School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266033, China.
School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China.
Int J Environ Res Public Health. 2022 Nov 24;19(23):15612. doi: 10.3390/ijerph192315612.
Prediction of groundwater quality is an essential step for sustainable utilization of water resources. Most of the related research in the study area focuses on water distribution and rational utilization of resources but lacks results on groundwater quality prediction. Therefore, this paper introduces a prediction model of groundwater quality based on a long short-term memory (LSTM) neural network. Based on groundwater monitoring data from October 2000 to October 2014, five indicators were screened as research objects: TDS, fluoride, nitrate, phosphate, and metasilicate. Considering the seasonality of water quality time series data, the LSTM neural network model was used to predict the groundwater index concentrations in the dry and rainy periods. The results suggest the model has high accuracy and can be used to predict groundwater quality. The mean absolute errors (MAEs) of these parameters are, respectively, 0.21, 0.20, 0.17, 0.17, and 0.20. The root mean square errors (RMSEs) are 0.31, 0.29, 0.28, 0.27, and 0.31, respectively. People can be given early warnings and take measures according to the forecast situation. It provides a reference for groundwater management and sustainable utilization in the study area in the future and also provides a new idea for coastal cities with similar hydrogeological conditions.
地下水质量预测是水资源可持续利用的重要步骤。研究区域内大多数相关研究都集中在水资源分布和合理利用上，但缺乏地下水质量预测方面的结果。因此，本文提出了一种基于长短期记忆（LSTM）神经网络的地下水质量预测模型。基于 2000 年 10 月至 2014 年 10 月的地下水监测数据，筛选出 TDS、氟化物、硝酸盐、磷酸盐和偏硅酸钠等五个指标作为研究对象。考虑到水质时间序列数据的季节性，采用 LSTM 神经网络模型对旱季和雨季的地下水指标浓度进行预测。结果表明，该模型具有较高的精度，可以用于预测地下水质量。这些参数的平均绝对误差（MAE）分别为 0.21、0.20、0.17、0.17 和 0.20。均方根误差（RMSE）分别为 0.31、0.29、0.28、0.27 和 0.31。人们可以根据预测情况提前发出警报并采取措施。这为研究区域未来的地下水管理和可持续利用提供了参考，也为具有类似水文地质条件的沿海城市提供了新思路。
