International Transaction Journal of Engineering, Management, & Applied Sciences & TechnologiesInternational Transaction J...
1. Introduction     Rapid growth of Bangkok and its vicinity in population, business, industries and tourismresults in inc...
Bangkok area, the results reveal that ANN can be applied very well to interpret artificial effectand natural effect to gro...
mainly of sand and gravel separated by clay beds. Details of these aquifers are as shown in Table1.                       ...
Kilome          Figure 1: Location of monitoring wells on land use map of greater Bangkok area.3. Analysis of Groundwater ...
Table 2 Classification of monitoring wells based on correlation coefficient and type of landuse              Correlation  ...
4. Division of Groundwater Flow Subbasin Using GA Model      Genetic algorithms (GA) is traditionally a procedure for oper...
KilometresFigure 3: Groundwater flow sub-basins for low density residential area and agricultural area with               ...
Kilomete     Fig. 5. Groundwater flow sub-basins for high density residential area and industrial area5. Forecasting  of  ...
summarizes the results of all cases, it has been found that ANN model can predict groundwatertable better than GA model fo...
-22                                                                     Calibrat                                      Pred...
7. Acknowledgement     This study was supported by the research collaboration between Saitama University andThammasat Univ...
Ramnarong V. Groundwater depletion and land subsidence in Bangkok. Proceedings of      conference on geology and mineral r...
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Application of Soft Computing Techniques for Analysis of Groundwater Table Fluctuation in Bangkok Area and Its Vicinity

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Being a good quality water resource, groundwater was over used during the last three decades to serve high water demand due to rapid growth in Bangkok and its vicinity. Excessive pumping rate of groundwater in Bangkok results in land subsidence problem and groundwater quality deterioration due to saltwater intrusion into shallow aquifers adjacent to the coast. This study applied a simple linear Genetic Algorithm (GA) model as an alternative tool for 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.

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

  1. 1. International Transaction Journal of Engineering, Management, & Applied Sciences & TechnologiesInternational Transaction Journal of Engineering, Management, & Applied Sciences & Technologies http://www.TuEngr.com, http://go.to/ResearchApplication of Soft Computing Techniques for Analysis of Groundwater TableFluctuation in Bangkok Area and Its Vicinity a* b cUruya Weesakul , Kunio Watanabe , and Natkritta Sukasema Department of Civil Engineering Faculty of Engineering, Thammasat University, THAILANDb Geosphere Research Institute, Saitama University, JAPANc Department of Civil Engineering Faculty of Engineering, Thammasat University, THAILANDARTICLEINFO A B S T RA C TArticle history: Being a good quality water resource, groundwater was overReceived 1 August 2010Received in revised form used during the last three decades to serve high water demand due20 September 2010 to rapid growth in Bangkok and its vicinity. Excessive pumpingAccepted 27 September 2010 rate of groundwater in Bangkok results in land subsidence problemAvailable online12 October 2010 and groundwater quality deterioration due to saltwater intrusion intoKeywords: shallow aquifers adjacent to the coast. This study applied a simpleGroundwater; linear Genetic Algorithm (GA) model as an alternative tool forArtificial 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 53Sciences & Technologies. Volume 1 No.1. eISSN: 1906-9642Online Available at http://tuengr.com/V01-01/01-01-053-065{Itjemast}_Uruya.pdf
  2. 2. 1. Introduction  Rapid growth of Bangkok and its vicinity in population, business, industries and tourismresults in increasing in water demand dramatically. Groundwater, as another good quality waterresources was over-abstraction during the last three decades in order to fulfill such highrequirement. Excessive pumping rate of groundwater in Bangkok and its adjacent 6 provincesarea (Nonthaburi, Pranakhon Si Ayutthaya, Patumthani, Samut-Prakan, Samut-Sakorn andNakhonpatom so called, Greater Bangkok area) results in land subsidence problem (AIT, 1982)as well as groundwater quality deterioration due to saltwater intrusion into shallow aquifersadjacent to the coast (Ramnarong, 1983 and Ramnarong, 1991). Several studies were conducted in order to investigate appropriate measurement to alleviatesuch problems, for example: mitigation of groundwater crisis and land subsidence in Bangkok(Ramnarong and Buapeng, 1991), groundwater resources of Bangkok and its vicinity: impact andmanagement of groundwater and land subsidence in the Bangkok Metropolitan area and itsvicinity (JIGA, 1995) and groundwater impact beneath a major metropolis: the Bangkokexperience (Ramnarong, 1996) etc. A number of attempt were implemented in order to remedythe problems such as control of groundwater use (mainly in the critical zone) to reducegroundwater 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 suchmeasurement and policy, presently, the groundwater situation in greater Bangkok seems to begradually 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 problemresulting in gradually increasing in groundwater level in greater Bangkok area (as shown inFigure 6). However, it is still necessary to monitor and forecast fluctuation of groundwater levelfor management and warning system. Several methods were proposed and manipulated for monitoring system for groundwaterresources management in the area. For example, the three-dimensional groundwater flow model(MODFLOW) and the one-dimensional consolidation model were successfully coupled andcalibrated 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 andland 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
  3. 3. Bangkok area, the results reveal that ANN can be applied very well to interpret artificial effectand natural effect to groundwater system, therefore, it is very appropriate tool for monitoring andmanagement environmental and engineering problem (Watanabe and Weesakul, 2004). However, it seems that the various models already developed require either a number of dataor mathematical skill for complicate manipulation, it is interesting to try to use a simple linearGenetic Algorithm (GA) model requiring only monthly data with short term record length (lessthan 10 years record) to analyzed and forecast a fluctuation of groundwater table in Bangkokarea. Therefore, this study tries to propose a simple linear Genetic Algorithm (GA) model toapply 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, sandor gravel lens. The deposits replenished every year by flooding of the Chao Phraya river. Theland is somewhat flatten with the elevation averaging around 1-2 metres above MSL. Thedeposition took place somewhere around 25 million years ago and was part of the main centralflood plain regime of Thailand. Groundwater trapped in void between gravel and sand grains offlood plain and lower terrace deposits, consisting of multiple aquifers from the depth of 40meters. These aquifers are underlain and overlain by layer of relatively impermeable clays andtypically known as confined aquifer. Water quality is normally suitable for drinking as well ashousehold 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, 15-30metres in thickness, known as the Bangkok Clay. Unconsolidated and semi-consolidatedsediments overlying the basement have a total thickness of about 400 metres to more than 1,800metres. 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*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 55Sciences & Technologies. Volume 1 No.1. eISSN: 1906-9642Online Available at http://tuengr.com/V01-01/01-01-053-065{Itjemast}_Uruya.pdf
  4. 4. mainly of sand and gravel separated by clay beds. Details of these aquifers are as shown in Table1. 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 goodquality. 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 thecomprehensive study program on groundwater and land subsidence. The network was aimed atmonitoring potentionmetric and water quality in the three main aquifers of Phra Pradaeng (PD)Nakhon Luang (NL) and Nonthaburi (NB). A network of groundwater monitoring systemconsists 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. Preliminaryanalysis of data reveals that only monthly data was recorded and some stations were justimplemented for few years. Based on availability of data, only 43 monitoring wells were selectedfor further analysis in this study. Figure 1 shows distribution of location of these wells overlanduse map of greater Bangkok area. Landuse map in 2007 was collected and used in the furtherclustering analysis. 56 Uruya Weesakul, Kunio Watanabe, and Natkritta Sukasem
  5. 5. 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 ofgroundwater table fluctuation between monitoring wells so that the similar behavior offluctuation can be grouped together. The result of analysis through correlation matrix reveals thatmonitoring wells can be roughly grouped into 3 categories. The first group (7 wells) has lowcorrelation with correlation coefficient less than 0.9. The second group (26 wells) has mediumcorrelation with correlation coefficient between 0.9 and 0.95. The last group (16 wells) has highcorrelation with correlation coefficient greater than 0.95. Table 2 shows classification of thesemonitoring wells in each group.*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 57Sciences & Technologies. Volume 1 No.1. eISSN: 1906-9642Online Available at http://tuengr.com/V01-01/01-01-053-065{Itjemast}_Uruya.pdf
  6. 6. 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 NB11,NB25,NB27,NB28, High density NB29,NB32,NB36,NB42, residential area NB53,NB54,NB56,NB57, and industrial area - - NB59,NB66,NB76,NB833.2 Clustering by Landuse Type  In order to be able to describe different behaviour of fluctuation of groundwater table indifferent groups (as shown in Table 2). Landuse type was introduced to investigate locations ofwells in each group. It has been found that pattern of fluctuation of groundwater table inagricultural area is less correlated to each other since use of groundwater for agriculturalpurposes depends on amount of rainfall related to variation in climate situation. However, formedium density to low density residential area, fluctuation of groundwater table has highercorrelation than agricultural area (0.90≥ρ≤0.95), since water supply system from surface water isquite well distributed and behaviour of water use in the area is more predictable. The highestcorrelation between wells was found in high density residential area and industrial area wherebehavior of water use is quite certain and predictable. Table 2 shows classification of group ofwells based on type of landuse. 58 Uruya Weesakul, Kunio Watanabe, and Natkritta Sukasem
  7. 7. 4. Division of Groundwater Flow Subbasin Using GA Model  Genetic algorithms (GA) is traditionally a procedure for operational similarities with thebiological and behavioral phenomena of living beings. In the last decade a flourishing literaturehas 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 befound 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 indivision of groundwater flow sub-basin. Groundwater monitoring wells in each category asshown in Table 2 were analyzed by using GA model. Each monitoring well in each group (Table2) was then tested as a target well to be predicted by its neighboring wells with in the samegroup. The resulted weighted coefficients (α) in linear equation of GA were used as indicator togroup monitoring wells within the same sub-basin. After successive processes of GA, for allwells in each category, division of groundwater flow sub-basins can be identified as shown inFigures 2 to 5.*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 59Sciences & Technologies. Volume 1 No.1. eISSN: 1906-9642Online Available at http://tuengr.com/V01-01/01-01-053-065{Itjemast}_Uruya.pdf
  8. 8. KilometresFigure 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. 60 Uruya Weesakul, Kunio Watanabe, and Natkritta Sukasem
  9. 9. Kilomete Fig. 5. Groundwater flow sub-basins for high density residential area and industrial area5. Forecasting  of  Groundwater  Table  Fluctuation  Using  GA  and  ANN  Models  In order to test capability of GA model for forecasting groundwater table fluctuation, GAmodel was used to analyze fluctuation of groundwater table fluctuation of each monitoring wellsin each sub-basin (as shown in Figures 2 to 5) by using monthly groundwater data from 1997 to2003 (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 toevaluate performance of model. ANN model was also used in the same manor for the purpose ofcomparison with GA model. Figure 6 illustrates an example of results by comparison betweenobserved and forecasted groundwater table by GA and ANN models at monitoring well located atChatu Chak district, Bangkok (industrial area). It reveals that performance of GA model inforecasting fluctuation of groundwater table is not much difference from ANN model. Table 3*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 61Sciences & Technologies. Volume 1 No.1. eISSN: 1906-9642Online Available at http://tuengr.com/V01-01/01-01-053-065{Itjemast}_Uruya.pdf
  10. 10. summarizes the results of all cases, it has been found that ANN model can predict groundwatertable better than GA model for all cases, with average relative error of 9.64% for ANN model andaverage relative error of 15.37% for GA model. However, considering simplicity of GA modeland short-term data record length requirement, GA model is an appropriate alternative tool forforecasting groundwater table with acceptable accuracy, particularly for insufficient groundwaterdata 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 62 Uruya Weesakul, Kunio Watanabe, and Natkritta Sukasem
  11. 11. -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 anotheralternative tool for monitoring and forecasting fluctuation of groundwater table in Bangkok areaits vicinity. Nonthaburi aquifer, one of three major aquifers amongst seven aquifers in greaterBangkok area, was analyzed in the study. Monthly groundwater table of 43 monitoring wellsamongst 92 wells in the area, during 12 years (1997-2009) was analyzed with landuse map. GAwas used to divide the area into sub-regions of groundwater basin. Comparison betweencapability of GA and ANN models reveals that ANN model has a better performance for allcases. 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 residentialarea and industrial area (9%-19% relative error). Due to its simplicity as well as short period ofrecord length of data requirement, GA is another appropriate alternative tool for monitoring andforecasting groundwater table fluctuation particularly for insufficient data area.*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 63Sciences & Technologies. Volume 1 No.1. eISSN: 1906-9642Online Available at http://tuengr.com/V01-01/01-01-053-065{Itjemast}_Uruya.pdf
  12. 12. 7. Acknowledgement  This study was supported by the research collaboration between Saitama University andThammasat University under the International Collaborative Graduate Program on Civil andEnvironmental Engineering (ICGP). Groundwater data was kindly provided by Department ofgroundwater 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 1982Asian 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. 1995Department of Mineral Resources (DMR). Groundwater resources in the Bangkok area: development and management study comprehensive report. Nation environment broad Bangkok Thailand 1982Goldberg D.E. Genetic algorithms in search. Optimization and machine learning. Addison- Wesley 1989Holland J.J. Adaptation in natural and artificial systems. University of Michigan Press. Ann Arbor, MI .1975Japan 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. 2004Phien-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 64 Uruya Weesakul, Kunio Watanabe, and Natkritta Sukasem
  13. 13. 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 1983Ramnarong, 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.*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 65Sciences & Technologies. Volume 1 No.1. eISSN: 1906-9642Online Available at http://tuengr.com/V01-01/01-01-053-065{Itjemast}_Uruya.pdf

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