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ANN-based prediction model for single-hole water inflow from piedmont to inland plain areas of Hebei Province, North China Plain
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Abstract: This study, based on Artificial Neural Network (ANN) technology, develops a quantitative prediction model for the unit water flow rate of the Quaternary strata in the Shijiazhuang-Hebei Plain area. The study area extends from the piedmont region of Shijiazhuang, at the eastern foothills of the Taihang Mountains, to the hinterland area of Hengshui within the plain in Hebei Province section of the North China Plain. The hydrological and exploration boreholes selected for modeling are primarily located in the southeastern part of Shijiazhuang urban area — the southern region of Xinji County — north of Hengshui City near the Shenzhou County area. By employing the Induced Polarization method (IP) and Vertical Electrical Sounding (VES), apparent resistivity (ρS), apparent polarization rate (ηS), half-decay time (Th), and decay degree (D) were obtained as initial input parameters. These were combined with the measured water flow rates from borehole pumping tests to build the training sample set. To address the prevalent issue of high-salinity interference in the study area, multiple regression analysis revealed that when the inverted resistivity (ρ) is less than 5 Ω·m and the inverted polarization rate (η) is greater than 8%, the contribution of groundwater salinity to the resistivity parameter reaches 42%±6%. Based on this, a comprehensive parameter T"=ρ*H/ρ' was established, where ρ is the aquifer resistivity, ρ' is the aquitard resistivity, and H is the aquifer thickness. The resistivity ratio effectively eliminates the coupling effect between electrical parameters and salinity. The input neurons of the improved model were adjusted to a four-parameter system consisting of decay time (Th), decay degree (D), deviation degree (σ), and the comprehensive parameter (T"). Experiments showed that the prediction error of the model on the validation set was reduced from the original model's 5%-10% to 0.9%-5%. The introduction of the T" parameter reduced the prediction error in high salinity areas (Cl->500 mg/L) to within 7%. The study confirms that the composite parameter T" based on geophysical inversion parameters can effectively characterize the coupling features of aquifer thickness and water quality. Even with a small sample size, through algorithm optimization, data augmentation, and model structural improvements, it is entirely possible to effectively enhance prediction accuracy and generalization ability, providing a new parameterization method for the quantitative evaluation of Quaternary pore water in plain areas.
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Key words:
- Artificial Neural Network /
- Single-Hole /
- Aquifer Thickness /
- Resistivity /
- Induced Polarization
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Table 1. Normalized data of input neuron
Hole number ρ/Ω·m Th/s D/% η/% Q/m3/h K1 1.3 1.2 0.730 1.1 57 K2 0.879 0.689 0.760 0.635 67 K3 0.687 0.569 0.820 0.605 36 K4 0.333 0.433 0.670 0.675 38 K5 0.881 0.373 0.627 0.883 58 K6 0.665 0.661 0.650 0.582 45 K7 0.480 0.320 0.355 0.240 79 K8 0.468 0.450 0.589 0.579 60 K9 0.312 0.355 0.755 0.079 63 J1 0.303 0.23 0.534 0.020 34 J2 0.223 0.231 0.509 0.587 41 J3 0.456 0.352 0.405 0.668 53 J4 0.423 0.155 0.680 0.502 51 J5 0.934 0.786 0.620 0.872 83 J6 0.218 0.556 0.601 0.618 55 J7 0.33 0.283 0.630 0.212 42 
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Table 2. Comparison of training error (all input data has been normalized)
Hole number ρ/Ω·m Th/s D/% η/% Actual Q/m3/h Predicted Q/m3/h K1 1.3 1.2 0.730 1.1 57 60.8165 K2 0.879 0.689 0.760 0.635 67 62.5421 K3 0.687 0.569 0.820 0.605 36 39.3233 J5 0.934 0.786 0.620 0.872 83 78.3232 J6 0.218 0.556 0.601 0.618 55 51.6692 J7 0.33 0.283 0.630 0.212 42 44.2
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Table 3. Correlation analysis of electrical parameters specific capacity
Analytic quantity Neurons σ $ \eta $ Correlation (r) 0.969 0.886 Coefficient of significance (p) 0.982 0.819
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Table 4. Correlation and significance analysis
Analytic quantity Neurons $ \rho $ H $T''$ Correlation (r) 0.893 0.866 0.992 Coefficient of significance (p) 0.998 0.977 0.989
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Table 5. Forecast results of new model.
Borehole
namesActual Q/
m3/hPredicted Q/
m3/hError/
%K1 57 55.232 3.1018 K2 67 66.363 0.9507 K3 36 34.582 3.9389 J5 83 84.367 1.647 J6 55 52.563 4.4309 J7 42 41.368 1.5048
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Table 6. Comparison of the prediction performance between the original and the improved models
Hole number Prediction error of
original model /%Prediction error of
new model /%K1 6.70 3.10 K2 6.56 0.95 K3 9.23 3.94 J5 5.63 1.65 J6 6.06 4.43 J7 5.24 1.50
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