| Citation: | JIA Zhuopeng，BI Junbo，YUAN Yong，et al. Prediction model of groundwater microbiological toxicological indicators in alpine regions based on LM-BPNN[J]. Bulletin of Geological Science and Technology，2025，44（2）：1-10 doi: 10.19509/j.cnki.dzkq.tb20240345
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To establish a prediction model for groundwater microbial toxicological indicators (total bacteria count [TBC] and total coliform count [TCC]) in alpine regions,
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.
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.
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.
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