Abstract: This paper presents an extensive review of literature
relevant to the modelling techniques adopted in sediment yield and
hydrological modelling. Several studies relating to sediment yield are
discussed. Many research areas of sedimentation in rivers, runoff and
reservoirs are presented. Different types of hydrological models,
different methods employed in selecting appropriate models for
different case studies are analysed. Applications of evolutionary
algorithms and artificial intelligence techniques are discussed and
compared especially in water resources management and modelling.
This review concentrates on Genetic Programming (GP) and fully
discusses its theories and applications. The successful applications of
GP as a soft computing technique were reviewed in sediment
modelling. Some fundamental issues such as benchmark,
generalization ability, bloat, over-fitting and other open issues
relating to the working principles of GP are highlighted. This paper
concludes with the identification of some research gaps in
hydrological modelling and sediment yield.