Application of Soft Computing Techniques for Analysis of Groundwater Table Fluctuation in Bangkok Area and Its Vicinity

International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

http://www.TuEngr.com, http://go.to/Research

Application of Soft Computing Techniques for Analysis of Groundwater Table
Fluctuation in Bangkok Area and Its Vicinity

a* b c
Uruya Weesakul , Kunio Watanabe , and Natkritta Sukasem
a
Department of Civil Engineering Faculty of Engineering, Thammasat University, THAILAND
b
Geosphere Research Institute, Saitama University, JAPAN
c
Department of Civil Engineering Faculty of Engineering, Thammasat University, THAILAND

ARTICLEINFO A B S T RA C T
Article history: Being a good quality water resource, groundwater was over
Received 1 August 2010
Received in revised form used during the last three decades to serve high water demand due
20 September 2010 to rapid growth in Bangkok and its vicinity. Excessive pumping
Accepted 27 September 2010 rate of groundwater in Bangkok results in land subsidence problem
Available online
12 October 2010 and groundwater quality deterioration due to saltwater intrusion into
Keywords: shallow aquifers adjacent to the coast. This study applied a simple
Groundwater; linear Genetic Algorithm (GA) model as an alternative tool for
Artificial Neural Network (ANN);
Genetic Algorithm (GA)
monitoring and forecasting of groundwater table. Nonthaburi
aquifer, one of three major aquifers amongst seven aquifers in
greater Bangkok area, was analyzed in the study. Monthly
groundwater table of 43 monitoring wells, amongst 92 wells, 12
years (1997-2009) data was analyzed with land use map. GA was
used to divide groundwater basin into sub-regions. Comparison
between capability of GA and Artificial Neural Network (ANN)
models for prediction of groundwater level reveals that ANN model
has a better performance for all cases. However, GA model might
be used to predict groundwater level with an acceptable accuracy
(9% to 26% relative error). Better performance was obtained in
medium to high residential area and industrial area (9-19% relative
error). Due to its simplicity as well as period of record length of
data requirement, GA is another appropriate alternative tool for
monitoring and forecasting groundwater table fluctuation
particularly for insufficient data area.
2010 International Transaction Journal of Engineering, Management, & Applied
Sciences & Technologies. Some Rights Reserved.

*Corresponding author (Dr. Uruya Weesakul). Tel/Fax: +66-2-5643001 Ext.3189. E-mail addresses:
wuruya@engr.tu.ac.th 2010. International Transaction Journal of Engineering, Management, & Applied 53
Sciences & Technologies. Volume 1 No.1. eISSN: 1906-9642
Online Available at http://tuengr.com/V01-01/01-01-053-065{Itjemast}_Uruya.pdf

1. Introduction
Rapid growth of Bangkok and its vicinity in population, business, industries and tourism
results in increasing in water demand dramatically. Groundwater, as another good quality water
resources was over-abstraction during the last three decades in order to fulfill such high
requirement. Excessive pumping rate of groundwater in Bangkok and its adjacent 6 provinces
area (Nonthaburi, Pranakhon Si Ayutthaya, Patumthani, Samut-Prakan, Samut-Sakorn and
Nakhonpatom so called, Greater Bangkok area) results in land subsidence problem (AIT, 1982)
as well as groundwater quality deterioration due to saltwater intrusion into shallow aquifers
adjacent to the coast (Ramnarong, 1983 and Ramnarong, 1991).

Several studies were conducted in order to investigate appropriate measurement to alleviate
such problems, for example: mitigation of groundwater crisis and land subsidence in Bangkok
(Ramnarong and Buapeng, 1991), groundwater resources of Bangkok and its vicinity: impact and
management of groundwater and land subsidence in the Bangkok Metropolitan area and its
vicinity (JIGA, 1995) and groundwater impact beneath a major metropolis: the Bangkok
experience (Ramnarong, 1996) etc. A number of attempt were implemented in order to remedy
the problems such as control of groundwater use (mainly in the critical zone) to reduce
groundwater abstraction since 1983, effective use of groundwater Act of 1977 (since June 1978)
and enforcement of groundwater charges policy since 1985, (Ramnarong, 1999). Due to such
measurement and policy, presently, the groundwater situation in greater Bangkok seems to be
gradually recovered (Limskul and Koontanakulvong, and Phien-wej et. al, 2006). Particularly,
the strict policy on pricing measures in the year 2003 can alleviate over-abstraction problem
resulting in gradually increasing in groundwater level in greater Bangkok area (as shown in
Figure 6). However, it is still necessary to monitor and forecast fluctuation of groundwater level
for management and warning system.

Several methods were proposed and manipulated for monitoring system for groundwater
resources management in the area. For example, the three-dimensional groundwater flow model
(MODFLOW) and the one-dimensional consolidation model were successfully coupled and
calibrated to simulate the piezometric levels and land subsidence in the Bangkok aquifer system.
MODFLOW results can replicate the observed amount and variation of piezometric levels and
land subsidence better than the quasi 3-D model results (AIT, 1998). Artificial Neural Network
(ANN) model was applied to monitor groundwater level for management system in greater

54 Uruya Weesakul, Kunio Watanabe, and Natkritta Sukasem

Bangkok area, the results reveal that ANN can be applied very well to interpret artificial effect
and natural effect to groundwater system, therefore, it is very appropriate tool for monitoring and
management environmental and engineering problem (Watanabe and Weesakul, 2004).

However, it seems that the various models already developed require either a number of data
or mathematical skill for complicate manipulation, it is interesting to try to use a simple linear
Genetic Algorithm (GA) model requiring only monthly data with short term record length (less
than 10 years record) to analyzed and forecast a fluctuation of groundwater table in Bangkok
area. Therefore, this study tries to propose a simple linear Genetic Algorithm (GA) model to
apply to monitor and forecast fluctuation of groundwater table in Bangkok area and its vicinity,
as another alternative tool for groundwater resources monitoring and management system.

2. Study Area and Data Collection

2.1 Study Area
Bangkok has no distinctive geological feature. The area consists entirely of alluvial deposits,
which accumulated during the Pleistocene period until the present day. It consists of very fine-
grained sediment mainly grayish or brownish clay forming a very thick layer with some silt, sand
or gravel lens. The deposits replenished every year by flooding of the Chao Phraya river. The
land is somewhat flatten with the elevation averaging around 1-2 metres above MSL. The
deposition took place somewhere around 25 million years ago and was part of the main central
flood plain regime of Thailand. Groundwater trapped in void between gravel and sand grains of
flood plain and lower terrace deposits, consisting of multiple aquifers from the depth of 40
meters. These aquifers are underlain and overlain by layer of relatively impermeable clays and
typically known as confined aquifer. Water quality is normally suitable for drinking as well as
household and industrial usages except in some areas and some aquifers, locally.

The ground surface of Bangkok is entirely underlined by blue to gray marine clay, 15-30
metres in thickness, known as the Bangkok Clay. Unconsolidated and semi-consolidated
sediments overlying the basement have a total thickness of about 400 metres to more than 1,800
metres. From detailed study of logs of groundwater wells, Department of Mineral Resources
(DMR) identified and named eight aquifers within 550 metres depth. These aquifers consist


mainly of sand and gravel separated by clay beds. Details of these aquifers are as shown in Table
1.
Table 1 Aquifers in Bangkok and its vicinity
Thickness Depth from ground elevation
Aquifer name
(m) (m)
Bangkok 30 16-30
Phra Pradaeng 20-50 60-80
Nakhon Luang 50-70 100-140
Nonthaburi 30-80 170-200
Sam Khok 40-80 240-250
Phaya Thi 40-60 275-350
Thonburi 50-100 350-400

Amongst these aquifers, Pha Pradaeng (PD), Nakhon Luang (NL) and Nonthaburi (NB)
aquifer are extensively utilized due to their availability of amount of water as well as their good
quality. According to availability of groundwater table data, and present extensively use,
groundwater from Nonthaburi (NB) aquifer was selected to be analyzed in this study.

2.2 Data Collection
The groundwater monitoring network in Bangkok was firstly established in 1987 under the
comprehensive study program on groundwater and land subsidence. The network was aimed at
monitoring potentionmetric and water quality in the three main aquifers of Phra Pradaeng (PD)
Nakhon Luang (NL) and Nonthaburi (NB). A network of groundwater monitoring system
consists of 279 monitoring wells, with 93 wells for PD, 94 wells for NL and 92 wells for NB.
Groundwater table data from 92 wells of NB aquifer were collected in the study. Preliminary
analysis of data reveals that only monthly data was recorded and some stations were just
implemented for few years. Based on availability of data, only 43 monitoring wells were selected
for further analysis in this study. Figure 1 shows distribution of location of these wells over
landuse map of greater Bangkok area. Landuse map in 2007 was collected and used in the further
clustering analysis.


Kilome

Figure 1: Location of monitoring wells on land use map of greater Bangkok area.

3. Analysis of Groundwater Table Fluctuation

3.1 Analysis of Correlation between Monitoring Wells
An agglomerative procedure was adopted in the study in order to investigate correlation of
groundwater table fluctuation between monitoring wells so that the similar behavior of
fluctuation can be grouped together. The result of analysis through correlation matrix reveals that
monitoring wells can be roughly grouped into 3 categories. The first group (7 wells) has low
correlation with correlation coefficient less than 0.9. The second group (26 wells) has medium
correlation with correlation coefficient between 0.9 and 0.95. The last group (16 wells) has high
correlation with correlation coefficient greater than 0.95. Table 2 shows classification of these
monitoring wells in each group.


Table 2 Classification of monitoring wells based on correlation coefficient and type of landuse
Correlation
coefficient
<0.90 0.90 0.95 >0.95

Landuse type
Low density NB61,NB86,NB88, NB02,NB35,NB46,
residential area NB89,NB90,NB91, NB47,NB64,NB82 -
and agricultural area NB92
NB24,NB38,NB58,NB63,
Medium density NB65,NB68,NB30,NB45,
residential area - NB50,NB51,NB55,NB62, -
NB81,NB87
High density
residential area
and industrial area
- - NB59,NB66,NB76,NB83

3.2 Clustering by Landuse Type
In order to be able to describe different behaviour of fluctuation of groundwater table in
different groups (as shown in Table 2). Landuse type was introduced to investigate locations of
wells in each group. It has been found that pattern of fluctuation of groundwater table in
agricultural area is less correlated to each other since use of groundwater for agricultural
purposes depends on amount of rainfall related to variation in climate situation. However, for
medium density to low density residential area, fluctuation of groundwater table has higher
correlation than agricultural area (0.90≥ρ≤0.95), since water supply system from surface water is
quite well distributed and behaviour of water use in the area is more predictable. The highest
correlation between wells was found in high density residential area and industrial area where
behavior of water use is quite certain and predictable. Table 2 shows classification of group of
wells based on type of landuse.


4. Division of Groundwater Flow Subbasin Using GA Model
Genetic algorithms (GA) is traditionally a procedure for operational similarities with the
biological and behavioral phenomena of living beings. In the last decade a flourishing literature
has been devoted to their application to real problems, after the pioneering work by John Holland
(1975). The basic of the method can be found in Goldberg (1989). Various application can be
found in Chambers (1995).

Kilomete

Figure 2: Groundwater flow sub-basin for low density residential area and agricultural area with
low correlation coefficient (ρ<0.9).

It is interesting to use GA model as a tool to describe groundwater flow region resulting in
division of groundwater flow sub-basin. Groundwater monitoring wells in each category as
shown in Table 2 were analyzed by using GA model. Each monitoring well in each group (Table
2) was then tested as a target well to be predicted by its neighboring wells with in the same
group. The resulted weighted coefficients (α) in linear equation of GA were used as indicator to
group monitoring wells within the same sub-basin. After successive processes of GA, for all
wells in each category, division of groundwater flow sub-basins can be identified as shown in
Figures 2 to 5.


Kilometres

Figure 3: Groundwater flow sub-basins for low density residential area and agricultural area with
high correlation coefficient ( >0.9).

Kilometers.

Figure 4: Groundwater flow sub-basins for medium density residential area.


Kilomete

Fig. 5. Groundwater flow sub-basins for high density residential area and industrial area

5. Forecasting of Groundwater Table Fluctuation Using GA and ANN
Models
In order to test capability of GA model for forecasting groundwater table fluctuation, GA
model was used to analyze fluctuation of groundwater table fluctuation of each monitoring wells
in each sub-basin (as shown in Figures 2 to 5) by using monthly groundwater data from 1997 to
2003 (7 years) as calibration period. Then monthly groundwater data from 2004 to 2009 (6 years)
was used for testing of performance of GA model in forecasting fluctuation of groundwater table.
Relative error between forecasted and observed groundwater table was adopted as indicator to
evaluate performance of model. ANN model was also used in the same manor for the purpose of
comparison with GA model. Figure 6 illustrates an example of results by comparison between
observed and forecasted groundwater table by GA and ANN models at monitoring well located at
Chatu Chak district, Bangkok (industrial area). It reveals that performance of GA model in
forecasting fluctuation of groundwater table is not much difference from ANN model. Table 3

summarizes the results of all cases, it has been found that ANN model can predict groundwater
table better than GA model for all cases, with average relative error of 9.64% for ANN model and
average relative error of 15.37% for GA model. However, considering simplicity of GA model
and short-term data record length requirement, GA model is an appropriate alternative tool for
forecasting groundwater table with acceptable accuracy, particularly for insufficient groundwater
data area.

Table 3: Comparison of performance of GA and ANN models in forecasting fluctuation of
groundwater table.
Relative error (%)
GA model ANN model
Landuse type Monitoring well
Calibration Prediction Calibration Prediction
1997-2003 2004-2009 1997-2003 2004-2009
Low density NB88,NB89,NB90, 18.32 26.41 9.65 17.49
residential NB91,NB92
area and
agricultural
9.08 11.67 4.28 8.64
area NB35,NB46,NB47,
<0.90 NB82
Medium NB24,NB38,NB58, 5.63 9.17 3.05 6.51
density NB63,NB65
residential area NB30,NB50,NB55, 10.98 13.98 2.79 5.16
0.90≤ρ≤0.95 NB81,NB62
High density NB11,NB27,NB32, 10.66 19.74 6.8 11.79
residential NB42,NB59
area and
industrial area NB53,NB54,NB66, 8.4 11.3 4.59 8.25
>0.95 NB76
Average 10.51 15.37 5.19 9.64


-22

Calibrat Predi
Groundwater level depth from assumed

-24

-26
ground elevation(m.)

-28

-30

-32

Observed
-34
GA model
-36 ANN model
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Year

Figure 6: Comparison between observed and forecasted groundwater table by GA and ANN
models at Chatu Chak, Bangkok (NB0042).

6. Conclusion
A simple linear Genetic Algorithm (GA) model was proposed to be used as another
alternative tool for monitoring and forecasting fluctuation of groundwater table in Bangkok area
its vicinity. Nonthaburi aquifer, one of three major aquifers amongst seven aquifers in greater
Bangkok area, was analyzed in the study. Monthly groundwater table of 43 monitoring wells
amongst 92 wells in the area, during 12 years (1997-2009) was analyzed with landuse map. GA
was used to divide the area into sub-regions of groundwater basin. Comparison between
capability of GA and ANN models reveals that ANN model has a better performance for all
cases. However, GA model can be used to predict groundwater level with an acceptable accuracy
(with 9% to 26% relative error). Better performance was obtained in medium to high residential
area and industrial area (9%-19% relative error). Due to its simplicity as well as short period of
record length of data requirement, GA is another appropriate alternative tool for monitoring and
forecasting groundwater table fluctuation particularly for insufficient data area.


7. Acknowledgement
This study was supported by the research collaboration between Saitama University and
Thammasat University under the International Collaborative Graduate Program on Civil and
Environmental Engineering (ICGP). Groundwater data was kindly provided by Department of
groundwater resources. All these supports are gratefully acknowledged.

8. References
Asian Institute of Technology. Investigation of land subsidence caused by deep well pumping in
the Bangkok area, phase IV : extension of subsidence observation network ; Research
report. Division of deotechnical and transportation engineering. Thailand 1982

Asian Institute of Technology. FEM quasi-3D modeling of responses to artificial recharge in the
Bangkok multiaquifers system. Environmental modeling and software 1998; 14: 141-151.

Chambers L. Practical Handbook of Genetic algorithms,Vols. 1 and 2.CRC Press. 1995

Department of Mineral Resources (DMR). Groundwater resources in the Bangkok area:
development and management study comprehensive report. Nation environment broad
Bangkok Thailand 1982

Goldberg D.E. Genetic algorithms in search. Optimization and machine learning. Addison-
Wesley 1989

Holland J.J. Adaptation in natural and artificial systems. University of Michigan Press. Ann
Arbor, MI .1975

Japan International Cooperation JICA. The study on management of groundwater and land
subsidence in the Bangkok metropolitan area and its vicinity. Report submitted to
Department of Mineral Resources and Public Works Department, Kingdom of Thailand
1995; 1-1 -11-5.

Limskul K. Koontanakulvong S. Groundwater pricing in greater Bangkok area. Water resources
systems research unit, Faculty of engneering, Chulalongkorn university.Thailand. 2004

Phien-wej N. Land subsidence in Bangkok Thailand. Engneering geology 2006; 82: 187-201.

Ramnarong, V. and Buapeng, S. Groundwater resources of Bangkok and its vicinity impact and
management. Proceedings of a national conference on geologic resources of Thailand
potential for future development Bangkok, Thailand 1992; 2: 172-184.

Ramnarong,V. and Buapeng S. Mitigation of Groundwater crisis and land subsidence in
Bangkok: J. Thai geosciences. 1991; 2: 125-137.

Ramnarong V. Evaluation of groundwater management in Bangkok: positive and negative.
Groundwater in urban environment: Department of mineral resources, Bangkok, Thailand
1999


Ramnarong V. Groundwater depletion and land subsidence in Bangkok. Proceedings of
conference on geology and mineral resources of Thailand, Department of mineral
resources, Bangkok, Thailand 1983

Ramnarong, V. Groundwater impact beneath a major metropolis: the Bangkok experience.
Proceedings of inaugural conference on groundwater and land-use planning, Fremantle,
Australia 1996; 107-117.

Watanabe K. and Weesakul U. Hydrological monitoring system based on the ANN: Application
to the groundwater management, Proceedings of the 9th nation convention on civil
engineering,Thailand 2004; INVITED-1-6.

Dr. Uruya Weesakul is Associate Professor at the Department of Civil Engineering, Faculty of Engineering,
Thammasat University. She received her B.Eng. (Civil Engineering) with Honors from Khonkhen University,
Thaialand. She received M.A. (Water resources Engineering) from Asian Institute of Technology (Thailand).
Also, she focused on remote sensing and gained M.A. (Remote sensing) GDTA , Toulouse (France). Later, she
received her PhD (Mechanical and Civil Engineering) from University of Montpellier II (France). Her current
research interests involve hydrological process in tropical southeast Asian area. Currently, Dr. Uruya Weesakul
is the Dean of the Faculty of Engineering, Thammasat University, Thailand.

Dr. Kunio WATANABE is Professor of the Geosphere Research Institute, Saitama University, Japan. He
received D.Eng. from University of Tokyo. He was JICA Expert at Thammasat University, Thailand (1997-
1998). Dr. WATANABE is specialized in ground water engineering, ground environmental engineering, and
geology.

Natkritta Sukasem was a graduate student at the Department of Civil Engineering, Faculty of Engineering,
Thammasat University. She received her B.Eng. from Kasetsart Univesity, Thailand. She is interested in
analysis of groundwater table fluctuation.


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