water resource management in a part of hindon basin, india...

96 Saumitra Mukherjee, Vijay Veer

International Journal of Innovations & Advancement in Computer Science

IJIACS

ISSN 2347 – 8616

Volume 3, Issue 4

June 2014

Water resource management in a part of Hindon basin, India

using Artificial Neural Networking and image processing

Technique

Saumitra Mukherjee

School of Environmental Sciences Jawaharlal Nehru University

New Delhi-110067

Vijay Veer Remote Sensing and GIS division

National Informatics Center

New Delhi- 110001

ABSTRACT

Artificial Neural Networking (ANN) analysis was done using MATLAB for 14 parameters of

groundwater quality in a part of Hindon basin India. The ANN analysis was correlated with the

maximum likelihood analysis using image processing software. It was found out that properties of

river water hardly affected the properties of ground water in this area. Though a nonlinear

relationship existed and may be validated in each case, they might not correspond one to one. It is

also important to note that though the models tested above were good to find a relationship between

various quality parameters in river water, the same were not found suitable to provide the fruitful

results for ground water.

Keywords: ANN; Groundwater Quality; Hindon basin; Image processing; MATLAB

INTRODUCTION

A lot of modeling work has been performed for Hindon basin, the present area. Most of them

are executed based on conventional approaches. Heavy metal concentrations in water for Hindon

basin with the amount of flowing water were studied by Jain et. al. (2001). They also studied the

effect of various operating variables, viz., initial concentration, solution pH, sediment dose, contact

time, particle size and temperature on adsorption characteristics of cadmium on bed sediments of

river Hindon in 2001. The adsorption data were analyzed using the Langmuir and Freundlich

adsorption models. Suthar et. al. (2009) assessed the level of heavy metals (Cd, Cr, Cu, Fe, Mn, Zn



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June 2014

and Pb) in water and sediments of Hindon River by taking 6 stations, covering the upstream and

downstream sites of Hindon. A study of the same nature was done by Jain and Sharma (2001). Gupta

et. al. (1979) carried out aquifer modeling studies of ground water in Krishni-Hindon interstream

region.

Possibility of predicting average summer-monsoon rainfall over India has been analyzed through

Artificial Neural Network model by Chattopadhyay (2007). Mohanty et. al. (2009) also developed

artificial neural network models for groundwater level forecasting in a river island of tropical humid

region. At the same time, the performance of the artificial neural network (ANN) model, i.e. standard

feed-forward neural network was examined by Sreekanth, et. al. (2009), for forecasting groundwater

level at Maheshwaram watershed, Hyderabad. Regression analysis of twelve data points of

underground drinking water of IM2 hand pumps at Moradabad, India was carried out by Navneet

Kumar and D.K. Sinha (2010) to study correlation of conductivity with ten parameters studied.

Location of study area

District Ghaziabad is situated in the middle of Ganga-Yamuna doab and spreads over 1966 sq

Km. It is bounded by longitude 770

12' to 780

13' and latitude 280

26' to 280

54' and is underlain by

Quaternary sediments. The district is administratively divided into 4 tehsils and is further divided

into 8 development blocks. The total population as per 2011 census is approx. 46,61,000. The density

of population is 1995 per sq km. District at Ghaziabad is drained by river Yamuna and Ganga and

their tributaries namely Hindon and Kali, Minor distributaries of Kali Nadi being Hawa drain

Chhoiya Nala and Chhoiya Nadi (Figure.1).

Materials Data and Software Used

Apart from topographic maps and satellite data, other historical data set is also used for the

analysis. The data is listed as below:

Rainfall data

Seismic Data

Discharge & River Water Level data

Ground Water Quality data

River Water Quality data

Data from Hazardous Industries

The above dataset provide a good help in training of the data sets for neural network. For applying the training results, sample data is also collected in field.



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Volume 3, Issue 4

June 2014

Figure.1.Hindon River basin , study area



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June 2014

Methodology

Development of ground water model whether through conventional or neural network involves exploring the related ground water parameters that involves the acquisition and analysis of

numerical data. Therefore, there should be a firm understanding of statistical and numerical methods as well as the ability to utilize relevant computer soft ware packages, in order to be able to analyze

the acquired data.

To study the correlation amongst various parameters of ground water and develop a

mathematical model with the help of neural network following steps are followed. Flow chart of the corresponding methodology is shown at respective places. Various steps are described below in brief.

Pre-processing of satellite data

In order to delineate various infra-structure and thematic layers, a number of Image processing techniques are utilized. Flow chart for this is shown in Plate 6.1. Raw digital satellite data contain geometric distortions due to instability of the satellite platforms, altitude & attitude variations

and earth rotation. In order for remote sensing data to be useful for resource and environmental users it must be having a definite scale and projection properties.

The sampling is conducted in selected parts of study area. The selection of sites is finalized after studying the input data on GIS as separate thematic layer. Following samples are finalized for

analysis through conventional methods as well as through neural network analysis.

Soil samples

Ground Water samples

River Water Data of Hindon (The station is at Galeta 770 28’ 12” in longitude and 290 04’

32” in latitude)

A number of software is used to execute the whole analysis. The analysis requires some kind of

Image Processing software, GIS Software and tools for mathematical analysis. These are listed below:

Rolta Geomatica 9.1.7 for Windows

ArcGIS 9.2 for Windows

Matlab R2010 for Windows

SPSS Statistics 17 for Windows

Neural Network Approach

In this method first the image is encoded into ASCII form using Erdas software and then

classified using MATLAB.

Classified images with these two above approaches are compared and analyzed to form a

final classified image. For this purpose, the two datasets are subtracted to find the anomalies. The Image dataset is again analyzed in those areas with the other datasets to remove the anomalies.



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Final classified image thus may be termed as Land use/ Land cover map of the study area and is a

combination of various thematic layers i.e. Agriculture, waste land, and some other infra-structure layers. The derived thematic layers shall help in the ground water model study based on

the various parametric studies.

Correlation with seismological data

For this purpose earthquake data since 1995 is analyzed with historical water level data at

various locations in and around the study area. Location and Magnitudes of these earthquakes’ epicenters along with water level locations are mentioned in Table. The distances of these epicenters

are also calculated from seven water level locations namely Nanpur, Sahibabad, Raoli, Simbhawali, Dhaulana, Tila Shabazpur and Muradnagar. Development of ground water model

Thirteen water quality parameters and seven heavy metals were used for the development of model. The model was made based on the following studies:

1. Dependence of One quality parameter (taken one at a time) with the others. For example s relation of electrical conductivity with sodium ions is studied with statistical approach. Same study has been extended through neural network. In this study last 10 year ground water quality data was used for

training of feed forward back propagation neural network and radial basis function neural network.

2. Dependence of One quality parameter with the remaining all taken together. The study may be

first done through regression analysis. After analyzing the results obtained from regression, input and targets were decided for neural network approach.

3. Dependence of Spatial distribution of water quality parameters altogether. Conventionally a

correlation matrix shall be generated for all quality parameters. Correlation and covariance has been studied for each parameter.

Same analysis was done through Self Organizing maps (SOM) of neural network.

4. Based on the permissible water quality values set up by Beuro of Indian Standard, water quality map is prepared for the study area

Artificial Neural Network study for Water resource management.

Artificial neural network (ANN) can be viewed as an interesting class of statistical pattern

recognition algorithm, which provides explicit facilities for modeling nonlinear and non-Gaussian

statistical regularities and proves to be a strong tool to prepare an equivalent model by virtue of its

capabilities of function approximation and classification. The potential of modeling the material

behavior using ANN was first proposed by Ghaboussi et al. In 1991. ANNs has become an attractive

technique for approaching many hydrologic problems as established by Daniel (1991). In general,

neural network offer viable solutions when there are large volumes of data to train the neural

network. When a problem is difficult or impossible to formulate analytically and experimental data

can be obtained, a neural network solution is normally appropriate.



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Conventional approach though simple and easy to perform, may not provide the accurate

result. This approach is basically dependent on the analyzing capability of the analyst. The whole

information is extracted manually through base maps and updated through visual interpretation of

satellite data.

Neural network and fuzzy logic have been successfully applied to a wide range of problems

covering a variety of sectors. An Artificial Neural Network (ANN) is a computing paradigm

designed to mimic human brain and nervous system. Neural network (NN) has a big role to play in

the field of water resources engineering where complex natural processes do minate. The high degree

of empiricism and approximation in the analysis of hydrologic systems make the use of NN highly

suitable. From mathematical point of view, it is complex non – linear function with many parameters

that are trained in such a way that ANN output becomes similar to the measured output on a known

dataset.

A Simple four layer fully connected feed forward neural network

Neural network are more efficient and require less amount of data for training (lee et al.). One

of the main advantages of neural networks for classification is that they are distribution free i.e. no

underlying model is assumed for the multivariate distribution of the class specific data in feature

space. It is therefore possible for a single class to be represented in feature space as a series of cluster

(rather than a single cluster). Recent works include incorporation of additional knowledge into the

neural network (Foody, 1995).

Present work focuses on various issues of ground water related problems of Hindon basin of

Ghaziabad district through Artificial Neural Network approach.



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June 2014

To develop a suitable model for Ground Water using Artificial Neural Network for Hindon

basin of Ghaziabad district of Uttar Pradesh, various thematic layers were derived.

The layers derived for the analysis are:

1. Agriculture

2. Wasteland

3. Land Use/ Land Cover

4. Geomorphology

5. Infra-Structure Layers

6. Digital Elevation Model

7. Surface Water

The data from these layers were fed into neural network to find out the satisfactory results.

Effect of Seismological data

For this purpose earthquake data since 1995 to 2010 was analyzed with historical water level data at

various locations in and around the study area.

Remote sensing and Artificial Neural Networking for Water resource management

It was observed from the water quality index map of the area that a majority of the samples

fall in very poor category having a water quality index > 20. Ground water at NTPC Dadri and

Dhaulana seems to be moderately good. Water quality at Pasaunda and Jalalpur were in ver y poor

category. Ground water at Newari might be treated as worse while at Bhikanpur it is not at all safe

for drinking purpose.

It is also noted that while land use pattern slightly affected ground water quality of the study

area, Normalized Difference Vegetation Index (NDVI) also proved to be a promising tool for

correlation with ground water quality. NDVI in most of the fallow land had a negative value, where

at Nidoli, Jalalpur and Bhadoli it had a slight positive value. Water quality index at these places were

in the range of 29-40.

In case of crop land pattern at Mawi, Abidpur and Muradnagar, Water Quality Index value

was in the range of 29-39 but NDVI was having both positive and negative values. Water Quality

Index value in the settlement areas at Ghaziabad, Surana and Khindora have a WQI in a range of 31-

34 while NDVI differed much. It was a negative value at Ghaziabad city while positive at other two

places.



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Important thing was noted that at Bhikanpur where almost all the quality parameters were

highest, NDVI value were found to be least. WQI value at this place was also found to be highest

among all the tested samples.

Sixteen neural network models were developed to describe the best relation between various

quality parameters; water quality index map provided the overall quality of ground water in the area.

Reliable information on water resources and its proper management are the most crucial

components for planning area specific development activities in today’s context. Ssurface water

available in the country is not adequate; making ground water is the only alternate source for day to

day activities. In recent years a constant decline in ground water level of Ghaziabad has been

observed due to urbanization, agricultural & industrial growth and unp lanned withdrawal from

subsoil aquifer.

Hindon basin of Ghaziabad district of Uttar Pradesh was selected for the present research

work. Being a highly industrialized and urbanized city, lots of municipal, industrial and agricultural

wastes are passed directly or indirectly to Hindon River. It is of prime importance to study ground

water quality of the area in terms of drinking purpose, its changing pattern due to surface water,

change in its level and quality due to seismic activity. Relation between these quality parameters is

also of prime importance in order to take measures of ground water quality management.

Remote sensing proves to be a powerful tool in water resources application; unfortunate part

is that quality of ground water cannot be directly mapped through it. Though a bit of information can

be gathered with the help of land use patterns and vegetation characteristics etc., its accurate

mapping cannot be ascertained.

Present study deals with development of ground water model with the help of ar tificial neural

network which enables to understand the complex phenomenon between various water quality

parameters that may not be accurately mapped through conventional approaches.

AwiFs, LISS III and Panchromatic satellite data of IRS 1D/ P6 along with the topographic

maps were used to create land use/ land cover map of the area. Geomorphology of the area depicted

that the area was divided into 9 main geomorphologic classes. Younger alluvial plain and older flood

plain occupied the entire upland. The area also showed mainly geomorphic lineaments in the vicinity

of Hindon River.



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June 2014

On interpretation of land use/ land cover map of the area, hindon basin was observed to be

located in a highly urbanized city. The area had a good amount of surface water as Hindo n river

along with a number of streams were found throughout the area. Various clusters of settlements may

also be seen including the main Ghaziabad city. City also had a number of industries producing a lot

of industrial waste most of them passes to Hindon without treatment. The area was mainly an

agriculture land. Approximately 25 % of the area was cropland while 57 % was covered in fallow

land. 10% belong to urban area, 8% of wasteland while remaining to other categories.

Additional data like water level and quality data of Hindon river and secondary ground water

locations, rainfall data and seismic data were also put to analysis wherever required. NDVI along

with lineament maps were later used in ground water modeling study.

Sampling sites for ground water were identified based on the land use/ land cover of the area,

their topography and vegetation conditions. Twenty two samples were analyzed for physical

parameters, cations, anions, heavy metals & trace elements. In most of the tested water samples, the

water was mainly NaHCO3 or CaHCO3 type and the average trend of dominant major cation was

Na+ >Ca2+> K+> Mg2+. Values of sodium were found beyond the permissible limit at Bhikanpur,

Newari and Pasuanda. Calcium and Magnesium concentration were found to be highest at Bhikanpur

while minimum at Dasna and Kakrana respectively.

Most of the study area is under permissible limit of sulphate. Nitrate pollution in the study

area was attributed mainly due to percolation of waste water from unlined surface dra ins carrying

sewage, landfills and street drainage etc. Nitrate was within permissible limit at all places except at

Bhikanpur. A very high value of chloride was observed at Bhikanpur, Newari, Jalalpur and Abidpur.

Newari was also found to have a high concentration of bicarbonate. Fluoride exceeded its standard

limit at Kalchhina, Newari and Pasaunda.

Statistical analysis of water samples showed a strong positive correlation of EC and TDS

with Na+, Ca2+, Mg2+, HCO3-, Cl-, SO4

2-, NO32-, and PO4

3-. Correlation of SO42-- Cl, Mg-Cl, Mg-SO4

suggested there might be an impact of agricultural activity. Major agricultural cropping was found in

the study area and excessive use of fertilizer led to increase ground water salinity. Correlation of Mg-

NO3, Mg-SO4 suggested a common source of these ions. NDVI observed with ground water quality

parameters was mostly not related with any parameter except in a moderate correlation with

potassium.



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Hydro chemical analysis revealed that the NaHCO3 type water facies was an indicative of

interaction of groundwater with schist, quartzite and granitic rocks. Presence of NaCl water type

also suggested that some water samples were facing the problem of salinity.

Principal component analysis performed on water quality parameters along with

corresponding NDVI values showed that only 3 principal components were able to explain almost

77% of the data. Factor 1 explained about 50% of the data and higher positive loading was observed

for almost all variables except Ph, potassium, phosphate, fluoride and NDVI. Factor 2 explained

about 14% of the data and high positive loadings were observed for potassium, phosphate and NDVI,

while Factor 3 was observed to have a positive higher loading for Ph and fluoride only and explained

about 13% of data.

Heavy metals and trace elements were found in permissible limit at all places except arsenic,

which was found to be at 23ppb at Jalalpur. Copper, Chromium and Silver were found in

concentration far less than the permissible limit prescribed by WHO. While arsenic, Manganese &

iron concentrations were found to be highest at Jalalpur, Zinc was observed to be highest at

Bhikanpur.

Select soil samples collected from vicinity of water samples were analyzed for heavy metals.

Minor enrichment of soil with cobalt, Zinc, Nickel was observed in the area, though it was found

uncontaminated in terms of chromium and molybdenum concentration. Igeo value for lead showed

uncontaminated to moderately contaminated soil while enrichment factor showed moderate to severe

enrichment of lead in the area. Most of the samples categorized into class 1 of uncontaminated

contaminated in respect of copper concentration.

To study the effect on ground water due to surface water, concentration of various chemical

parameters of ground water were studied with respect to increasing distance from river. Electrical

conductivity & TDS was found to be high at Bhikanpur and Newari situated near Hindon river and

upper Ganga canal respectively. But there were other places showing a low value in the vicinity of

the river.

Relation of river water quality parameters was studied with that of ground water. Correlation

matrix of quality parameters was generated and it was observed except for Ph; no other parameter

was in the same correlation in both river water and ground water.



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Same study was extended through neural network approach. Various feed forward neural

networks with back propagation were tried on river water quality parameters. Electrical Conductivity

was treated as target and rest 11 parameters; Ph, Sodium, Potassium, Calcium, Potassium,

Magnesium, Chloride, Fluoride, Bicarbonate, Sulphate, Phosphate and N itrate were chosen as

independent input. Input data set was normalized before feeding into the model.

10 year river water data was used to train the various networks. The available data was

randomly partitioned into training, testing and validation data sets. Network was trained using

training data whereby it computed outputs; compared outputs with target and then adjusted the

weights and repeated the process. For back propagation trainman function was used for training of

each network. The network was trained a number of times till mean squared error reached to a

minimum. Once the network was trained with the stopping criteria, it was tested with the test and

validation data sets. The water quality map shows relative water quality index (Figure.2, Table.1). It

was observed from the water quality index map of the area that a majority of the samples fall in very

poor category having a water quality index > 20. Ground water at NTPC Dadri and Dhaulana seems

to be moderately good. Water quality at Pasaunda and Jalalpur were in very poor category. Ground

water at Newari might be treated as worse while at Bhikanpur it is not at all safe for drinking

purpose. It is also noted that while land use pattern slightly affected ground water quality of the

study area, NDVI also proved to be a promising tool for correlation with ground water quality.

NDVI in most of the fallow land had a negative value, where at Nidoli, Jalalpur and Bhadoli it had a

slight positive value. Water quality index at these places were in the range of 29-40.

In case of crop land pattern at Mawi, Abidpur and Muradnagar, Water Quality Index value

was in the range of 29-39 but NDVI was having both positive and negative values. Water Quality

Index value in the settlement areas at Ghaziabad, Surana and Khindora have a WQI in a range of 31-

34 while NDVI differed much. It was a negative value at Ghaziabad city while positive at other two

places. Important thing was noted that at Bhikanpur where almost all the quality parameters were

highest, NDVI value were found to be least. WQI value at this place was also found to be highest

among all the tested samples. Sixteen neural network models were developed to describe the best

relation between various quality parameters; water quality index map provided the overall quality of

ground water in the area.



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Figure.2. Water quality index map of part of Hindon basin

Network architecture of best 5 models which provided accurate results for training, testing as well as

validation were reported. It was noted that Model with 3 hidden layers with logsig function and 5-10-

1 neurons was unable to be trained in spite of repeated attempts. Also Model with 3 hidden layers all

with logsig function and 8-4-1 neurons was unable to be trained. If the output layer function was

changed to Purelin instead of Logsig, results obtained were comparable for all datasets. Model M3 of

two hidden layers with 5 & 10 neurons each and sigmoid activation function was treated as best

while all models were also comparable to each other.



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Sample No. Name LULC Category NDVI Value WQI Value

VV-01 Nandgram Fallow Land -0.10429400 27

VV02 Bhikanpur Fallow Land -0.00653595 144

VV-03 Bhadoli Fallow Land 0.14666700 29

VV-04 Jalalpur Fallow Land 0.10810800 40

VV-05 Mawi Crop Land 0.20689700 32

VV-06 Newari Land with scrub 0.05109490 68

VV-07 Abidpur Crop Land 0.09589040 29

VV-08 Muradnagar Crop Land -0.03649630 34

VV-09 Morta Settlement -0.06756760 33

VV-10 Ghaziabad Settlement -0.14942500 39

VV-11 Sadarpur Fallow Land -0.08433730 24

VV-12 Dasna Fallow Land -0.07878790 37

VV-13 Dhaulana Fallow Land -0.08280250 20

VV-14 NTPC Fallow Land -0.03184710 18

VV-15 Kakrana Fallow Land -0.05952380 29

VV-16 Nidoli Fallow Land 0.02994010 30



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Table 1: Water Quality Index with Land Use/ Land Cover (LULC) Categories

Table 2: Comparison of Analysis through Regression and ANN (Model M7)

Model

(M7)

MSE Coefficient of Correlation ( R)

Training Testing Validation All

Regression 0.201393 0.400386

ANN 0.0242 0.967 0.981 0.999 0.989


Model

(M8)



Regression 0.1061 0.876

ANN 0.0142 0.886 0.940 0.670 0.886

VV-17 Kalchhina Fallow Land -0.02816900 28

VV-18 Galand Fallow Land -0.05128210 28

VV-19 Surana Settlement 0.04054050 31

VV-20 Khindora Settlement 0.05343510 34

VV-21 Pasaunda Fallow Land -0.10958900 40

VV-22 Charauri Fallow Land -0.07792210 30



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Model

(M9)



Regression 0.2189 0.08881

ANN 0.00632 0.659 0.369 0.962 0.408

Table 5: Comparison of Analysis through Regression and ANN(Model M10)



0.146814 0.7442

0.00495 0.975 0.682 0.0370 0.8444


Model

((M11)



Regression 0.086595 0.919105

ANN 0.00242 0.6314 0.9584 0.8866 0.9284



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Model

(M12)



Regression 0.16814 0.643978

ANN 0.0206 0.9886 0.9565 0.4987 0.9282


Model

(M14)



Regression 0.157763 0.696221

ANN 0.0148 0.8905 0.8781 0.7962 0.8886


Model

(M15)




Model

(M13)



Regression 0.105497 0.87726

ANN 0.00267 0.9747 0.5131 0.9254 0.9554



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Regression 0.082783 0.926349

ANN 0.0148 0.8905 0.8781 0.7962 0.8886


Model

(M16)



Regression 0.211397 0.273516

ANN 0.121 0.6105 0.7112 0.9294 0.6020


Model

(M17)



Regression 0.217817 0.133287

ANN 0.00518 0.9781 0.7099 0.9850 0.9586


Model

(M18)



Regression 0.219637 0.035819

ANN 0.0406 0.9588 0.4451 0.5060 0.6322



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Model

(M19)



Regression 0.023585 0.996828

ANN 1.48x10-7 1.0000 0.9952 0.9681 0.9993

Overall 14 parameters were taken into account for calculating the index map for water

quality. All these parameters were assigned a weight (wi) according to its relative importance in the

overall quality of water for drinking purpose. Assigning a proper weight to each parameter was very

important in finding out water quality index. The weight also depends upon the application or

purpose of quality index. It means that while a high weight of Ph is given if taste is of primary

concern, the same may be lowered in case of water used for irrigation. For quality index map

prepared for drinking purpose it becomes imperative to consider taste/color/odor also. Impact of

water quality on human health must not be ignored for preparation of such maps.

Result and Discussion

Based on a number of studies it is noted that adverse health effects due to potassium

consumption from drinking-water are unlikely to occur in healthy individuals. Sodium salts are

found in virtually all food (the main source of daily exposure) and drinking water and its level is

typically less than 20 ppm but can markedly exceed this in some countries. On the basis of existing

data, no firm conclusions were drawn concerning the possible impact of sodium in humans.

No health-based guideline value is proposed for hardness. However, the degree of hardness

in water may affect its acceptability to the consumer in terms of taste and scale deposition. There is

also no scientific evidence of magnesium toxicity in drinking water.

Each model was simulated to ground water to observe the predicted value of EC with all 11 input

parameters assigned to each model. Expected values of EC at some places were very high as

compared to the target value while at certain locations it is far below than the target value.



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It was concluded that properties of river water hardly affected the properties of ground water in this

area. Though a nonlinear relationship existed and may be validated in each case, they might not

correspond one to one. It is also important to note that though the models tested above were good to

find a relationship between various quality parameters in river water, the same were not found

suitable to provide the fruitful results for ground water.

Relation of river water level was then studied with that of ground water with the help of conventional

approach. From the correlation matrix generated, it was found that water level of Hindon river only

had a slight positive correlation with Garhmukteshwar which is the farthest point from Hindon river.

This relation was then studied with the help of feed forward back propagation neural network. Water

level of Hindon was treated as target while of secondary locations as input. Error in the expected

output was compared with the actual output to see the overall impact of river water level o n that of

ground water.

A feed forward back propagation network with sigmoid function, one hidden layer with 8 neurons

and 1 neuron in output layer was found best in this case. It might be interpreted that whether or not

the river Hindon was an effluent, but its water level might be ascertained by the adjacent ground

water level locations.

Effect of river water to the ground water in terms of level and quality; was studied using different

approaches and it was found that neural network analysis found to be a better analysis tool for

studying effect of river water on ground water as compared to conventional regression approach.

Application of neural network was found to be more effective in water level applications as

compared to water quality.

To study the effect of seismic activity on ground water level, earthquake data since 1995 were

analyzed with secondary water level & quality data at various locations in and around the study area.

After analyzing these graphs it was interpreted that water level followed its normal trend i.e.

increasing in monsoon season and then depleting in dry season, though some sharp changes were

observed at some points. Although no direct major impact of seismological activity on water level

fluctuations in the study area was found, a minor impact of seismic activity was observed on ground

water level at Tila Shabazpur & Simbhawali out of seven observed locations. However, geomorphic

lineaments could be located only near Tila Shabazpur. Thus effect of seismic activity in the area ma y

be treated as very mild. It may also be due to the fact that there was no major earthquake in the 3 X 3

degree region around the basin and not a single point of even micro-tremor was lying in the basin.



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Effect of seismic activity was also studied on ground water quality parameters. In view of

non availability of sufficient data, the study was extended for only four locations of the study area.

Electrical conductivity and bicarbonate at Dhaulana showed a steep fall after a tremor of 5.2

magnitude in 1998. Chloride continued to rise thereafter for 2- 3 years after 5.2 magnitude tremor

before rapidly going down. It might be the impact of after effects of the major tremors and

successive micro tremors in the vicinity of the area. It was also noted that calcium, magnesium and

nitrate concentrations started decreasing after a tremor in 1998. Concentration of sulphate, sodium,

chloride, magnesium and nitrate at Simbhawali followed a rapid fall after a micro tremor of 5.2 in

1998.

Nitrate was found to be constantly increasing since 1998 till 2001 at Tila Shabazpur. In view

of unavailability of data for further years, correlation with rainfall could not be established. Sulphate,

sodium, chloride, magnesium and calcium followed a pattern of rapid increase after a micro tremor

of 5.2 in 1998 in contrast to the case at Simbhawali. Sulphate concentration at Raoli might have an

overall impact due to seismic activity.

Hence, it may be concluded that most of the parameters showed a different pattern of

variation with seismic activity. This phenomenon may be treated as area specific and may not be

ascertained due to micro-tremors.

Development of a suitable and sustainable ground water model for the study area was taken

up in order to understand a true linear or non linear re lationship between various water quality

parameters. A number of cases were designed based on various combinations of inputs. Electrical

conductivity in each case was treated as target while other parameters individually or in a

combination were taken as input. All the cases were executed through regression as well as through

neural network to achieve the desirable result.

A number of models were developed so as to see the inter-relationship between various

ground water quality parameters along with NDVI. Each model with different network architecture

was able to understand the complex and non linear relation of input parameters. The outputs derived

through conventional regression and neural network models were plotted and it was found that in

each case ANNs were able to produce far superior output as compared to conventional approach. It

was also noted that because of complex nature of ground water quality parameters, each model was

input specific and could not be used for other parameters.



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Volume 3, Issue 4

June 2014

Though regression analysis through conventional approach only provided a linear relation

between output and input; neural networks also provided a relation between the predicted and target

value. In all developed models mean square error was much less as compared to conventional

regression. In case of model 22, where all the parameters along with NDVI were used; mean square

error was 0.02569 while 7.47x10-6 in case of neural network analysis. This model was developed

with one hidden layer of 4 neurons. Activation function was Logsig in hidden layer and purelin in

output layer. Overall correlation coefficient through neural network was found to be 0.9709 in

comparison of 0.957 for conventional approach. Only those models were considered which have a

high correlation coefficient in each datasets of training, testing and validation.

Water quality index map of the study area was finally prepared with sixteen quality

parameters. All these parameters were assigned a weight according to its relative importance to

calculate quality index. These quality index values were correlated with the land use/ land cover

classes of the Hindon basin. Vegetation anomalies were also looked upon for these locations.

It was noted that a majority of the samples fall in very poor category having a water quality

index >20. Ground water at Dadri and Dhaulana seems to be moderately good which falls in fallow

land category. Water quality was found to be very poor at Pasaunda and Jalalpur which belonged to

fallow land category. Ground water at Newari might be treated as worse while at Bhikanpur it was

not suitable for drinking purpose. Some water samples collected from crop land category also found

to be poor in terms of water quality. Hence, a minimum impact of land use category on water quality

was observed in the study area.

NDVI in most of the fallow land had a negative value where water quality index range was

29-40. It was also noted that at Bhikanpur where almost all the quality parameters were highest,

NDVI value were found to be least. Water Quality Index value at this place is also found to be

highest among all the tested samples.

REFERENCES

[1] Chattopadhyay S. (2007): Feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India, Institute of Geophysics, Polish Academy of Sciences, vol. 55,

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[2] Daniell, T. M, 1991. ‘‘Neural networks—Applications in hydrology and water resources

engineering.’’ Int. Hydrology and Water Resources Symposium, Perth, 2–4 October 1991, 791–802.



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Volume 3, Issue 4

June 2014

[3] Foody, G.M. 1995, land cover classification using an artificial neural network with ancillary

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[4] Ghaboussi, J., Garret, J. and Wu, X. (1991) “Knowledge-based modeling of material behavior with neural networks,” Journal of Engineering Mechanics, ASCE 117(1), 132–153.

[5] Gupta C.P., Thangarajan, M. and Rao V.V.S.G. (1997): Electric Analog Model Study of Aquifer in Krishni- Hindon Interstream Region, U.P., India. Vol. 17, No. -3, Ground Water- May-June,

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[7] Jain C. K. and Sharma M. K. (2001): Distribution of trace metals in the Hindon River System, India, Journal of Hydrology 253 (2001) 81-90.

[8] Kumar Navneet and Sinha, D.K. (2010): Drinking water quality management through correlation studies among various physicochemical parameters: A case study; international journal of

environmental sciences, Volume 1, No 2 ,2010; 253-259

[9] Mohanty S., Jha, Madan K., Kumar A. and Sudheer K. P. (2009): Artificial Neural Network

Modeling for Groundwater Level Forecasting in a River Island of Eastern India Water Resources Management, DOI 10.1007/s11269-009-9527.

[10] Sreekanth, P. D., Geethanjali, N., Sreedevi, P. D., Ahmed, S., Kumar, N. R. and Kamala Jayanthi, P. D. (2009): Forecasting groundwater level using artificial neural networks, Current

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[11] Suthar Surindra, Nema, Arvind K., Chabukdhara, Mayuri and Gupta, Sanjay K., Assessment of

metals in water and sediments of Hindon River, India: Impact of industrial and urban discharges, Journal of Hazardous Materials 171 (2009) 1088–1095

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