尾矿库风险预测的Wavelet-CNN-LSTM模型
A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction
摘要：
尾矿池是储存工业废料的地方。一旦尾矿库倒塌，附近的村庄将被摧毁，有害化学物质将造成严重的环境污染。因此，迫切需要一种可靠的预测模型来研究尾矿坝稳定系数的变化趋势，并进行预警。为了填补这一空白，本文提出了一种基于小波的长短期记忆（LSTM）和卷积神经网络（CNN）的混合网络，即Wavelet-CNN-LSTM神经网络，用于尾矿库风险预测。首先，我们构造了一种特殊的非线性数据处理方法来填补缺失值的数值反演（NI）方法，它结合了相关分析，灵敏度分析和随机森林（RF）算法。其次，提出了一种新的预测模型来监测浸润线，浸润线是尾矿库的生命线，可以直接反映尾矿库的稳定性。利用离散小波变换（DWT）将原始浸润线数据分解为4层小波并对数据进行去噪后，使用CNN识别和学习时间序列中的空间结构，然后使用LSTM细胞检测长短期相关性。最后，通过与其他最先进的算法进行比较，我们的模型进行了不同的实验，以评估其有效性。结果表明，Wavelet-CNN-LSTM在平均绝对百分比误差（MAPE），均方根误差（RMSE）和R2方面都取得了最佳成绩。
英文摘要：
Tailings ponds are places for storing industrial waste. Once the tailings pond collapses, the villages nearby will be destroyed and the harmful chemicals will cause serious environmental pollution. There is an urgent need for a reliable forecast model, which could investigate the variation trend of stability coefficient of tailing dam and issue early warnings. In order to fill the gap, this work presents an hybrid network - Wavelet-based Long-Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), namely Wavelet-CNN-LSTM netwrok for predicting the tailings pond risk. Firstly, we construct the especial nonlinear data processing method to impute the missing value with the numerical inversion (NI) method, which combines correlation analysis, sensitivity analysis, and Random Forest (RF) algorithms. Secondly, a new forecasting model was proposed to monitor the saturation line, which is the lifeline of the tailings pond and can directly reflect the stability of the tailings pond. After using the discrete wavelet transform (DWT) to decompose the original saturation line data into 4-layer wavelets and de-noise the data, the CNN was used to identify and learn the spatial structures in the time series, followed by LSTM cells for detecting the long-short-term dependence. Finally, different experiments were conducted to evaluate the effectiveness of our model by comparing it with other state-of-the-art algorithms. The results show that Wavelet-CNN-LSTM achieves the best score both in mean absolute percentage error (MAPE), root-mean-square error (RMSE) and R 2 .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
