基于ELM模型的淺層地下水位埋深時空分布預測
發(fā)布時間：2019-03-03 10:41
【摘要】：選用石家莊平原區(qū)補排因子的多種組合為輸入參數,利用28眼水井的實測資料作為預測目標值,首次建立基于極限學習機(Extreme learning machine,ELM)的地下水位埋深時空分布預測模型,討論補排因子在不同缺失情況下對模型精度的影響;利用Arc GIS分析誤差空間分布趨勢,并與常用的三隱層BP神經網絡模型進行對比。結果表明:基于水均衡理論的ELM地下水位埋深模擬模型能夠準確反映人類和自然雙重影響下地下水系統(tǒng)的非線性關系,模型輸入因子中缺失降水量或開采量的模擬結果均方根誤差(RMSE)比缺失其余因子的RMSE高2.00倍及以上,同時模型有效系數(E_(ns))和決定系數(R~2)進一步降低;與BP模型相比,ELM模型可使RMSE減小43.6%,誤差區(qū)間降低46.4%,Ens和R2提高至0.99,且RMSE在空間相同區(qū)域上均明顯呈現(xiàn)出ELM模型小于BP模型;ELM模型在南部高誤差區(qū)的移植精度(RMSE低于1.82 m/a,E_(ns)高于0.95)高于BP模型(RMSE超過3.00 m/a,Ens低于0.85);因此,影響地下水位埋深的主導因素是降水量和開采量,且ELM模型在精度、穩(wěn)定性和空間均勻性上較優(yōu),移植預測效果較好,可利用已知資料推求區(qū)域空間內其余未知水井的淺層地下水位埋深;該模型可作為水文地質參數及補排資料缺乏條件下淺層地下水位埋深預測的推薦模型。
[Abstract]:The spatial-temporal distribution prediction model of groundwater table depth based on limit learning machine (Extreme learning machine,ELM) is established for the first time by using the measured data of 28 wells as the prediction target value and using various combinations of complementary drainage factors in Shijiazhuang Plain as input parameters. The influence of complementary removal factor on the accuracy of the model is discussed in different cases. The spatial distribution trend of error is analyzed by Arc GIS and compared with the three hidden layer BP neural network model. The results show that the ELM groundwater table depth simulation model based on the water equilibrium theory can accurately reflect the nonlinear relationship between human and natural groundwater systems. The root mean square error (RMSE) of the model input factor is 2.00 times higher than that of the other factors, and the effective coefficient (E _ (ns) and decision coefficient) of the model is further reduced. The root mean square error (RMS) of the model input factor is 2.00 times higher than that of the other factors. Compared with BP model, ELM model reduced RMSE by 43.6%, error interval decreased by 46.4%, Ens and R2 increased to 0.999, and RMSE showed that ELM model was smaller than BP model in the same area of space. The transplant accuracy of the ELM model in the southern high error area (RMSE < 1.82m / a, E _ (ns) > 0.95) was higher than that of the BP model (RMSE > 3.00m / a, Ens < 0.85). Therefore, the main factors affecting the depth of groundwater table are precipitation and mining amount, and the ELM model is better in precision, stability and spatial uniformity, and the effect of transplant prediction is better. The shallow groundwater table depth of the remaining unknown wells in the regional space can be calculated by using the known data. This model can be used as the recommended model for predicting the depth of shallow groundwater table under the condition of lack of hydrogeological parameters and supplementary drainage data.
【作者單位】： 昆明理工大學現(xiàn)代農業(yè)工程學院;長沙理工大學水利工程學院;中國農業(yè)科學院農業(yè)環(huán)境與可持續(xù)發(fā)展研究所;作物高效用水與抗災減損國家工程實驗室;寧鄉(xiāng)縣水利水電勘測設計院;
【分類號】：P641
本文編號：2433622
[Abstract]:The spatial-temporal distribution prediction model of groundwater table depth based on limit learning machine (Extreme learning machine,ELM) is established for the first time by using the measured data of 28 wells as the prediction target value and using various combinations of complementary drainage factors in Shijiazhuang Plain as input parameters. The influence of complementary removal factor on the accuracy of the model is discussed in different cases. The spatial distribution trend of error is analyzed by Arc GIS and compared with the three hidden layer BP neural network model. The results show that the ELM groundwater table depth simulation model based on the water equilibrium theory can accurately reflect the nonlinear relationship between human and natural groundwater systems. The root mean square error (RMSE) of the model input factor is 2.00 times higher than that of the other factors, and the effective coefficient (E _ (ns) and decision coefficient) of the model is further reduced. The root mean square error (RMS) of the model input factor is 2.00 times higher than that of the other factors. Compared with BP model, ELM model reduced RMSE by 43.6%, error interval decreased by 46.4%, Ens and R2 increased to 0.999, and RMSE showed that ELM model was smaller than BP model in the same area of space. The transplant accuracy of the ELM model in the southern high error area (RMSE < 1.82m / a, E _ (ns) > 0.95) was higher than that of the BP model (RMSE > 3.00m / a, Ens < 0.85). Therefore, the main factors affecting the depth of groundwater table are precipitation and mining amount, and the ELM model is better in precision, stability and spatial uniformity, and the effect of transplant prediction is better. The shallow groundwater table depth of the remaining unknown wells in the regional space can be calculated by using the known data. This model can be used as the recommended model for predicting the depth of shallow groundwater table under the condition of lack of hydrogeological parameters and supplementary drainage data.
【作者單位】： 昆明理工大學現(xiàn)代農業(yè)工程學院;長沙理工大學水利工程學院;中國農業(yè)科學院農業(yè)環(huán)境與可持續(xù)發(fā)展研究所;作物高效用水與抗災減損國家工程實驗室;寧鄉(xiāng)縣水利水電勘測設計院;
【分類號】：P641
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