基于LM-BPNN的高寒地区地下水微生物−毒理指标预测模型

贾卓鹏; 毕俊擘; 原勇; 权昕; 王帅伟; 孙伟超

doi:10.19509/j.cnki.dzkq.tb20240345

基于LM-BPNN的高寒地区地下水微生物−毒理指标预测模型

doi: 10.19509/j.cnki.dzkq.tb20240345

贾卓鹏^{1, 2,},
毕俊擘^{3, 4, ,},
原勇¹,
权昕⁵,
王帅伟³,
孙伟超^{3, 4}

1.
阿里地区人民医院，西藏阿里地区 859400
2.
西安医学院第一附属医院，西安 710000
3.
中国地质科学院水文地质环境地质研究所，石家庄 050061
4.
中国地质大学（北京）中国地质科学院，北京 100083
5.
西安医学院，西安 710021

基金项目: 西藏阿里地区科学技术局资助项目（ALKJ-BJCZ-2024-04）

详细信息

作者简介:
贾卓鹏：E-mail：13088969796@163.com

通讯作者:
E-mail：bjunbo@mail.cgs.gov.cn

中图分类号: X54
计量
- 文章访问数: 26
- PDF下载量: 0
- 被引次数: 0
出版历程
- 收稿日期: 2024-06-19
- 录用日期: 2024-08-26
- 修回日期: 2024-08-22
- 网络出版日期: 2025-03-21

Prediction model of groundwater microbiological toxicological indicators in alpine regions based on LM-BPNN

JIA Zhuopeng^{1, 2
,},
BI Junbo^{3, 4
, ,},
YUAN Yong¹,
QUAN Xin⁵,
WANG Shuaiwei³,
SUN Weichao^{3, 4}

1.
Ngari Prefecture People's Hospital, Ngari Area Xizang 859400, China
2.
The First Affiliated Hospital of Xi'an Medical University, Xi'an 710000, China
3.
Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, China
4.
Chinese Academy of Geological Sciences, China University of Geosciences (Beijing), Beijing 100083, China
5.
Xi'an Medical College, Xi'an 710021, China

More Information

Author Bio:
E-mail：13088969796@163.com

Corresponding author: E-mail：bjunbo@mail.cgs.gov.cn

摘要

摘要:
为建立高寒地区地下水微生物−毒理指标（细菌菌落总数（total bacteria count，简称TBC）、总大肠杆菌菌落总数（total coliforms count，简称TCC））预测模型，以我国西南高原地区某一水源地地下水微生物−毒理指标（TBC、TCC）为研究对象，通过正交试验设计，改变温度、pH值、孔隙率等外部环境条件，开展室内批试验，获取了不同孔隙率、取样深度，流出溶液的温度、pH值、化学需氧量（chemical oxygen demand，简称COD）、氧化还原电位（oxidation-reduction potential，简称ORP）等地下水水质指标及地下水中TBC和TCC值，然后分别以TBC、TCC为目标，以取样深度、溶液温度、pH值、土壤孔隙率、ORP、COD为影响因素，基于Levenberg-Marquardt（LM）优化的神经网络（BPNN）算法，建立了高寒地区地下水微生物−毒理指标的预测模型。结果表明，TBC、TCC模型的预测结果与试验结果的变化趋势一致，且最大相对误差均小于15%（TBC、TCC模型的最大相对误差分别为11.52%，14.55%），符合工程要求。该方法可用于高寒地区地下水微生物−毒理指标的预测，可为高原地区地下水资源可持续利用和污染的有效防治提供科学依据，并为地下水中TBC、TCC的测定提供新的思路。
- 微生物−毒理指标 /
- TCC /
- TBC /
- 环境条件 /
- 地下水水质指标 /
- LM-BPNN /
- 预测模型 /
- 高寒地区
Abstract: Objective
To establish a prediction model for groundwater microbial toxicological indicators (total bacteria count [TBC] and total coliform count [TCC]) in alpine regions,
Methods
this work focuses on the microbial toxicity indicators (TBC and TCC) of groundwater from a specific water source in the western plateau region of China. By using an orthogonal experimental design combined with indoor soil column batch experiments, we varied environmental factors such as pH, temperature, and porosity to obtain the evolution results of TBC and TCC under different depths, pH values, oxidation-reduction potential (ORP) values, temperatures, porosities, and chemical oxygen demand (COD) conditions. On the MATLAB platform, a predictive model for microbial toxicity indicators in groundwater in frigid regions was subsequently established using the LM (Levenberg Marquardt)-optimized BPNN (neural network) algorithm.
Results
These results indicate that the predictive results of the established TBC and TCC models align well with the experimental results. The maximum relative errors are less than 15% (meeting engineering requirements), yielding 11.52% and 14.55% for TBC, and TCC, respectively. Moreover, the evolutionary trends of TBC and TCC match the experimental results.
Conclusion
This model can be used for predicting microbial toxicity indicators in groundwater in plateau regions, and the results of this study provide new insights for predicting microbial toxicity indicators in groundwater in high-elevation areas.
- microbiological toxicological indicator /
- TCC /
- TBC /
- environmental condition /
- groundwater quality indicator /
- LM-BPNN /
- prediction model /
- alpine region

HTML全文

图 1 研究区位置示意图

注：底图采用中华人民共和国自然资源部网站的公开地图（阿里地区地图政区简图版），审图号为：藏S（2023）004号，底图未修改

Figure 1. Location of study area

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图 2 土体相对密度测定

Figure 2. Determination of specific gravity of the soil sample

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图 3 室内土柱试验

a. 试验装置示意图；b. 实际试验装置

Figure 3. Indoor soil column test

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图 4 TBC (a)、TCC (b)、溶液pH值 (c)与COD (d)测定试验

Figure 4. TBC (a), TCC (b), solution pH (c) and COD (d) determination

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图 5 BP神经网络结构

Figure 5. Schematic diagram of BP neural network structure

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图 6 BP神经网络结构层次图

Figure 6. Structure hierarchy diagram of BP neural network

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图 7 TBC (a)和TCC (b)预测模型的训练结果图

Figure 7. Training results of TBC (a) and TCC (b) predictive model

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图 8 TBC (a)和TCC (b)模型预测结果与试验结果对比图

Figure 8. Comparison between the predicted results of TBC (a) and TCC (b) model and the experimental results

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图 9 TBC (a)与TCC (b)模型预测结果与试验结果变化趋势图

Figure 9. Comparison between the predicted results of TBC (a) and TCC (b) model and the experimental results

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表 1 正交试验参数

Table 1. Orthogonal test table

试验编号	温度/℃	孔隙率	pH值
1	5	0.50	6
2	5	0.55	7
3	5	0.60	8
4	15	0.50	7
5	15	0.55	8
6	15	0.60	6
7	25	0.50	8
8	25	0.55	6
9	25	0.60	7

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表 2 室内试验结果

Table 2. Indoor test results

序号	深度/m	温度/℃	孔隙率	pH值	ORP/mV	ρ(COD)/(mg·L⁻¹)	TBC/(CFU·mL⁻¹)	TCC/(MPN·100⁻¹ mL⁻¹)
1	0.1	5	0.6	6.14	36	4.81	74900	33
2	0.2	5	0.6	6.06	41	6.16	107300	22
3	0.3	5	0.6	5.94	48	8.50	111000	19
4	0.4	5	0.6	5.96	47	3.08	43700	27
…	…	…	…	…	…	…	…	…
103	0.3	25	0.55	6.41	20	8.18	482000	376
104	0.4	25	0.55	6.07	40	8.77	320000	368
105	0.5	25	0.55	7.08	−19	8.65	250000	420

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表 3 TBC、TCC模型预测结果与试验结果的误差

Table 3. Error between the predicted results of TBC and TCC model and the experimental results

序号	TBC预测模型		TCC预测模型
序号	绝对误差	相对误差/%	绝对误差	相对误差/%
1	2360	5.18	4	9.09
2	600	0.94	4	7.69
3	5100	6.71	−2	−5.13
4	−1970	−6.75	3	4.62
5	1500	4.17	−5	−10.42
6	2670	8.14	−21	−9.29
7	2950	7.20	−10	−3.60
8	−200	−0.50	3	1.36
9	70	0.25	28	7.55
10	2560	10.64	−14	−3.52
11	30000	6.22	30	7.98
12	−11000	−3.44	−34	−9.24
13	15400	6.16	34	8.10
14	15800	2.36	−37	−8.41
15	17000	3.59	−14	−5.00
16	50000	8.33	−14	−5.76
17	3000	0.55	23	5.12
18	9800	1.91	−40	−11.49
19	25400	7.06	17	5.31
20	−14200	−5.92	10	2.78
21	−5000	−1.98	−6	−2.50
22	8700	3.30	38	7.04
23	17400	6.40	23	10.45
24	−22200	−7.12	32	14.55
25	53000	11.52	26	8.13

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