| SYS | | 0326841 |
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| LBL | | -----naa--22--------450- |
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| 005 | | 20240412105728.9 |
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| 014 | | $a 001132022600001 $2 CCC |
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| 014 | | $a 001132022600001 $2 WOS CC. SCIE |
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| 014 | | $a 2-s2.0-85181736187 $2 SCOPUS |
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| 017 | 70 | $a 10.1016/j.jafrearsci.2023.105127 $2 DOI |
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| 035 | | $a biblio/1154801 $2 CREPC2 |
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| 100 | | $a 20240313d2024 m y slo 03 ba |
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| 101 | 0- | $a eng |
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| 102 | | $a GB |
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| 200 | 1- | $a Predicting Earth-Vas2 parameters of coastal plain sand aquifers using artificial neural Network(s), in the Calabar region of Nigeria $f Emmanuel I. Akaerue ... [et al.] |
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| 330 | | $a This study employs artificial neural networks (ANNs), specifically the Nonlinear Autoregressive with External Input (NARX) model as an ANN add-on for Matlab, to forecast Earth-Vas2 parameters. The model leverages a time series approach, with the Earth-Vas2 index (y(t)) as the predictive target and various parameters (eleva tions, static water level, aquifer material thickness, transmissivity, hydraulic conductivity, specific capacity, vadose media) denoted as x(t) to facilitate accurate predictions. The Levenberg-Marquardt training algorithm was applied to the ANN using geo-hydraulic data from 56 boreholes for training and 24 boreholes for validation and testing. Validation metrics such as mean square error (MSE), correlation coefficient (R), and coefficient of determination (R2 ) were utilized. The results indicate a high degree of accuracy, with low MSE values of 0.0171 (training), 0.0087 (validation), and 0.0327 (testing). Strong correlation coefficients (R) of 0.9882, 0.9898, and 0.9844, along with high R2 values (97.66%), emphasize the robustness of the ANN model. Actual and predicted Earth-Vas2 values exhibit a similar pattern, affirming the model’s effectiveness. The R-value of 0.9882 signifies a strong positive correlation within the model, validating its ability to capture variable interactions in fluid migration control. Geo-hydraulic Earth-Vas2 maps identify areas with varying migration efficiency, guiding groundwater management. The study emphasizes routine subsurface assessments for crucial data in environ mental planning and risk reduction in industrial and municipal activities. This method provides cost-effective estimation of key geo-hydraulic properties for aquifer vulnerability assessments, with far-reaching implica tions for groundwater management, environmental protection, and land use planning. |
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| 463 | -1 | $1 001 umb_un_cat*0326973 $1 011 $a 1464-343X $1 011 $a 1879-1956 $1 200 1 $a Journal of African Earth Sciences $v No. 209 (2024), pp. 1-12 $1 210 $a Oxford $c Elsevier Ltd. $d 2024 |
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| 606 | 0- | $3 umb_un_auth*0139533 $a hydrogeológia $X hydrogeology |
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| 606 | 0- | $3 umb_un_auth*0170072 $a korelačné koeficienty |
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| 607 | -1 | $3 umb_un_auth*0106362 $a Nigéria |
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| 700 | -1 | $3 umb_un_auth*0303742 $a Akaerue $b Emmanuel I. $4 070 $9 35 |
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| 701 | -1 | $3 umb_un_auth*0303743 $a Onwuka $b Obialo S. $4 070 |
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| 701 | -1 | $3 umb_un_auth*0303744 $a George $b Anthony M. $4 070 |
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| 701 | -1 | $3 umb_un_auth*0292177 $a Ekwok $b Stephen Eguba $4 070 |
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| 701 | -1 | $3 umb_un_auth*0302519 $a Alarifi $b Saad S. $4 070 |
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| 701 | -1 | $3 umb_un_auth*0120546 $a Andráš $b Peter $f 1953- $p UMBFP01 $4 070 $9 30 $T Katedra geografie a geológie |
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| 701 | -1 | $3 umb_un_auth*0290966 $a Eldosouky $b Ahmed M. $4 070 |
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| 801 | | $a SK $b BB301 $g AACR2 $9 unimarc sk |
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| 856 | | $u https://www.sciencedirect.com/science/article/pii/S1464343X2300300X $a Link na plný text |
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| T85 | | $x existuji fulltexy |
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