ABSTRACT
Modeling a drought forecasting system is one of the most significant challenges in the field of water resources and environmental engineering that arise as a result of the physical complexity of a natural phenomenon or the time-consuming process needed for analyzing different components of a system. Data-driven models have been found to be very powerful tools that can help overcome such challenges by presenting opportunities to build basic models from the observed patterns as well as accelerating the response of decision-makers in facing with the real-world problems. Since they are able to map causal factors and consequent outcomes of an event without the need for a deep understanding of the physical process surrounding the occurrence of an event, these models have become popular among water resources and environmental engineers. Also, as recent progresses in soft computing have enriched the collection of data-driven techniques by presenting new models as well as enhancing the classic ones, the continuity of such popularity is expected.
