Model relevance: The model should address the problem effectively by representing the necessary processes, considering the equations used, and understanding the assumptions and limitations of the mathematical model.
Data availability: The model's complexity should align with the availability of data; if data is insufficient, a simpler model might be more appropriate.
Complexity considerations: Complex models are useful for large-scale simulations but require high computational power, extensive input, and a deep understanding of the model, which may introduce errors if not handled carefully.