 04584nam 22007095 450 001 9910682564603321 005 20251217154723.0 010 $a981-19-9733-0 024 7 $a10.1007/978-981-19-9733-4 035 $a(MiAaPQ)EBC7218112 035 $a(Au-PeEL)EBL7218112 035 $a(OCoLC)1373986223 035 $a(DE-He213)978-981-19-9733-4 035 $a(PPN)269100296 035 $a(CKB)26305236900041 035 $a(EXLCZ)9926305236900041 100 $a20230321d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplication of Machine Learning Models in Agricultural and Meteorological Sciences /$fby Mohammad Ehteram, Akram Seifi, Fatemeh Barzegari Banadkooki 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (201 pages) 311 08$aPrint version: Ehteram, Mohammad Application of Machine Learning Models in Agricultural and Meteorological Sciences Singapore : Springer,c2023 9789811997327 320 $aIncludes bibliographical references. 327 $aThe 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. 330 $aThis 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 predictinghydrological 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. 606 $aMachine learning 606 $aAtmospheric science 606 $aSubsistence farming 606 $aMachine Learning 606 $aAtmospheric Science 606 $aSubsistence Agriculture 606 $aAprenentatge automàtic$2thub 606 $aIntel·ligència artificial$2thub 606 $aAgricultura$2thub 606 $aMeteorologia$2thub 608 $aLlibres electrònics$2thub 615 0$aMachine learning. 615 0$aAtmospheric science. 615 0$aSubsistence farming. 615 14$aMachine Learning. 615 24$aAtmospheric Science. 615 24$aSubsistence Agriculture. 615 7$aAprenentatge automàtic 615 7$aIntel·ligència artificial 615 7$aAgricultura 615 7$aMeteorologia 676 $a006.3 700 $aEhteram$b Mohammad$01254605 702 $aSeifi$b Akram 702 $aBanadkooki$b Fatemeh Barzegari 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910682564603321 996 $aApplication of Machine Learning Models in Agricultural and Meteorological Sciences$93083332 997 $aUNINA