What term describes the necessity of using both error-based and uncertainty-based modeling?

The term that best describes the necessity of using both error-based and uncertainty-based modeling is synergistic modeling. This approach emphasizes the integration of different modeling techniques to capture a more comprehensive understanding of geospatial phenomena. By combining error-based modeling, which focuses on quantifying and correcting inaccuracies within the data, with uncertainty-based modeling that accounts for the inherent uncertainties present in data collection and analysis processes, practitioners can produce more robust and reliable geospatial analyses.

Synergistic modeling recognizes that these two aspects of modeling are not mutually exclusive, but rather enhance each other when used together. The aim is to provide a more holistic view of the data, leading to better decision-making and more effective problem-solving in spatial contexts.

Those alternatives, while they may suggest some level of integration or consideration of varying analysis techniques, do not specifically denote the critical combination and interaction necessary between error-based and uncertainty-based models that synergistic modeling encapsulates.