**2. Methodology**

*Deep Learning Applications*

consumption for a full-scale waste rock pile.

costly and time-consuming.

For day-to-day mine site management, flood control and contaminant remediation plan are dependent on the evaluation and prediction of drainage flow rates and drainage chemistries. Various methodologies have been developed over the past several decades to predict the drainage from waste rock storages. For predicting drainage flow rates, a numerical model to simulate groundwater flow through unsaturated bed or layers of earth is developed in [3]. In reference [4], a water balance approach is proposed to calculate the conservation of total water flow through waste rock piles by dividing the whole hydrological process into independent components. In terms of predicting drainage chemistries, a numerous numerical models enabled with mass transport effect are developed to evaluate the geochemical reaction and transport inside waste rock piles [5–7]. Furthermore, using dimensional equation to correlate drainage chemistries with seepage flow rates from waste rock piles is explored in [8]. In reference [9], the effectiveness of a cover system is assessed and a Multiphysics model is developed to predict the iron loading and lime

However, there are several limitations with above predictive models. For example: 1. Many predictive models are based on lab testing and then scaled up to predict the result in the field, but there is little comprehensive understanding on how to scale up; 2. Simplification and assumptions of geo-bio-chemical processes for the geochemical reaction and leaching process in waste rock storages are critical to the accuracy of the predictive models; 3 Lab or field characterization of material and transport properties related to predictive models is essential, which is also very

To understand and minimize the environmental impact from the contaminated drainage, routine monitoring of waste rock storages is required by many governmental regulators. With the rapid development of computer and sensing technologies, constant and comprehensive monitoring on the waste rock storages is now possible for many mine sites. Daily or even hourly monitoring data become available for many key parameters such as precipitation, temperature, wind, internal temperature, gas concentrations, air/water flow rates, drainage chemistries, etc. These monitoring data are accumulated to weekly, monthly and yearly datasets, and become so huge and complex that traditional data analysis approaches are inadequate to handle and investigate them. As one of the most famous machine learning technologies, artificial neural network not only has the advantages of high processing speed and high computational accuracy, but also enables a machine to mimic human learning behavior and problem solving functions. Thus using neural network to investigate the huge monitoring datasets and further predict drainage flow rates and drainage chemistries from waste rock storages shows very promising potentials. For example, the concentrations of sulphate, chlorine, total dissolved solids and total suspended solids in mine water are predicted by artificial neural network based on the input of pH, temperature and hardness in [10]. Heavy metal included in acid rock drainage is investigated by support vector machine and neural network for a copper mine in Iran [11]. Five machine learning approaches to predict copper concentration are compared in [12]. A feedforward neural network with weather input is proposed to

In this book chapter, a refined feedforward neural network based on [13] will be introduced to learn from historical monitoring data and then predict the drainage flow rate, in addition, the refined neural network will also be extended to predict the drainage chemistries in the field. Compared with above traditional predictive models, the proposed neural network approach requires much less simplification and assumption of geo-bio-chemical processes involved and it can significantly reduce characterization cost for mining companies, as the monitoring data inherently contain the information of all the underlying physical mechanisms within real

predict drainage flow rates for a full scale waste rock pile [13].

**102**

#### **2.1 Feedforward neural network**

The Feedforward neural network is an artificial neural network wherein connections between the artificial neurons do not form a cycle, which is different from its variant: recurrent neural networks. The artificial neurons are capable of simulating basic learning behaviors through receiving inputs, calculating a weighted sum and then passing the sum through a transformation known as activation function to produce outputs. The mathematical calculation for an artificial neuron in a feedforward neural network is generally illustrated as follows:

$$\mathbf{X}\_{t,p} = \mathfrak{g}\_{t,p} \left( \sum\_{i=1}^{q} \mathbf{X}\_{t-1,i} w\_{t,p,i} + \mathbf{b}\_{t,p} \right) \tag{1}$$

where *t* denotes the layer number and *p* denotes the order number in that layer. The combination of *t* and *p* can be used to determine the location of the neuron in the network. For the *p*th neuron located at the *t*th layer, there are q inputs from *Xt*<sup>−</sup>**1 1**, through *Xt q* <sup>−</sup>**1**, , and also weights from *wt p*, ,**<sup>1</sup>** to . *wt,p,q* In addition, *bt p*, is the bias input, ϕ*t p*, is the activation function and *Xt p*, is the output of that neuron. After calculating the output of *Xt p*, based on Eq. (1), it may further propagate to the input of the next layer or leave the network as the output of the whole feedforward neural network.
