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Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity

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An 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 hetero-geneity 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 man-agement 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:Model Reuse (MR), Hierarchical Recursion (HR), Elastic Learning Environment (ELE) and Goal Orientation (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 im-proves 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.

Artificial intelligenceExclusionary multiple modelling (EMM)Groundwater level predictionInclusive multiple modelling (IMM)Model management practices

Ata Allah Nadiri

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Department of Earth Sciences,Faculty of Natural Science,University of Tabriz,29 Bahman Boulevard,Tabriz,East Azerbaijan,Iran

Institute of Environment,University of Tabriz,East Azerbaijan,Iran

This research is one of the outputs of the research group team on Artificial Intelligence Multiple Models, which is financially

scheme808

2021

地学前缘(英文版)
中国地质大学（北京） 北京大学

地学前缘(英文版)

CSTPCDCSCDSCI
影响因子：0.576
ISSN：1674-9871
年,卷(期)：2021.12(2)
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