**3.1 Input, target and neural network parameters**

To validate the proposed neural network approach, a full-scale case study is performed to predict real drainage flow rate and drainage chemistry in field condition. A real waste rock pile from an anonymous mine site in western Canada is adopted in this study. The proposed neural network is trained by historical monitoring data for 16 years (Year 1 to Year 16). After the training is completed, it will be used to predict the drainage in the next 2 years (Year 17 and Year 18). A comparison between real measurement and predicted value will be provided.

At this mine site, the drainage flow rates are not directly measured but they are calculated based on readings of the water level in v-notch weirs installed at the end of the drainage collecting ditch. Thus the v-notch is used as the one actual target of neural network training. In the following discussion, the drainage flow rate actually refers to the original measurement of v-notch from the weirs. Among all drainage chemistry data from this waste rock pile, acidity is selected for this case study as another target, because it is directly linked to the lime consumption for contaminant treatment. These drainage measurement are generally performed in a dynamic time frame at this mine site. During spring freshet and large precipitation periods, the measurements are usually more frequent than the remaining time in a year, as increased drainage flow rate is observed.

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**Table 1.**

**Type of input layer**

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

temperature, maximum temperature, mean temperature, total rain, total snow, total precipitation and snow thickness on ground, etc. For this case study, the total 16 years weather monitoring data have been obtained for the training purpose. As mentioned in the previous section, two independent weather parameters measured on a daily basis - the total precipitation and mean temperature are selected as the

To evaluate how long the weather can impact on the drainage from this waste rock pile, two types of input layers are proposed for comparison in the study. When a target (flow rate or acidity) is selected to train the neural network, its measurement date is extracted. Daily average input layer consists of 21 neurons including the time tag of measurement day, daily total precipitation and daily mean temperature from previous 9 days and the measurement day, which mainly investigates short-term weather impact on the drainage. Furthermore, weekly average input layer has 21 neurons including the time tag of measurement day, weekly average total precipitation and weekly averaged mean temperature from previous 9 weeks and current week to investigate long-term weather impact. The weekly average input layer reflects longer weather monitoring data than the daily average input layer does, however, high frequent information is filtered in the weekly average input layer. The summary of daily average and weekly average input layers can be found in **Table 1**. Here 0 day means the measurement day, 0 week means measurement day and previous 6 days. Finally, both types of input layers are adopted to train the neural network to determine which input layer is more competent to

As the mine site is anonymous, the original monitoring data is confidential and not publicly accessed. To protect the site information, only normalized historical monitoring data including total precipitation, mean temperature, flow rate and acidity from the 16 years are provided in **Figure 3**. Total numbers of flow rate measurement and acidity measurement during the 16 years is 1741 and 320, respectively. It should be noticed that the weather data are extracted on a daily basis and any missing data is represented by a gap. The flow rate and acidity is not measured in a fixed time frame and the time interval is dynamic, so each measurement data is represented by a solid dot in the figure. As some weather data are missing, not all of drainage measurements are utilized for the training. Those drainage measurements that do not have a complete daily average or weekly average input will be excluded. In terms of the hold-out approach to avoid overfitting, 80% of the total observation samples are used for training and 20% for validation. No testing data is allocated

**Daily Average Weekly Average**

Mean temperature of −9 week Total precipitation of −9 week Mean temperature of −8 week Total precipitation of −8 week

Mean temperature of −1 week Total precipitation of −1 week Mean temperature of 0 week Total precipitation of 0 week Time tag of measurement day

… … … …

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

inputs for the proposed neural network.

capture the underlying pattern and make a better prediction.

Neuron number 21 21

Total precipitation of −9 day Mean temperature of −8 day Total precipitation of −8 day

Mean temperature of −1 day Total precipitation of −1 day Mean temperature of 0 day Total precipitation of 0 day Time tag of measurement day

Description Mean temperature of −9 day

… … … …

*Description of daily average and weekly average input layers.*

The weather monitoring data at this site is extracted from the website of Environment Canada (weather.gc.ca) on a daily basis, including minimum
