The document describes two indices - a logit-based index and a neural network-based index - for assessing the interaction between extractive quarrying activities and groundwater resources. The indices are based on parameters relating to the quarry operation and surrounding groundwater conditions. The indices were developed, compared and applied to a case study quarry to predict interaction levels and guide further environmental impact assessments. Future work could involve expanding the approach to other mining activities and testing alternative machine learning methods.

1. Development and comparison of two interaction indices between
extractive activity and groundwater resources
J. Barthélemy, T. Carletti, L. Collier, V. Hallet, M. Moriamé, A. Sartenaer
SMART (University of Wollongong, Australia)
NAXYS (University of Namur, Belgium)
July 2015 - Wollongong
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 1 / 19

2. Outline
1 Introduction and motivation
2 Interaction index and parameters
3 Discrete choice-based Logit indicator
4 Neural network-based indicator
5 Application
6 Conclusions
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 2 / 19

3. Introduction and motivation
Underground resources: water and rock
Population density and environmental pressures
⇓
Limited lateral quarry extension
⇓
Excavate deeper
⇓
Aquifer level reached
⇓
Pumping systems to depress the water table
⇓
Need to assess the environmental impact
⇓
Interaction index
The Neufvilles quarry (Belgium)
Maximal dewatering flow of
2.000.000 m3/year
Water table dropped from 70m
down to 25m.
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 3 / 19

4. Quarries and groundwater in Wallonia (Belgium)
Area: 16,844 km2
Population: 3 585 214 (210/km2)
Quarries: 160 sites, 73 × 106 T/year
Groundwater: 380.4 × 106 m3/year
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 4 / 19

5. Interaction index
Interaction index (Dauphiné and Provitolo, 2003)
I = f (Quarry, Groundwater)
Interaction index Feasibility study
low hydrogeology characterization
medium low + piezometric monitoring
high medium + steady state (static) mathematical model
very high high + transient state (dynamic) mathematical model
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 5 / 19

6. Quarry Parameters: Hazard
Geological context
G1 completely isolated by other formations with low permeability
G2 limited extension and partly compartmentalized
G3 local extension
G4 regional extension
Hydrogeological context
H1 aquiclude formation
H2 aquitard formation
H3 aquifer formation
H4 carbonate aquifer formation
Piezometric context: altimetric level of the quarry floor
Z1 higher than the piezometric level of the water table
Z2 lower than the piezometric level of the water table but higher than the river thalweg
which is the regional base level
Z3 lower than the piezometric level of the water table and the altimetric level of the
river thalweg which is the regional base level
Z4 lower than the piezometric level of the water table and the altimetric level of the
river thalweg which is not the regional level any more (the river is perched)
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 6 / 19

7. Quarry Parameters: Hazard
Piezometric context
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 7 / 19

8. Groundwater Parameters: Vulnerability
Relative position of the quarry and the water catchments
C1 outside the drainage zone of a catchment
C2 in the drainage zone of a catchment
C3 in the distant prevention area of a catchment (50 days of delay in case of aquifer
contamination)
C4 in the close prevention area of a catchment (24 hours of delay in case of aquifer
contamination)
Production of the catchments
T1 lower than 2 m3/h
T2 between 2 and 10 m3/h
T3 between 10 and 30 m3/h
T4 greater than 30 m3/h
Potential quality of the catchments
L1 poor quality
L2 water potabilisable with minor treatment
L3 good quality water
L4 water of exceptional quality (mineral water)
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 8 / 19

9. Dataset: Physically feasible combination of the quarry parameters with I
Id Interaction index H Z G C T L
1 low 1 1 1 1 1 1
2 low 1 1 1 2 1 1
3 low 1 1 1 3 1 1
...
...
...
...
...
...
...
...
1511 medium 4 3 2 1 1 1
1512 medium 4 3 2 2 1 1
1513 medium 4 3 2 3 1 1
..
.
..
.
..
.
..
.
..
.
..
.
..
.
..
.
2198 high 4 1 1 3 2 4
2199 high 4 1 1 4 2 4
2200 high 4 1 1 3 3 2
...
...
...
...
...
...
...
...
3325 very high 4 4 4 2 4 4
3326 very high 4 4 4 3 4 4
3327 very high 4 4 4 4 4 4
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 9 / 19

10. First Index: Logit-based approach
Logit discrete choice model
Probability that entity n selects alternative i associated with utility Uin
Uin = Vin (observable) + ǫin (hidden)
Pin = P(Uin > Ujn) = P(ǫjn − ǫin < Vin − Vjn, ∀ j = i)
ǫin independent and F(ǫin) = e−e−ǫin
⇒ Pin =
eVin
k eVkn
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 10 / 19

11. Logit-based index
Model
Vl = 0
Vm = βm
+
4
k=1
βm
g,k Gk + βm
h,k Hk + βm
z,k Zk + βm
c,kCk + βm
t,k Tk + βm
l,kLk
Vh = βh
+
4
k=1
βh
g,k Gk + βh
h,k Hk + βh
z,k Zk + βh
c,kCk + βh
t,kTk + βh
l,kLk
Vv = βv
+
4
k=1
βv
g,k Gk + βv
h,k Hk + βv
z,k Zk + βv
c,kCk + βv
t,k Tk + βv
l,kLk
Summary statistics (BIOGEME)
Number of estimated parameters 48
Number of observations 3327
Null log-likelihood L(0) -4612.201
Final log-likelihood L(ˆβ) -1754.804
ρ2 0.620
¯ρ2 0.609
Diagnostic Convergence reached
Variance-covariance from analytical Hessian
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 11 / 19

12. Logit-based Index: Estimated parameters (BIOGEME)
k ˆβi
g,k
ˆβi
h,k
ˆβi
z,k
ˆβi
c,k
ˆβi
t,k
ˆβi
l,k
ˆβi
Vm
1 0.00 0.00 0.00 0.00 0.00 0.00
-9.42
2 -0.38 0.00 2.21 3.26 0.45 4.08
3 -0.39 2.97 4.65 5.35 1.69 4.64
4 -0.44 2.97 4.59 5.34 4.50 4.78
Vh
1 0.00 0.78 -5.35 -6.92 -3.57 -7.20
-1.97
2 0.00 0.00 0.00 0.00 0.00 0.00
3 0.00 5.11 3.88 4.99 2.97 1.91
4 0.00 5.11 3.84 5.35 6.47 3.24
Vv
1 0.00 -7.36 -16.90 -23.50 -13.00 -13.90
24.20
2 0.00 -8.96 -7.69 -8.48 -6.16 -3.66
3 0.00 0.00 0.00 0.00 0.00 0.00
4 0.00 0.00 0.00 0.78 2.53 3.30
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 12 / 19

13. Second Index: Artificial neural network-based approach
Decision rule
I = arg max
l,m,h,v
{Ol , Om, Oh, Ov }
Logistic activation function
f (Hi ) = max 0,
1
e− k wki Ik
− θi
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 13 / 19

14. Feed-forward neural network: Choosing the right design
Proportion of correct prediction with respect to the number of nodes in the hidden layer
⇒ 7 nodes in the hidden layer (92.19% of correct predictions)
Training: R neuralnet package, 75% training, 25% validation
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 14 / 19

15. Application on the Boverie quarry and the Tridaine spring (Lhoist Group)
G2, H4, Z1, C4, T4, L3
Tridaine Spring
Provide water to Rochefort and
Saint-Remy’s abbey (beer!)
Boverie quarry
High purity limestone ($$$)
Google Earth
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 15 / 19

16. Application on the Boverie quarry and the Tridaine spring (Lhoist Group)
Current situation
G2, H4, Z1, C4, T4, L3
Logit:
Pl ≤ 0.01%, Pm = 1.50%, Ph = 66.4%, Pv = 32.1%
⇒ high level of interaction likely, but very high must no be ruled out!
ANN:
Ol = 0.002, Om = 0.001, Oh = 0.037, Ov = 0.960
⇒ very high level of interaction.
Recommendation
Complete mathematical hydrodynamic and geographic modelling!
(Dassargues et al., 2012)
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 16 / 19

17. Application on the Boverie quarry and the Tridaine spring (Lhoist Group)
Deepening the quarry
Altimetric levels
current: 220 m groundwater: 215.5 m planned: 160 m
Z1 ⇒ Z4
Logit:
Pl ≤ 0.01%, Pm ≤ 0.01%, Ph ≤ 0.01%, Pv = 99.9%
⇒ very high level of interaction.
ANN:
Ol = 0.002, Om = 0.001, Oh = 0.035, Ov = 0.962
⇒ very high level of interaction.
Recommendation
Same as previously!
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 17 / 19

18. Conclusion and future work
2 interaction indices between extractive activity and groundwater resources
AAN and Logit-based indices
Depenging on I, operator will conduct an appropriate feasibility study
Satisfactory results and successfully applied on one quarry
Accuracy: ANN > Logit
ANN and Logit complementary:
→ Logit is probabilistic
→ ANN is a deterministic black box
Future work
Extension to other type of mining activities
Testing alternative ANN designs, decision trees, discrete choice models,...
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 18 / 19

19. Thank you for your attention! And...
Barthélemy et al. (SMART - NAXYS) Interaction indices July 2015 19 / 19