Faculty of Science >
PhD Theses : Science >
Please use this identifier to cite or link to this item:
|Title: ||Water quality data analysis and modeling of the Langat river basin|
|Authors: ||Hafizan Hj Juahir|
|Keywords: ||Water quality|
Artificial Neural Network
|Issue Date: ||Jun-2009 |
|Publisher: ||University Malaya|
|Abstract: ||This thesis concerns the investigation of spatial and temporal water quality pattern and the development of artificial neural network (ANN) prediction models. These are based on secondary data on water quality, hydrological and meteorological variables, land use variables and landscape metrics along the main Langat River. In this work three different tools, namely envirometrics, GIS and non-parametric test of trend were integrated to investigate the changes in river water quality and land use based on seasonal and spatial affects and their relationship with each other. The new landscape metrics were developed using patch analysis to represent the cumulative effects of pollution loading due to land use changes from upstream to downstream of the Langat River Basin. Finally the development of ANN prediction models was carried out based on the results obtained by the analyses mentioned above.23 water quality data collected from seven monitoring stations manned by DOE from 1988 to 2002 were used in this study. Land use analyses based on five land use types, namely, agriculture, forest, urban, waterbody and others as well as LUCI, were developed using land use maps of years 1974, 1981, 1984, 1988, 1990, 1991, 1995, 1996, 1997, and 2001. Various land use analyses were carried out for the 1, 2 and 3 km buffer areas as well as the whole Langat Basin area.
Hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA), multiple linear regression and
ANN were applied to study the spatial and temporal variations of the most significant
water quality variables and to investigate the origin of pollution sources. Three sub basin clusters were formed based on water quality parameter analysis using HACA. These
clusters are designated as high pollution source (HPS), moderate pollution source (MPS)
and low pollution source (LPS) regions respectively. Significant water quality parameters that contribute to the clustering of the regions were also consequently determined using DA. Temporally, ten water quality variables, namely, temperature (T), dissolved oxygen (DO), pH, conductivity (Cond.), salinity (SAL), total solids (TS), chlorine (Cl), potassium (K), magnesium (Mg) and E.coli were successfully discriminated using DA based on seasonal (wet and dry) variations. In order to ascertain and qualitatively describe contributors to the pollution of the river, PCA and FA (varimax functionality) were used. Seven principal components (PCs) were obtained with 81% total variance for the high pollution source (HPS) region, while six PCs with 71% and 79% total variance were obtained for moderate pollution source (MPS) and low pollution source (LPS) regions respectively. The pollution sources for the HPS and MPS are of anthropogenic origins (industrial, municipal waste and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the identified main sources of pollution.
Based on the analyses carried out on land use of the Langat Basin and water quality
parameters of Langat River, input-output relationships were established for the
development of ANN prediction models. Five different types of prediction models based
on different input and output parameters were developed to predict (i) river class (two models), (ii) water quality parameters, (iii) pollution region, and (iv) land use.
Encouraging prediction results were obtained using the ANN models with acceptable accuracies.
From this study we can conclude that the application of the various envirometric and
statistical methods on the Langat river basin data is able to reveal meaningful
information on the temporal and spatial variability of land use and surface water pollution of this large and complex river system. Development of ANN models based on the data also yields useful models that can be employed as decision tools for policy makers in planning for more effective and sustainable land development policies and water quality monitoring programs.|
|Description: ||Thesis (PhD) -- Faculty of Science, University of Malaya, 2009.|
|Appears in Collections:||PhD Theses : Science|