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Record W1518520608

MODELING OF GROUNDWATER FLOW AND DRAWDOWN EVOLUTION SIMULATION OF ABIDJAN AQUIFER (CÔTE D'IVOIRE)

2013· article· en· W1518520608 on OpenAlexaff
Kouamé Kan Jean, Jourda Jean Patrice, Saley Mahaman Bachir, Deh Serges Kouakou, Anani Abenan Tawa, Yves Leblanc, Vincent Cloutier, Biémi Jean

Bibliographic record

VenueRePEc: Research Papers in Economics · 2013
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsMODFLOWAquiferPiezometerDrawdown (hydrology)HydrogeologyGroundwater flowHydrology (agriculture)GroundwaterEnvironmental sciencePopulationGeologyGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

Groundwater is the main source of drinking water supply to Abidjan District. However, the rapid growth of Abidjan population and the highest demand in water are a threat quantitative of this resource. This study has been undertaken by means to obtain the order of drawdown magnitude of Abidjan aquifer on the horizon from 2005 to 2030 according to an increasing in flow rates of pumping stations provided by SODECI to satisfy the highest demand in drinking water. To achieve such an objective, a hydrogeological model of Abidjan aquifer has been designed using the code MODFLOW by using historical data (geological, hydrogeological and piezometric) to predict the impact such an exploitation on this aquifer. The numerical model designed was calibrated in steady-state mode with piezometric data from 1978 and then validated in transient mode from piezometric data from 1992. The piezometry evolution simulation and the drawdown calculation from 2005 to 2030 were made in transient mode. The results of this model revealed that the project of rates increase from 2005 to 2030 is possible. These flows will pass from 256 490 m3/day to 310 760 m3/day. However, the highest drawdowns will be observed into piezometers; Anonkoua kouté 2 (15.71 m), Niangon 1 (6.86 m) and Adonkoua (6.24 m).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.240
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2013
Admission routes1
Has abstractyes

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