**1. Introduction**

Hard rock mining generates a huge amount of mine wastes (mine tailings and waste rocks), which often contains metal sulfide minerals. Once exposed to air and water during and after mining, the oxidation of metal sulfides minerals releases acid and heavy metals to the environment. The oxidation of metal sulfides can be accelerated in the presence of microorganisms. The drainage from mine wastes may have high level of toxic elements and chemicals such as arsenic, selenium, lead, uranium, zinc etc. Over time, waste rocks are deposited in the storages which can contain over one hundred million tons and cover a few hundred hectares. The drainage water from waste rock storages and its impact on the surrounding environment are becoming critical challenges to both of mining companies and the public. The treatment of the drainage from waste rock storages may have to last decades, even centuries, and bring a significant cost to the mining sectors [1, 2].

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 consumption for a full-scale waste rock pile.

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 costly and time-consuming.

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

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

**103**

bias input,

ϕ

feedforward neural network.

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

waste rock storages. However, the prediction accuracy is highly dependent on the quality of monitoring data as the proposed neural network is actually a mathemati-

The proposed feedforward neural network selects the weather monitoring data from mine sites as the input to predict and the drainage as the output. The underlying logic for this approach is based on the fact that the water passing through the waste rock storage is mainly from two sources: 1 precipitation falls directly onto the storage and infiltrates into it; 2 groundwater originating from uphill precipitation flows into the storage from higher elevations. Both sources are highly dependent on rain, snow, temperature, hydrologic properties and geo-bio-chemical conditions in the field. As the hydrologic properties and geo-bio-chemical conditions are relatively stable than previous factors related to the weather, the evolution of total precipitation and mean temperature from ambient environment at the mine site is then adopted to correlate with the drainage flow rates and also drainage chemistries. The correlation can be gradually captured by machine learning through studying historical monitoring data from a specific waste rock storage. In addition, the reference [13] proposed to use the number of year and month as additional input to capture long-term fluctuation of drainage. As the number of month is naturally uncycled, the value of the month number has no meaning for machine learning but only brings learning issue when December transits to January. The chapter proposes to use the concept of accumulated days to capture the long term fluctuation instead. With further normalizing all input data, the refined feedforward neural network can better predict the drainage flow rates and further the drainage chemistries. A case study on a full-scale waste rock storage will be provided to validate the

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 feedfor-

> − =

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

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

*t p*, is the activation function and *Xt p*, is the output of that neuron.

**1** , , , ,, , *q t p t p t i t pi t p i X Xw b*

ϕ

 <sup>=</sup> <sup>+</sup> <sup>∑</sup> **<sup>1</sup>**

(1)

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

proposed approach in this chapter.

**2.1 Feedforward neural network**

ward neural network is generally illustrated as follows:

**2. Methodology**

cal regression process.

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

waste rock storages. However, the prediction accuracy is highly dependent on the quality of monitoring data as the proposed neural network is actually a mathematical regression process.

The proposed feedforward neural network selects the weather monitoring data from mine sites as the input to predict and the drainage as the output. The underlying logic for this approach is based on the fact that the water passing through the waste rock storage is mainly from two sources: 1 precipitation falls directly onto the storage and infiltrates into it; 2 groundwater originating from uphill precipitation flows into the storage from higher elevations. Both sources are highly dependent on rain, snow, temperature, hydrologic properties and geo-bio-chemical conditions in the field. As the hydrologic properties and geo-bio-chemical conditions are relatively stable than previous factors related to the weather, the evolution of total precipitation and mean temperature from ambient environment at the mine site is then adopted to correlate with the drainage flow rates and also drainage chemistries. The correlation can be gradually captured by machine learning through studying historical monitoring data from a specific waste rock storage. In addition, the reference [13] proposed to use the number of year and month as additional input to capture long-term fluctuation of drainage. As the number of month is naturally uncycled, the value of the month number has no meaning for machine learning but only brings learning issue when December transits to January. The chapter proposes to use the concept of accumulated days to capture the long term fluctuation instead. With further normalizing all input data, the refined feedforward neural network can better predict the drainage flow rates and further the drainage chemistries. A case study on a full-scale waste rock storage will be provided to validate the proposed approach in this chapter.
