Evolutionary Models for Computing Photosynthetically Active Radiation

This book is intended primarily for students lecturers researchers analysts statistical estimators machine learning and other soft computing evaluators institutions government and non-governmental agencies corporate bodies and the public in the field of photosynthetically active radiation (PAR) renewable energy ecology solar and atmospheric physics atmospheric and meteorological physics plant science agricultural meteorology and horticultural studies etc. to quantify analyze evaluate and model many challenges in a simple and logical way to present a few solutions application of well-understandable and high-tech principles knowledge and knowledge of PAR modeling. The purpose is to take readers'' understanding into consideration of analytical procedure techniques applications and optimization techniques based on empirical algorithm machine learning and evolutionary techniques applied in statistical machine learning modeling; and the confidence and skills necessary to effectively analyze interpret quantify and model PAR data.