001461661 000__ 07578cam\a2200685\i\4500
001461661 001__ 1461661
001461661 003__ OCoLC
001461661 005__ 20230503003404.0
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001461661 008__ 230325s2022\\\\si\a\\\\ob\\\\000\0\eng\d
001461661 019__ $$a1373928325
001461661 020__ $$a9789811997334$$qelectronic book
001461661 020__ $$a9811997330$$qelectronic book
001461661 020__ $$z9811997322
001461661 020__ $$z9789811997327
001461661 0247_ $$a10.1007/978-981-19-9733-4$$2doi
001461661 035__ $$aSP(OCoLC)1373986223
001461661 040__ $$aEBLCP$$beng$$erda$$cEBLCP$$dGW5XE$$dYDX$$dEBLCP$$dOCLCF$$dYDX
001461661 049__ $$aISEA
001461661 050_4 $$aQ325.5$$b.E38 2022
001461661 08204 $$a006.3/1$$223/eng/20230330
001461661 1001_ $$aEhteram, Mohammad,$$eauthor.
001461661 24510 $$aApplication of machine learning models in agricultural and meteorological sciences /$$cMohammad Ehteram, Akram Seifi, Fatemeh Barzegari Banadkooki.
001461661 264_1 $$aSingapore :$$bSpringer,$$c[2022]
001461661 300__ $$a1 online resource (xi, 196 pages) :$$billustrations (chiefly color).
001461661 336__ $$atext$$btxt$$2rdacontent
001461661 337__ $$acomputer$$bc$$2rdamedia
001461661 338__ $$aonline resource$$bcr$$2rdacarrier
001461661 500__ $$a13 Predicting Temperature Using Optimized Adaptive Neuro-fuzzy Interface System and Bayesian Model Averaging
001461661 504__ $$aIncludes bibliographical references.
001461661 504__ $$aReferences -- 3 Structure of Shark Optimization Algorithm -- 3.1 Introduction -- 3.2 The Structure of Shark Algorithm -- 3.3 Application of SSO in Climate Studies -- 3.4 Application of SSO in Agricultural Studies -- 3.5 Application of SSO in Other Studies -- 3.6 Conclusion -- References -- 4 Sunflower Optimization Algorithm -- 4.1 Introduction -- 4.2 Applications of SFO in the Different Fields -- 4.3 Structure of Sunflower Optimization Algorithm -- References -- 5 Henry Gas Solubility Optimizer -- 5.1 Introduction -- 5.2 Application of HGSO in Different Fields
001461661 5050_ $$aIntro -- 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
001461661 5058_ $$a5.3 Structure of Henry Gas Solubility -- References -- 6 Structure of Crow Optimization Algorithm -- 6.1 Introduction -- 6.2 The Application of the COA -- 6.3 Mathematical Model of COA -- References -- 7 Structure of Salp Swarm Algorithm -- 7.1 Introduction -- 7.2 The Application of the Salp Swarm Algorithm in Different Fields -- 7.3 Structure of Salp Swarm Algorithm -- References -- 8 Structure of Dragonfly Optimization Algorithm -- 8.1 Introduction -- 8.2 Application of Dragonfly Optimization Algorithm -- 8.3 Structure of Dragonfly Optimization Algorithm -- References
001461661 5058_ $$a9 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
001461661 5058_ $$a11.6.2 Investigation the Accuracy of Models -- 11.6.3 Discussion -- 11.7 Conclusion -- References -- 12 Predicting Rainfall Using Inclusive Multiple Model and Radial Basis Function Neural Network -- 12.1 Introduction -- 12.2 Structure of Radial Basis Function Neural Network (RABFN) -- 12.3 RABFN Models -- 12.4 Structure of Inclusive Multiple Model -- 12.5 Case Study -- 12.6 Results and Discussion -- 12.6.1 Choice of Random Parameters -- 12.6.2 Investigation the Accuracy of Models -- 12.6.3 Discussion -- 12.7 Conclusion -- References
001461661 506__ $$aAccess limited to authorized users.
001461661 520__ $$aThis book is a comprehensive guide for agricultural and meteorological predictions. It presents advanced models for predicting target variables. The different details and conceptions in the modelling process are explained in this book. The models of the current book help better agriculture and irrigation management. The models of the current book are valuable for meteorological organizations. Meteorological and agricultural variables can be accurately estimated with this book's advanced models. Modelers, researchers, farmers, students, and scholars can use the new optimization algorithms and evolutionary machine learning to better plan and manage agriculture fields. Water companies and universities can use this book to develop agricultural and meteorological sciences. The details of the modeling process are explained in this book for modelers. Also this book introduces new and advanced models for predicting hydrological variables. Predicting hydrological variables help water resource planning and management. These models can monitor droughts to avoid water shortage. And this contents can be related to SDG6, clean water and sanitation. The book explains how modelers use evolutionary algorithms to develop machine learning models. The book presents the uncertainty concept in the modeling process. New methods are presented for comparing machine learning models in this book. Models presented in this book can be applied in different fields. Effective strategies are presented for agricultural and water management. The models presented in the book can be applied worldwide and used in any region of the world. The models of the current books are new and advanced. Also, the new optimization algorithms of the current book can be used for solving different and complex problems. This book can be used as a comprehensive handbook in the agricultural and meteorological sciences. This book explains the different levels of the modeling process for scholars.
001461661 588__ $$aDescription based on online resource; title from digital title page (viewed on April 25, 2023).
001461661 650_0 $$aMachine learning.
001461661 650_0 $$aArtificial intelligence$$xAgricultural applications.
001461661 650_0 $$aMeteorology.
001461661 655_0 $$aElectronic books.
001461661 7001_ $$aSeifi, Akram,$$eauthor.
001461661 7001_ $$aBanadkooki, Fatemeh Barzegari,$$eauthor.
001461661 77608 $$iPrint version:$$aEhteram, Mohammad$$tApplication of Machine Learning Models in Agricultural and Meteorological Sciences$$dSingapore : Springer,c2023$$z9789811997327
001461661 852__ $$bebk
001461661 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-9733-4$$zOnline Access$$91397441.1
001461661 909CO $$ooai:library.usi.edu:1461661$$pGLOBAL_SET
001461661 980__ $$aBIB
001461661 980__ $$aEBOOK
001461661 982__ $$aEbook
001461661 983__ $$aOnline
001461661 994__ $$a92$$bISE