**3.2 Regression and prediction results**

*Deep Learning Applications*

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**Figure 3.**

*(c) year 8 to year 12 and (d) year 13 to year 16.*

*Normalized monitoring data for the 16 years (year 1 to year 16). (a) Year 1 to year 4; (b) year 5 to year 8;* 

After the training is completed, all training and validation observation samples go through Eq. (1) again and then the calculated output is called drainage regression. The *MSE* and *R* between drainage regression and real measured ones (targets) are listed in **Table 2** based on a grid search is performed on both of daily average input layer and weekly average input layer with 5, 10 and 20 neurons in the hidden layer. It is observed that results from the neuron network with weekly average input layer are generally better than those obtained from the neuron network with daily average input layer, indicating that both of flow rate and acidity from this waste rock pile are mainly controlled by long-term weather trend rather than short-term one. For flow rate regression, the lowest *MSE* is obtained from both of 10 and 20 neurons in hidden layer, so the neuron network trained by weekly average input layer with 10 neurons in hidden layer is selected as the successful candidate to predict future drainage flow rates as it has smaller size and less undermined coefficients than the network with 20 neurons in the hidden layer. For acidity regression, the lowest *MSE* is obtained from the neuron network with weekly average input layer and 20 neurons in the hidden layer, which is selected to predict future drainage acidities.


**Table 2.** *Results of the grid search.*

#### *Deep Learning Applications*

The comparison between regressions of flow rate and acidity and real measurements (target) in temporal scale are provided in **Figure 4**. It is found that the proposed neural network is capable to capture the underlying correlation between the drainage and weather, as not only seasonal fluctuations in a year but also long term evolution across years are well reflected in the drainage regression.

In addition, the successful candidate neuron networks (weekly average input layer with 10 neurons in hidden layer for flow rate, and weekly average input layer with 20 neurons in hidden layer for acidity) are adopted to predict the future flow rate and acidity in the Year 17 and Year 18 based on the input extracted from the real weather monitoring data. The prediction and real measurement in temporal scale are compared in **Figure 5**.

It is observed that the general trend for both of flow rate and acidity are well predicted by the neural network. The proposed neural network is capable to predict the time and also the amount of peak flow rates in the spring freshet of both years, which is important for site water management. In terms of acidity, the long term downward trend shown in Year 11 to Year 16 is reflected in the prediction, which matches the trend of real measurement in both years. However, seasonal fluctuation has some mismatch. The reason is that the amount of observation samples for acidity is much less than those for flow rate, so the acidity prediction is not as good as the flow rate prediction in this case study. The accuracy can be improved when more monitoring data are accumulated for the training in the future.

**Figure 4.** *Drainage regression vs. real measurement for the 16 years (year 1 to year 16). (a) Flow rate and (b) acidity.*

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rock storages.

**4. Conclusions**

**Figure 5.**

*(b) acidity.*

the machine learning process.

*The Application of Artificial Neural Network to Predicting the Drainage from Waste Rock…*

A machine learning algorithm based on feedforward neural network is introduced in this chapter to correlate the drainage flow rate and drainage chemistry with the field precipitation and temperature for waste rock storages. Comparing with traditional predictive models, the neural network approach requires little simplification and assumptions of bio-geo-chemical processes involved, in additional, the cost and time for characterizations can be significantly reduced. The advantage of the neural network is that all underlying mechanisms have been naturally reflected in the monitoring data, which can be gradually captured during

*Drainage prediction vs. real measurement for the next 2 years (year 17 and year 18). (a) Flow rate and* 

A case study on a full-scale waste rock storage is performed. The results show that the flow rate and acidity of the drainage discharged in the field have strong correlations with previous 10 weekly averaged weather data at this site. The capability of making prediction of future drainage in the field is also validated. However, the structure of input layer, hidden layer number, neurons in the hidden layer are all site specific, which may be adjusted for the applications to other waste

It should also be addressed that the measurements of drainage flow rate and drainage chemistry may not always be accurate in the field, furthermore, they can fluctuate in a single day depending on the hydrogeological conditions. So the monitoring data may not represent the daily average in some cases, which means that some mismatch between the prediction and measurement does not necessarily

*DOI: http://dx.doi.org/10.5772/intechopen.96162*

*The Application of Artificial Neural Network to Predicting the Drainage from Waste Rock… DOI: http://dx.doi.org/10.5772/intechopen.96162*

**Figure 5.** *Drainage prediction vs. real measurement for the next 2 years (year 17 and year 18). (a) Flow rate and (b) acidity.*
