Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity
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: (...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-03-01
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| Series: | Geoscience Frontiers |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674987120301766 |
| _version_ | 1797712886142861312 |
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| author | Rahman Khatibi Ata Allah Nadiri |
| author_facet | Rahman Khatibi Ata Allah Nadiri |
| author_sort | Rahman Khatibi |
| collection | DOAJ |
| description | 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 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: 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 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. |
| first_indexed | 2024-03-12T07:28:23Z |
| format | Article |
| id | doaj.art-956d004671464db18e0d8acc48478206 |
| institution | Directory Open Access Journal |
| issn | 1674-9871 |
| language | English |
| last_indexed | 2024-03-12T07:28:23Z |
| publishDate | 2021-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geoscience Frontiers |
| spelling | doaj.art-956d004671464db18e0d8acc484782062023-09-02T21:59:15ZengElsevierGeoscience Frontiers1674-98712021-03-01122713724Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneityRahman Khatibi0Ata Allah Nadiri1GTEV-ReX Limited, Swindon, UKDepartment 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; Corresponding author. Department of Earth Sciences, Faculty of Natural Science, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.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 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: 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 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.http://www.sciencedirect.com/science/article/pii/S1674987120301766Artificial intelligenceExclusionary multiple modelling (EMM)Groundwater level predictionInclusive multiple modelling (IMM)Model management practices |
| spellingShingle | Rahman Khatibi Ata Allah Nadiri Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity Geoscience Frontiers Artificial intelligence Exclusionary multiple modelling (EMM) Groundwater level prediction Inclusive multiple modelling (IMM) Model management practices |
| title | Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity |
| title_full | Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity |
| title_fullStr | Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity |
| title_full_unstemmed | Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity |
| title_short | Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity |
| title_sort | inclusive multiple models imm for predicting groundwater levels and treating heterogeneity |
| topic | Artificial intelligence Exclusionary multiple modelling (EMM) Groundwater level prediction Inclusive multiple modelling (IMM) Model management practices |
| url | http://www.sciencedirect.com/science/article/pii/S1674987120301766 |
| work_keys_str_mv | AT rahmankhatibi inclusivemultiplemodelsimmforpredictinggroundwaterlevelsandtreatingheterogeneity AT ataallahnadiri inclusivemultiplemodelsimmforpredictinggroundwaterlevelsandtreatingheterogeneity |