Mohammad Firuz Ramli

A knowledge-driven GIS modeling technique for groundwater potential mapping at the Upper Langat Basin, Malaysia

The aim of this paper is to use a knowledge-driven expert-based geographical information system (GIS) model coupling with remote-sensing-derived parameters for groundwater potential mapping in an area of the Upper Langat Basin, Malaysia. In this study, nine groundwater storage controlling parameters that affect groundwater occurrences are derived from remotely sensed imagery, available maps, and associated databases. Those parameters are: lithology, slope, lineament, land use, soil, rainfall, drainage density, elevation, and geomorphology. Then the parameter layers were integrated and modeled using a knowledge-driven GIS of weighted linear combination. The weightage and score for each parameter and their classes are based on the Malaysian groundwater expert opinion survey. The predicted groundwater potential map was classified into four distinct zones based on the classification scheme designed by Department of Minerals and Geoscience Malaysia (JMG). The results showed that about 17% of the study area falls under low-potential zone, with 66% on moderate-potential zone, 15% with high-potential zone, and only 0.45% falls under very-high-potential zone. The results obtained in this study were validated with the groundwater borehole wells data compiled by the JMG and showed 76% of prediction accuracy. In addition statistical analysis indicated that hard rock dominant of the study area is controlled by secondary porosity such as distance from lineament and density of lineament. There are high correlations between area percentage of predicted groundwater potential zones and groundwater well yield. Results obtained from this study can be useful for future planning of groundwater exploration, planning and development by related agencies in Malaysia which provide a rapid method and reduce cost as well as less time consuming. The results may be also transferable to other areas of similar hydrological characteristics.

Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors.

This article describes design and application of feed-forward, fully-connected, three-layer perceptron neural network model for computing the water quality index (WQI)(1) for Kinta River (Malaysia). The modeling efforts showed that the optimal network architecture was 23-34-1 and that the best WQI predictions were associated with the quick propagation (QP) training algorithm; a learning rate of 0.06; and a QP coefficient of 1.75. The WQI predictions of this model had significant, positive, very high correlation (r=0.977, p<0.01) with the measured WQI values, implying that the model predictions explain around 95.4% of the variation in the measured WQI values. The approach presented in this article offers useful and powerful alternative to WQI computation and prediction, especially in the case of WQI calculation methods which involve lengthy computations and use of various sub-index formulae for each value, or range of values, of the constituent water quality variables.

Characterization of spatial patterns in river water quality using chemometric pattern recognition techniques.

This study employed three chemometric data mining techniques (factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA)) to identify the latent structure of a water quality (WQ) dataset pertaining to Kinta River (Malaysia) and to classify eight WQ monitoring stations along the river into groups of similar WQ characteristics. FA identified the WQ parameters responsible for variations in Kinta Rivers WQ and accentuated the roles of weathering and surface runoff in determining the rivers WQ. CA grouped the monitoring locations into a cluster of low levels of water pollution (the two uppermost monitoring stations) and another of relatively high levels of river pollution (the mid-, and down-stream stations). DA confirmed these clusters and produced a discriminant function which can predict the cluster membership of new and/or unknown samples. These chemometric techniques highlight the potential for reasonably reducing the number of WQVs and monitoring stations for long-term monitoring purposes.

The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review

In recent years, several types of simulation and prediction models have been used within a GIS environment to determine a realistic future for urban growth patterns. These models include quantitative and spatio-temporal techniques that are implemented to monitor urban growth. The results derived through these techniques are used to create future policies that take into account sustainable development and the demands of future generations. The aim of this paper is to provide a basis for a literature review of urban Cellular Automata (CA) models to find the most suitable approach for a realistic simulation of land use changes. The general characteristics of simulation models of urban growth and urban CA models are described, and the different techniques used in the design of these models are classified. The strengths and weaknesses of the various models are identified based on the analysis and discussion of the characteristics of these models. The results of the review confirm that the CA model is one of the strongest models for simulating urban growth patterns owing to its structure, simplicity, and possibility of evolution. Limitations of the CA model, namely weaknesses in the quantitative aspect, and the inability to include the driving forces of urban growth in the simulation process, may be minimized by integrating it with other quantitative models, such as via the Analytic Hierarchy Process (AHP), Markov Chain and frequency ratio models. Realistic simulation can be achieved when socioeconomic factors and spatial and temporal dimensions are integrated in the simulation process.

Identification of the Hydrogeochemical Processes in Groundwater Using Classic Integrated Geochemical Methods and Geostatistical Techniques, in Amol-Babol Plain, Iran

Hydrogeochemical investigations had been carried out at the Amol-Babol Plain in the north of Iran. Geochemical processes and factors controlling the groundwater chemistry are identified based on the combination of classic geochemical methods with geographic information system (GIS) and geostatistical techniques. The results of the ionic ratios and Gibbs plots show that water rock interaction mechanisms, followed by cation exchange, and dissolution of carbonate and silicate minerals have influenced the groundwater chemistry in the study area. The hydrogeochemical characteristics of groundwater show a shift from low mineralized Ca-HCO3, Ca-Na-HCO3, and Ca-Cl water types to high mineralized Na-Cl water type. Three classes, namely, C1, C2, and C3, have been classified using cluster analysis. The spatial distribution maps of Na+/Cl−, Mg2+/Ca2+, and Cl−/HCO3 − ratios and electrical conductivity values indicate that the carbonate and weathering of silicate minerals played a significant role in the groundwater chemistry on the southern and western sides of the plain. However, salinization process had increased due to the influence of the evaporation-precipitation process towards the north-eastern side of the study area.

Evaluation of Factors Influencing the Groundwater Chemistry in a Small Tropical Island of Malaysia

Groun in a very complex way. In this work, multivariate statistical analysis was used to evaluate the factors controlling the groundwater chemistry of Kapas Island (Malaysia). Principal component analysis (P dwater chemistry of small tropical islands is influenced by many factors, such as recharge, weathering and seawater intrusion, among others, which interact with each other CA) was applied to 17 hydrochemical parameters from 108 groundwater samples obtained from 18 sampling sites. PCA extracted four PCs, namely seawater intrusion, redox reaction, anthropogenic pollution and weather factors, which collectively were responsible for more than 87% of the total variance of the island’s hydrochemistry. The cluster analysis indicated that three factors (weather, redox reaction and seawater intrusion) controlled the hydrochemistry of the area, and the variables were allocated to three groups based on similarity. A Piper diagram classified the island’s water types into Ca-HCO3 water type, Na-HCO3 water type, Na-SO4-Cl water type and Na-Cl water type, indicating recharge, mixed, weathering and leached from sewage and seawater intrusion, respectively. This work will provide policy makers and land managers with knowledge of the precise water quality problems affecting the island and can also serve as a guide for hydrochemistry assessments of other islands that share similar characteristics with the island in question.

Coupling of DEM and remote-sensing-based approaches for semi-automated detection of regional geostructural features in Zagros mountain, Iran

In recent years, remote-sensing data have increasingly been used for the interpretation of objects and mapping in various applications of engineering geology. Digital elevation model (DEM) is very useful for detection, delineation, and interpretation of geological and structural features. The use of image elements for interpretation is a common method to extract structural features. In this paper, linear features were extracted from the Landsat ETM satellite image and then DEM was used to enhance those objects using digital-image-processing filtering techniques. The extraction procedures of the linear objects are performed in a semi-automated way. Photographic elements and geotechnical elements are used as main keys to extract the information from the satellite image data. This paper emphasizes on the application of DEM and usage of various filtering techniques with different convolution kernel size applied on the DEM. Additionally, this paper discusses about the usefulness of DEM and satellite digital data for extraction of structural features in SW of Zagros mountain, Iran.

Spatial-temporal variation of surface water quality in the downstream region of the Jakara River, north-western Nigeria: A statistical approach

The pollution status of the downstream section of the Jakara River was investigated. Dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), suspended solids (SS), pH, conductivity, salinity, temperature, nitrogen in the form of ammonia (NH3), turbidity, dissolved solids (DS), total solids (TS), nitrates (NO3), chloride (Cl) and phosphates (PO3− 4) were evaluated, using both dry and wet season samples, as a measure of variation in surface water quality in the area. The results obtained from the analyses were correlated using Pearsons correlation matrix, principal component analysis (PCA) and paired sample t-tests. Positive correlations were observed for BOD5, NH3, COD, and SS, turbidity, conductivity, salinity, DS, TS for dry and wet seasons, respectively. PCA was used to investigate the origin of each water quality parameter, and yielded 5 varimax factors for each of dry and wet seasons, with 70.7 % and 83.1 % total variance, respectively. A paired sample t-test confirmed that the surface water quality varies significantly between dry and wet season samples (P < 0.01). The source of pollution in the area was concluded to be of anthropogenic origin in the dry season and natural origins in the wet season.

Accuracy assessment of moderate resolution image spectroradiometer products for dust storms in semiarid environment

Dust storms are strongly and negatively associated with the annual cycle of rainfall and coincide with the west and southwesterly winds in west and south west of Iran. Accuracy assessment of particulate matter products of moderate resolution image spectroradiometer was studied in this research. Moderate resolution image spectroradiometer products consist of aerosol optical thickness, its corresponding image red, green and blue and moderate resolution image spectroradiometer/ terra calibrated radiances 5 minutes L1B swath 1 km, which shows the environmental information at terrestrial, atmospheric and ocean phenomenology. Daily aerosol optical thickness data retrieved from moderate resolution image spectroradiometer from May 2009 to May 2010 were compared with the amount of particulate matter measured at ground in Sanandaj, Iran, using non-linear correlation coefficient. Results showed that the moderate resolution image spectroradiometer image / terra calibrated radiances 5 minutes L1B swath 1 km is able to detect dust storms distribution and their blowing direction over study area clearly. The air quality conditions obtained in with dust storm period were unhealthy and correlation coefficients between moderate resolution image spectroradiometer aerosol optical thickness and particulate matter concentration in this period were higher than without dust storm period. The moderate resolution image spectroradiometer aerosol optical thickness values lower than 0.1 were acquired uncertainty level. Comparison of moderate resolution image spectroradiometer images/ terra calibrated radiances 5 minutes L1B swath 1 km and image red, green and blue showed that moderate resolution image spectroradiometer has limitation in retrieval of aerosol optical thickness from the dust storm with high concentration of particulate matter. This study reveals that the algorithm which is applied to refine the aerosol optical thickness is not able to recognize the amount of particulate matter in low and very high concentrations sensitively. No study has previously been conducted to investigate the accuracy of the moderate resolution image spectroradiometer particulate matter products.

Groundwater irrigation quality mapping using geostatistical techniques in Amol–Babol Plain, Iran

Groundwater is acknowledged to be a reliable source for agricultural activities in arid and semi-arid regions. An assessment of the suitability of groundwater for agricultural usage was carried out on the Amol–Babol Plain, Iran, where agriculture is the dominant economic activity. Groundwater samples were collected from 154 wells during the wet and dry seasons in 2009. The sodium percentage, sodium adsorption ratio, residual sodium carbonate, magnesium hazard, and Kelly’s ratios were used as indicators for the water quality. Geostatistical technique of ordinary kriging method was used to create spatial distribution maps. The thematic maps of salinity hazard, sodium adsorption ratio, and sodium percentage indicated an increasing trend of concentration from the western and southern areas to the east and north-east of the plain. The maps also show that the groundwater quality decreases gradually from the west and south sides to the north-eastern side. There is also no significant change in seasonal variation of water quality parameters.