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 f...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 south-eastern 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 resistiv-ity(ρ)is less than 5Ω·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 parame-ter T"=ρ*H/ρ'was established,whereρis the aquifer resistivity,ρ'is the aquitard resistivity,and H is the aquifer thickness.The resistivity ratio effectively eliminates the coupling effect between electrical parame-ters 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 gener-alization ability,providing a new parameterization method for the quantitative evaluation of Quaternary pore water in plain areas.展开更多
基金financially supported by the National Natural Science Foundation of China(No.42277460)the Fundamental Research Project of Institute of Hydrogeology and Environmental Geology,Chinese Academy of Geological Sciences(No.SK202210).
文摘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 south-eastern 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 resistiv-ity(ρ)is less than 5Ω·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 parame-ter T"=ρ*H/ρ'was established,whereρis the aquifer resistivity,ρ'is the aquitard resistivity,and H is the aquifer thickness.The resistivity ratio effectively eliminates the coupling effect between electrical parame-ters 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 gener-alization ability,providing a new parameterization method for the quantitative evaluation of Quaternary pore water in plain areas.
