摘要：AbstractAn explicit model management framework is introduced for predictive Groundwater Levels (GWL), particularly suitable to Observation Wells (OWs) with sparse and possibly heterogeneous data. The framework implements Multiple Models (MM) under the architecture of organising them at levels, as follows: (i)Level 0: treat heterogeneity in the data, e.g. Self-Organised Mapping (SOM) to classify the OWs; and decide on model structure, e.g. formulate a grey box model to predict GWLs. (ii)Level 1: construct MMs, e.g. two Fuzzy Logic (FL) and one Neurofuzzy (NF) models. (iii)Level 2:formulate strategies to combine the MM at Level 1, for which the paper uses Artificial Neural Networks (Strategy 1) and simple averaging (Strategy 2). Whilst the above model management strategy is novel, a critical view is presented, according to which modelling practices are: Inclusive Multiple Modelling (IMM) practices contrasted with existing practices, branded by the paper as Exclusionary Multiple Modelling (EMM). Scientific thinking over IMMs is captured as a framework with four dimensions: ModelReuse (MR),Hierarchical Recursion (HR),Elastic Learning Environment (ELE) and GoalOrientation (GO) and these together make the acronym of RHEO. Therefore, IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data. The results provide some evidence that (i) IMM at two levels improves on the accuracy of individual models; and (ii) model combinations in IMM practices bring ‘model-learning’ into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.Graphical abstractDisplay OmittedHighlights•Inclusive Multiple Models (IMM) are piloted for predicting groundwater levels.•IMM is implemented through a framework of four dimensions at Levels 0, 1 and 2.•Dimensions include: Model Reuse, Hierarchical and Recursion and Goal-Orientation.•Level 0 uses SOM; Level 1: SFL, MFL and NF models; Level 2: ANN and simple averages.•A pilot study serves as a proof-of-concept for IMM and for defensibility of results.
