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Integrated Geophysical, Hydrogeochemical and Artificial Intelligence Techniques for Groundwater Study in the Langat Basin, Malaysia.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Integrated Geophysical, Hydrogeochemical and Artificial Intelligence Techniques for Groundwater Study in the Langat Basin, Malaysia./
Author:	Khaki, Mahmoud.
Description:	1 online resource (213 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-02, Section: A.
Contained By:	Dissertations Abstracts International85-02A.
Subject:	Water quality. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30595379click for full text (PQDT)
ISBN:	9798379978983

Integrated Geophysical, Hydrogeochemical and Artificial Intelligence Techniques for Groundwater Study in the Langat Basin, Malaysia.
Khaki, Mahmoud.

Integrated Geophysical, Hydrogeochemical and Artificial Intelligence Techniques for Groundwater Study in the Langat Basin, Malaysia. - 1 online resource (213 pages)

Source: Dissertations Abstracts International, Volume: 85-02, Section: A.

Thesis (Ph.D.)--University of Malaya (Malaysia), 2014.

Includes bibliographical references

Geophysical, hydrogeochemical and artificial intelligence techniques were used to study the groundwater characteristics and their associated problems in Langat Basin, Malaysia. Resistivity surveys and geochemical analyses were used to delineate regions of Langat Basin that are contaminated by brackish water. Hydrogeochemical data of groundwater samples collected from seventeen wells from 2008 to 2013 were analysed. Ninety eight geoelectrical resistivity survey measurements were conducted to obtain subsurface resistivity data. The Wenner array was selected because of its sensitivity in detecting vertical changes in subsurface resistivity. The resistivity imaging results show that the upper layer is usually clay and below this layer is an aquifer with various depths of 10 to 30 m, and the layer thickness changes from 10 to 45 m, respectively, from east to west across the study area. The depth to bedrock varies from 30 m up to 65 m. The results learned from the resistivity survey confirmed the pattern of a continuous structure of layers, as detected from the borehole and geological information. Chemical analyses show the total dissolved solid exceeds 1000 mg/L in the west and is less than 1000 mg/L in the east of study area. Furthermore, the results of the resistivity survey and those from the hydrogeochemical analyses show that the groundwater within the study area is a mixture of brackish and freshwater zones.A novel investigation in modelling of groundwater level and quality using Artificial Neural Networks (ANNs) and Adaptive neuro fuzzy inference systems (ANFIS) methods was developed in the study area. Water table modelling based on ANNs and ANFIS technology were developed to simulate the water table fluctuations based on the relationship between the variations of rainfall, humidity, evaporation, minimum and maximum temperature and water table depth. The mean square errors and correlation coefficient of the water table depth models for 84 months were between 0.0043 to 0.107 and 0.629 to 0.99 respectively for all models. Evaluating the results of the various kinds of models show the earned results of the ANFIS model are superior to those gained from ANNs in which they are both more precise and with less error. Furthermore, four common training functions; Gradient descent with momentum and adaptive learning rate back propagation, Levenberg-Marquardt algorithm, Resilient back propagation, Scaled conjugate gradient were compared for the modelling of groundwater level. These results confirm that, for all the networks the Levenberg-Marquardt algorithm is the most effective algorithm to model the groundwater level. This study also developed the potential of the ANFIS and ANN to simulate total dissolved solid (TDS) and electrical conductivity (EC) by employing the values of other existing water quality parameters from five sampling stations over six years from 2008 to 2013. A good agreement between simulate values and their respective measured values in the quality of the groundwater were found. TDS and EC values predicted from the model accompanying obtained result present increasing in concentration continually in the future whereas for well no 3 (the nearest well to costal line) reaches at 32625.51 mg/L and 69501.76 μS/cm in 2025, respectively.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379978983Subjects--Topical Terms:

556913
Water quality.
Index Terms--Genre/Form:

542853
Electronic books.

Integrated Geophysical, Hydrogeochemical and Artificial Intelligence Techniques for Groundwater Study in the Langat Basin, Malaysia.
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100 1 $a Khaki, Mahmoud. $3 3702096
245 1 0 $a Integrated Geophysical, Hydrogeochemical and Artificial Intelligence Techniques for Groundwater Study in the Langat Basin, Malaysia.
264 0 $c 2014
300 $a 1 online resource (213 pages)
336 $a text $b txt $2 rdacontent
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338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-02, Section: A.
500 $a Advisor: Hj Taib, Samsudin.
502 $a Thesis (Ph.D.)--University of Malaya (Malaysia), 2014.
504 $a Includes bibliographical references
520 $a Geophysical, hydrogeochemical and artificial intelligence techniques were used to study the groundwater characteristics and their associated problems in Langat Basin, Malaysia. Resistivity surveys and geochemical analyses were used to delineate regions of Langat Basin that are contaminated by brackish water. Hydrogeochemical data of groundwater samples collected from seventeen wells from 2008 to 2013 were analysed. Ninety eight geoelectrical resistivity survey measurements were conducted to obtain subsurface resistivity data. The Wenner array was selected because of its sensitivity in detecting vertical changes in subsurface resistivity. The resistivity imaging results show that the upper layer is usually clay and below this layer is an aquifer with various depths of 10 to 30 m, and the layer thickness changes from 10 to 45 m, respectively, from east to west across the study area. The depth to bedrock varies from 30 m up to 65 m. The results learned from the resistivity survey confirmed the pattern of a continuous structure of layers, as detected from the borehole and geological information. Chemical analyses show the total dissolved solid exceeds 1000 mg/L in the west and is less than 1000 mg/L in the east of study area. Furthermore, the results of the resistivity survey and those from the hydrogeochemical analyses show that the groundwater within the study area is a mixture of brackish and freshwater zones.A novel investigation in modelling of groundwater level and quality using Artificial Neural Networks (ANNs) and Adaptive neuro fuzzy inference systems (ANFIS) methods was developed in the study area. Water table modelling based on ANNs and ANFIS technology were developed to simulate the water table fluctuations based on the relationship between the variations of rainfall, humidity, evaporation, minimum and maximum temperature and water table depth. The mean square errors and correlation coefficient of the water table depth models for 84 months were between 0.0043 to 0.107 and 0.629 to 0.99 respectively for all models. Evaluating the results of the various kinds of models show the earned results of the ANFIS model are superior to those gained from ANNs in which they are both more precise and with less error. Furthermore, four common training functions; Gradient descent with momentum and adaptive learning rate back propagation, Levenberg-Marquardt algorithm, Resilient back propagation, Scaled conjugate gradient were compared for the modelling of groundwater level. These results confirm that, for all the networks the Levenberg-Marquardt algorithm is the most effective algorithm to model the groundwater level. This study also developed the potential of the ANFIS and ANN to simulate total dissolved solid (TDS) and electrical conductivity (EC) by employing the values of other existing water quality parameters from five sampling stations over six years from 2008 to 2013. A good agreement between simulate values and their respective measured values in the quality of the groundwater were found. TDS and EC values predicted from the model accompanying obtained result present increasing in concentration continually in the future whereas for well no 3 (the nearest well to costal line) reaches at 32625.51 mg/L and 69501.76 μS/cm in 2025, respectively.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Water quality. $3 556913
650 4 $a Precipitation. $3 3680799
650 4 $a Humidity. $3 3559498
650 4 $a Fuzzy sets. $3 562997
650 4 $a Back propagation. $3 3681810
650 4 $a Electrodes. $3 629151
650 4 $a Groundwater. $3 566494
650 4 $a Permeability. $3 915594
650 4 $a Neural networks. $3 677449
650 4 $a Hydrology. $3 545716
650 4 $a Brackish water. $3 3686894
650 4 $a Drilling. $3 3563985
650 4 $a Aquifers. $3 551140
650 4 $a Geology. $3 516570
650 4 $a Hydrologic sciences. $3 3168407
650 4 $a Geophysics. $3 535228
650 4 $a Mathematics. $3 515831
650 4 $a Southeast Asian studies. $3 3344898
650 4 $a Water resources management. $3 794747
655 7 $a Electronic books. $2 lcsh $3 542853
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690 $a 0800
690 $a 0373
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690 $a 0405
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710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a University of Malaya (Malaysia). $3 3700764
773 0 $t Dissertations Abstracts International $g 85-02A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30595379 $z click for full text (PQDT)