Paper Presentation: To develop two different ANN models and compare their performances to evaluate the current situation and predict the behavior of water quality with respect to changes in pollutant loads and hydrological conditions.

1. Comparison of ANN Algorithm
for Water Quality Prediction of
River Ganga
Aradhana Giri and N.B. Singh
Department of Civil Engineering, Institute of Engineering and Technology
Lucknow, UP, India
Presented By:
Suresh Pokharel (074MSCSK015)
M.Sc. in Computer System and Knowledge Engineering
Institute of Engineering, Pulchowk Campus 1

2. Introduction
● Maintaining the Availability and Quality of the freshwater
resources : A vital environmental challenge
● Human actions in urban areas surrounding the Ganga River (i.e.
Kanpur, Allahabad, Varanasi etc.) generates severe impact.
Causes of Water Pollution:
- Rapid increase in population
- Rising standards of living
- Exponential growth of Industrialization 2

3. Introduction to Ganga River
● The most worshipped river of the Hindus
● One of the most polluted river of the country
● 25 big cities located along its bank
● 95% of the sewage enters directly to the river
● Total length: 2525 KM (Gangotri to Gangasagar)
● Highly Polluted: 600 KM
3

4. Ganga River : National River of India
4

5. A man cleaning garbage along the banks of the river
Ganges in Kolkata, India, April 9, 2017. 5

6. Objective
To develop two different ANN models and compare their
performances to evaluate the current situation and
predict the behaviour of water quality with respect to
changes in pollutant loads and hydrological conditions
6

7. Gathered Data
● Hydro Meteorological data
● Quality Measurement in River
● Land use
● Management practices on land
● Point pollution measurement
● Flow and water level data
7

8. Table: PRIMARY WATER QUALITY CRITERIA FOR
DESIGNATED (BEST - USE - CLASSES)
Source: http://www.uppcb.com/river_quality.htm
8

9. Source: http://www.uppcb.com/pdf/march24april18.pdf
9

10. Dissolved Oxygen
● Dissolved oxygen refers to the level of free,
non-compound oxygen molecule present in water
● Fish and aquatic animals cannot split oxygen from water
(H2O) or other oxygen-containing compounds.
● Measured in units of milligrams of gas per liter of water –
mg/L. (parts per million or ppm).
10

11. How Temperature affects water quality?
● Temperature impacts both the chemical and biological
characteristics of surface water
● Warm water is less capable of holding dissolved oxygen.
● Low dissolved oxygen levels leave aquatic organisms in a
weakened physical state and more susceptible to
disease, parasites, and other pollutants.
11
Source: US Geographical Survey (https://water.usgs.gov/edu/temperature.html)

12. Artificial Neural Network Model
Monthly data of previous 5 years (2008 to 2012) are measured with
attributes:
● Temperature
● Flow rate
● BOD (Biochemical Oxygen Demand)
● DO (Dissolved Oxygen)
Implementation Platform: MATLAB NN Toolbox
Data Source: Uttar Pradesh Pollution Control Board (UPPCB)
12

13. Division of Data
Training Data : 70%
Testing Data : 30%
Input_Train: 1x42
Output_Train: 1x42
Input_validation: 1x18
Output_validation: 1x18
13

14. Training Algorithms
Back Propagation:
Backpropagation works by
using a loss function to
calculate how far the
network was from the target
output.
14

15. Training Algorithm - 1
Gradient Descent with adaptive learning:
● Function name: traingda
● A network training function that updates weight and bias values
according to gradient descent with adaptive learning rate.
● Backpropagation is used to calculate derivatives of performance
dperf with respect to the weight and bias variables X.
● Each variable is adjusted according to gradient descent:
dX = alpha*d(perf)/dX
● Gradient Descent has a problem of getting stuck in Local Minima
15

16. Activation Function for GDA
For Hidden Layer
real-valued function ф whose value depends only on the distance
Gaussian radial basis function
r = |x-xi
| ф = e-(εr)^2
For Output Layer
Linear activation function
Transfer Function
Hyperbolic Sigmoid Tangent function ( tansig )
16

17. Training Algorithm - 2
Levenberg Marquardt Back Propagation:
● The LM curve-fitting method : combination the gradient descent method and the
Gauss-Newton method.
Backpropagation is used to calculate the Jacobian jX of performance perf with
respect to the weight and bias variables X.
Each variable is adjusted according to Levenberg-Marquardt,
jj = jX * jX
je = jX * E
dX = -(jj+I*mu) je where E is all errors and I is the identity matrix.
Function in Matlab: trainlm
Source: https://www.mathworks.com/help/deeplearning/ref/trainlm.html
17

18. Activation Function for LM
For Hidden Layer
Tanh (Hyperbolic Tangent function)
f(x) = 1 — exp(-2x) / 1 + exp(-2x)
-1 < output < 1
For Output Layer
Linear activation function
Transfer Function
Log Sigmoid Transfer function (logsig )
18

19. Testing
● Test with unknown data set
● After training with each ANN
configuration, Performance evaluation
methods used :
Correlation Coefficient
Mean Square Error (MSE)
Root Mean Square Error (RMSE) Xi
: Real ith data
Yi
: ith Estimate
N : NUmber of Xi, Yi
X
̄ : Average of X
19

20. Results
Fig. Comparison between output of Levenberg Marquardt (LM) backpropagation and Gradient Descent with
adaptive learning (GDA) rate backpropagation
20

21. 21

22. Results (contd ...)
Best Trained R = 0.9860
Best Validation R = 0.9800
Best Output vs. Target = 0.9919
● In comparison to GDA, result of LM is found to be more
accurate
● LM is faster as compared to GDA
22

23. References
1. Giri, Aradhana, and N. B. Singh. "Comparison of Artificial Neural Network
Algorithm for Water Quality Prediction of River Ganga." Environmental
Research Journal 8.2 (2014): 55-63.
2. Webb, Bruce W., and Franz Nobilis. "Long-term changes in river temperature
and the influence of climatic and hydrological factors." Hydrological Sciences
Journal 52.1 (2007): 74-85.
3. Zhang, Guoqiang, B. Eddy Patuwo, and Michael Y. Hu. "Forecasting with
artificial neural networks:: The state of the art." International journal of
forecasting 14.1 (1998): 35-62.
4. Maier, Holger R., and Graeme C. Dandy. "Neural networks for the prediction
and forecasting of water resources variables: a review of modelling issues and
applications." Environmental modelling & software 15.1 (2000): 101-124.
23

24. Thank You!
24