Hybrid Models and Multi-model Data Fusion

Abstract

The need for increased accuracy and precision in data-driven models has motivated the researchers to develop innovative models. Hybrid models and multi-model ensemble estimations are applied to increase accuracy and precision of single models. To get an idea about how different models could be combined in a way to increase each other’s abilities, the chapter begins with a summary on the characteristics of the models presented in the previous chapters of the book. The models are compared based on different criteria to give the readers ideas on how to take advantages of the models’ strengths and avoid their weakness through the hybrid models and multi-model data fusion approach. The chapter continues with the examples of hybrid models and general techniques of multi-model data fusion. The approach of multi-model data fusion contains an important process of individual model generation which is going to be discussed in the last section of the chapter.