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In this chapter, we showed a method for measuring of the air quality index by using the CCD/CMOS sensor. We showed two examples to obtain index values by using webcam and CCTV. Both devices provided a high correlation between the measured and estimated PM10. So, the imaging method is capable to measure PM10 values in the environment. Futher application can be conducted using different devices.

The sea surface temperature (SST) algorithm was only valid for cloud free water pixels. The cloudy pixels should be separated before the SST algorithm could be applied. The cloud masking algorithm was used to separate the cloudy pixels from non-cloudy pixels. The cloud surface, ocean surface and vegetated, arid or snow covered land surfaces have different response to reflectance, brightness temperature and emissivity. The cloud detection or masking tests were based on the different response patterns of the earth surfaces or clouds to the reflection or emission of the wave radiation. The threshold values were different for the different seasonal and regional areas. Therefore the threshold values for each test would be determined before cloud masking test were performed. Krieble(1989) had proposed a procedure to derive suitable temperature thresholds for new areas of application. The land and sea areas which seen likely to be the coldest but cloud free were identified visually by users. However this method is subjective and quite time consuming. The results were varying with the users. Sauders (1986) had determined the threshold for local uniformity test with SD value less than 0.2 K for cloud free pixels over the sea in Northeastern Europe. France and Cracknell (1994, 1995) found SD values less than 0.4 K for cloud free pixels over the sea in northeastern Brazil. In this study, histograms of the cloud over land, cloud free land, cloud over sea, cloud free sea areas would be utilized. It was different with the suggested method that utilized whole sea area. The histogram was expected to be bimodal, a clear separation between the digital number for the colder clouds and the warmer sea surface (Cracknell, 1997). However, in practical, it was difficult to get the clear bimodal histogram for whole sea area. Therefore the new method generated the four histograms by using the ROI tool of software Envi V.4.4 to select the four separate areas.

Three-dimensional (3-D) resistivity surveys usually use a rectangular grid of electrodes to accurately resolve the subsurface structures in areas with very complex geology. However, in some survey areas such as heavily urbanized areas, it is not possible to use a normal grid of electrodes due to physical obstructions such as constructions, buildings, or other types of obstacle. The only practical arrangement is to use electrodes confined to the perimeter of the survey area. Many approaches have been proposed to investigate subsurface features for confined areas, such as the “Baker” and “L and Corner” arrays. These techniques normally use heuristic rules and are designed for perimeters with sharp corners such as rectangles, but might not be applicable for perimeters with smooth shapes such as a circle. New techniques that automatically select arrays that maximize the model resolution can be adapted to select optimum arrays for perimeters of any shape. We study the effectiveness of two sets of optimized perimeter arrays generated based on a modified “Compare R” (CR) method. The performance of these optimized perimeter arrays is compared with the standard “L and Corner” arrays. This is demonstrated by using two synthetic examples and one field survey dataset. In the synthetic models, the results show that, when using both the optimized and standard arrays, the vertical resolution is poorer than the horizontal resolution. However, the optimized perimeter arrays produce better resolution and structure detectability than “L and Corner” arrays. In addition, there is a slight improvement with the noise-weighted optimized dataset, which shows slightly higher resistivity contrasts and the lowest data misfits.

Flood can be assessed through flood vulnerability, risk, and susceptibility analysis using remote sensing, geographic information system, and hydrological modelling. In this chapter, different stages, complexities, and processes of flood vulnerability, risk, and susceptibility assessment were discussed. The study reveals that flood vulnerability should be assessed based on four aspects: physical, social, economic, and environmental. Flood risk should be assessed by three stages: risk analysis, disaster relief, and preparedness, whereas flood susceptibility assessment involves three processes. Overall, it was found that the responsible factors vary as per the local conditions, which need to be carefully analyzed and selected. Furthermore, the role of remote sensing and geographic information system in flood risk management were found important especially in flood risk mapping and in the selection of responsible flooding factors.

Remote sensing and geographic information system techniques were used for prioritization of flood vulnerability zones based on integrated parameters. Estimation of various linear and shape morphometric parameters, spatial and temporal land use and land cover changes (2005–2015), soil loss and sediment yield were performed for each river basin and utilized for the flood vulnerability zones. Further, ranks were assigned on the basis of changes in the parameters' value in areas/distribution of areas. To prioritize, five categories were classified: ‘very high’, ‘high’, ‘medium’, ‘low’ and ‘very low’ in terms of priority. The final prioritization of the integrated analysis revealed that 16 and 5 river basins fall under high and very high priority respectively. It is concluded that these river basins will be selected to conduct hydrological modelling for flood analysis. Validation results support the presented methodology and showed that in the 2014 Kelantan flood, the water level rose drastically in high and very high priority river basins situated in midstream. © 2018 John Wiley & Sons, Ltd.

The optimized ‘Compare R’ (CR) method is considered one of the best algorithms to automatically generate a set of electrode arrays that maximizes the model resolution for electrical resistivity imaging. In its original version, the calculation time to generate a set of arrays, even for a short resistivity line, required several hours. Recently, researchers spent considerable effort to reduce execution time. In addition, other modifications have been made to improve the practical aspects, such as minimizing the electrode polarization effects and incorporating errors in the data. In line with this approach, the effectiveness of the optimized arrays that are generated based on a modified version of ‘Compare-R' optimization algorithm is demonstrated through a comprehensive and quantitative comparison with the conventional Dipole-Dipole (DD) and Wenner-Schlumberger (WS) arrays. Four different synthetic models were generated and noisy data cases were considered. The results show that the optimized array has significantly better resolution and target detail than conventional arrays. This is clear in enhancing the detectability accuracy in the deepest part of the inversion models. Of the conventional arrays, the DD array provides inversion images mostly closely related to the performance of the optimized array models.

The conventional Euler deconvolution has 5 unknown parameters to be solve which are the location of source (x0, y0, and z0), the background field and the structural index (SI). Among these 5 unknowns, the SI is to be manually selected by the interpreter. The manual input of SI into the Euler equation makes the technique to be semi-automated. A new technique based on Euler deconvolution that estimate background, horizontal coordinate (x0 and y0), depth and structural index (SI) of gridded magnetic data is presented. The theoretical and field model study over magnetic sources demonstrates the ability of the method to solve for the source location and nature of the target, the technique does not depend on magnetic latitude. An integrated automated filter which is based on convolution window, SI deviation, regression error and analytic signal is prepared and used for selecting valid solution. Finally, the technique is applied to real magnetic data of Sebarang Jaya, the clustered depth solutions coincided with the high amplitude/values of analytic signal and these are the possible positions of the target being sought. The technique is fast means of magnetic data interpretation and easy to implement as it involves first order derivatives of the field.

Precipitable water vapor (PWV) is a highly variable, but important greenhouse gas that regulates the radiation budget of the earth. Its variability in time and space makes it difficult to quantify. Knowledge of its vertical distribution, in particular, is crucial for many reasons. In this study, empirical relationships between isobaric layers of PWV over Peninsular Malaysia are examined. Analysis of variance (ANOVA) technique on Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) observations, from 2005 to 2011, has been used to propose a relationship of the form, W=α(WL)β for the middle (MW) and upper (UW) layers PWV. W is either MW or UW with α and β as regression coefficients, which are functions of latitude. Coefficients of determination (R2) and root mean square error (RMSE) of respective values between 0.75–0.86 and 1.65–2.38 mm, across the zones, were obtained for both the MW and UW predictions, with a mean bias (MB) below ±1 mm.The predicted and observed PWV presented a better agreement northerly. Initial predictability test for each model was done on two independent data sets: ATOVS (2012–2015), and radiosonde (2010–2011) at Penang, Kuantan and Sepang stations, with very good outcomes. The results of the tests revealed remarkable performances, when compared with two previously reported models. The inclusion of variable regression coefficients, and the utilization of satellite-derived data, which provide soundings of data-void regions between radiosonde networks, proved to have optimized the results.

The study was conducted to develop a rainfall erosivity model for tropical climates to estimate daily rainfall erosivity and determine the most effective power law relationship between rainfall erosivity and daily precipitation. Thirty minute resolution rainfall data recorded in 55 stations of a state in Peninsular Malaysia were analysed. Using three precipitation limits of 0.1, 5.0 and 12.7 mm, the behaviour of rainfall on average annual rainfall erosivity gave ranges of 10,264–54,284, 8,151.5–48,301 and 4,958–39,938 MJ mm ha−1 h−1 y−1 respectively. It was found that the number of individual events, occurring within a day, were more in the area compared other types of events for all precipitation limits (0.1, 0.5 and 12.0 mm). Spatio-temporal variation of monthly coefficient values of power law relationship was found with the highest R2 in the range of 0.93–0.94 for 0.1 mm precipitation limit. Out of 55 stations, 15 were selected for model development and assessment. On the basis of importance of smaller events, a 0.1 mm precipitation limit was selected for the proposed model. The proposed model was found good for monthly and annual rainfall erosivity estimation apart from few limitations. Furthermore, the validity of the proposed model was checked for different parts of the area.

Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality parameters are important due to health impact. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. Seasonal aerosol optical depth (AOD) values at 550 nm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA’s Terra satellites, for the 10 years period of 2000 - 2010 were used to test 7 different spatial interpolation methods in the present study. The accuracy of estimations was assessed through visual analysis as well as independent validation based on basic statistics, such as root mean square error (RMSE) and correlation coefficient. Based on the RMSE and R values of predictions made using measured values from 2000 to 2010, Radial Basis Functions (RBFs) yielded the best results for spring, summer and winter and ordinary kriging yielded the best results for fall.

We have investigated the average P-wave travel-time residuals for some stations around Southern Thailand, Peninsular Malaysia and Singapore at regional distances. Six years (January, 2010–December, 2015) record of events from central and northern Sumatra was obtained from the digital seismic archives of Integrated Research Institute for Seismology (IRIS). The criteria used for the data selection are designed to be above the magnitude of mb 4.5, depth less than 200 km and an epicentral distance shorter than 1000 km. Within this window a total number of 152 earthquakes were obtained. Furthermore, data were filtered based on the clarity of the seismic phases that are manually picked. A total of 1088 P-wave arrivals and 962 S-wave arrivals were hand-picked from 10 seismic stations around the Peninsula. Three stations IPM, KUM, and KOM from Peninsular Malaysia, four stations BTDF, NTU, BESC and KAPK from Singapore and three stations SURA, SRIT and SKLT located in the southern part of Thailand are used. Station NTU was chosen as the Ref. station because it recorded the large number of events. Travel-times were calculated using three 1-D models (Preliminary Ref. Earth Model PREM (Dziewonski and Anderson, 1981, IASP91, and Lienert et al., 1986) and an adopted two-point ray tracing algorithm. For the three models, we corroborate our calculated travel-times with the results from the use of TAUP travel-time calculation software. Relative to station NTU, our results show that the average P wave travel-time residual for PREM model ranges from −0.16 to 0.45 s for BESC and IPM respectively. For IASP91 model, the average residual ranges from −0.25 to 0.24 s for SRIT and SKLT respectively, and ranges from −0.22 to 0.30 s for KAPK and IPM respectively for Lienert et al. (1986) model. Generally, most stations have slightly positive residuals relative to station NTU. These corrections reflect the difference between actual and estimated model velocities along ray paths to stations and can compensate for heterogeneous velocity structure near individual stations. The computed average travel-time residuals can reduce errors attributable to station correction in the inversion of hypocentral parameters around the Peninsula. Due to the heterogeneity occasioned by the numerous fault systems, a better 1-D velocity model for the Peninsula is desired for more reliable hypocentral inversion and other seismic investigations.

Objective: To propose an integrated approach for flood analyses in order to understand the causes and effects of flooding, and to achieve better measurements in flood risk predictions and mitigation. Method: The role of remote sensing, hydrology and geophysical methods in determining and selection of input and model parameters in hydrological modeling for flood analyses were discussed. Findings: Remote sensing and GIS are found to be important in any preliminary flood analysis while hydrological parameters such as precipitation, infiltration, evapotranspiration and runoff and their factors such as meteorological characteristics, land use land cover changes, types of vegetation and soil properties play a crucial role for accurate hydrological modelling. In hydrogeological investigation, geophysical methods such as Transient Electromagnetic and Resistivity methods are found best for the determination of subsurface hydrological factors such as hydraulic conductivity, porosity and permeability. Improvement: It is found that the combination of these methods can furnish precise flood analyses through modeling techniques.

Currently two algorithms are being used routinely by the MODIS Atmosphere and Ocean Team in order to distinguish sediment influence and clear water pixels over turbid water area. These two algorithms require complicated computational analyses. In this paper, a simple algorithm based on empirical technique to detect the sediment-influenced pixels over coastal waters is proposed as an alternative to these two algorithms. This study used apparent reflectance acquired from MODIS L1B product. This algorithm is based on the gradient difference of the line connecting the 0.47- and 1.24-μm channels and 0.47- and 0.66-μm channels of a log-log graph of the apparent reflectance values against MODIS wavelengths. Over clear-water areas (deep blue sea), the 0.47-, 0.66- and 1.24-μm channels fitted very well in line with correlation R > 0.99. Over turbid waters, a substantial increase of 0.66 μm in the reflectance leads to a low correlation value. By computing the difference between the gradient of the line connecting 0.47 and 0.66 μm and the gradient of the line connecting 0.47 and 1.24 μm, the threshold to discriminate turbid and shallow coastal waters from clear-water pixels can be obtained. If the gradient difference is greater than 0, the pixels were then marked as sediment-influenced pixels. This proposed algorithm works well for MODIS Terra and Aqua sensor. The comparison of this algorithm with an established algorithm also showed a good agreement.

The integration of Aeromagnetic data and remotely sensed imagery with the intents of mapping the subsurface geological structures in part of the South-western basement complex of Nigeria was developed using the PCI Geomatica Software. 2013. The data obtained from the Nigerian Geological Survey Agency; was corrected using Regional Residual Separation of the Total Magnetic field anomalies enhanced, and International Geomagnetic Reference Field removed. The principal objective of this study is, therefore, to introduce a rapid and efficient method of subsurface structural depth estimate and structural index evaluation through the incorporation of the Euler Deconvolution technique into PCI Geomatica 2013 to prospect for subsurface geological structures. The shape and depth of burial helped to define these structures from the regional aeromagnetic map. The method enabled various structural indices to be automatically delineated for an index of between 0.5 SI and 3.0 SI at a maximum depth of 1.1 km that clearly showed the best depths estimate for all the structural indices. The results delineate two major magnetic belts in the area; the first belt shows an elongated ridge-like structure trending mostly along the NorthNortheast-SouthSouthwest and the other anomalies trends primarily in the Northeast, Northwest, Northeast-Southwest parts of the study area that could be attributed to basement complex granitic intrusions from the tectonic history of the area. The majority of the second structures showed various linear structures different from the first structure. Basically, a significant offset was delineated at the core segment of the study area, suggesting a major subsurface geological feature that controls mineralisation in this area.

Topographic and climatic changes are the main causes of abrupt flooding in tropical areas. It is the need to find out exact causes and effects of these changes. Numerical modeling techniques plays a vital role for such studies due to their use of hydrological parameters which are strongly linked with topographic changes. In this review, some of the widely used models utilizing hydrological and river modeling parameters and their estimation in data sparse region are discussed. Shortcomings of 1D and 2D numerical models and the possible improvements over these models through 3D modeling are also discussed. It is found that the HEC-RAS and FLO 2D model are best in terms of economical and accurate flood analysis for river and floodplain modeling respectively. Limitations of FLO 2D in floodplain modeling mainly such as floodplain elevation differences and its vertical roughness in grids were found which can be improve through 3D model. Therefore, 3D model was found to be more suitable than 1D and 2D models in terms of vertical accuracy in grid cells. It was also found that 3D models for open channel flows already developed recently but not for floodplain. Hence, it was suggested that a 3D model for floodplain should be developed by considering all hydrological and high resolution topographic parameter's models, discussed in this review, to enhance the findings of causes and effects of flooding.

The use of a single geophysical method is prone to more ambiguity and loss of vital geological information at geophysical interpretation stage. To circumvent this problem, a statistical approach and image processing technique were used to combine three electrode arrays (dipole-dipole, pole-dipole, and Werner-Schlumberger). The combined models were tested using both synthetic and field models. For synthetic models, the mean absolute error (MAE), mean absolute percentage error (MAPE) and mean resistivity values were used as criteria to assess the combined models. For field data, the three protocols were combined and the models were compared with borehole data. Considering the overall performance, maximum value model has a low error and correlated well with the borehole data than the other combined models analyzed although the performance of these models depends on resistivity in addition to the nature of geologic structure under investigation. The correlation between resistivity and lithological changes with depth (borehole data) used in this research provided a more quantitative comparison approach than the ones available in the literature. The image processing technique served as an aid to make post inversion analysis such as combining data sets and quantitative comparison among/between the models.

Total suspended sediment (TSS) plays a significant role in the environment. Many researchers show that TSS has a high correlation with the red portion of the visible light spectrum. The correlation is highly dependent on geography of the study area. The aim of this study was to develop specific algorithms utilizing corrected MODIS Terra 250-m surface reflectance (Rrs) product (MOD09) to map TSS over the Penang coastal area. Field measurements of TSS were performed during two cruise trips that were conducted on 8 December 2008 and 29 January 2010 over the Penang coastal area. The relationship between TSS and the surface reflectance of MOD09 was analysed using regression analysis. The developed algorithm showed that Rrs are highly correlated with the in-situ TSS with R2 is 0.838. The result shows that the Rrs product could be used to estimate TSS over the Penang area.

The uncertainty in the quantification of aerosol properties such as concentration, size, and composition, spatially and temporally makes regional studies important. Therefore, this study presents seasonal variations of aerosol optical properties over Ilorin (8&#17632'N, 4&#17634'E), Nigeria. Long-term (1998-2013) records of aerosol optical depth (AOD) and angstrom exponent α, from ground-based Aerosol Robotic Network (AERONET) are used to study the seasonal variability, characteristics and types of aerosol. The study showed that seasonal variations (Harmattan and Summer) result in different aerosol concentration, characteristics, and types. The magnitude and sensitivity of AOD to wavelength are found low in Summer with significant increase during Saharan dust transport season (Harmattan). The average mean AODs are 0.73 ± 0.50, 0.97 ± 0.52 and 0.46 ± 0.29 with corresponding mean angstrom of 0.66 ± 0.36, 0.68 ± 0.34, and 0.64 ± 0.37 for the entire period, Harmattan and Summer seasons. High frequency of occurrence of angstrom exponent below 1 (78% and 81%) which were observed during Harmattan and Summer indicates that the particles are generally coarse in mode. The results revealed that for both Harmattan and Summer seasons, the dominant aerosol was dust (DA) with frequency of occurrence of 82% and 79%. However, mixed aerosol (MIXA) (14.4%) is the second dominant case during Harmattan while in Summer maritime aerosol (MA) (9.1%) associated with transport due to southwesterly trade wind is the second dominant aerosol. This conclusion is supported by size distribution data for the study site which showed that large volume of aerosol particle size are enclosed in largely coarse mode range in all seasons. A 7-day back trajectory seasonal frequency plot sourced from the Hysplit Single Particles Lagrangian Integrated Trajectory model (Hysplit_4 model) shows that dust are transported from the Sahara during north-easterly trade wind flow while the observed marine aerosols are conveyed by the southwesterly trade wind influences to the study site.

The objective of this work is to analyze the temporal and spatial variability of the monthly mean aerosol index (AI) obtained from the Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) in comparison with the available ground observations in Nigeria during 1984-2013. It also aims at developing a regression model to allow the estimation of the values of AI in Nigeria based on the data from ground observations. TOMS and OMI data are considered and treated separately to provide continuity and consistency in the long-term data observations, together with the meteorological variable such as wind speed, visibility, air temperature and relative humidity that can be used to characterize the dust activity in Nigeria. The results revealed a strong seasonal pattern of the monthly distribution and variability of absorbing aerosols along a north to south gradient. The monthly mean AI showed higher values during the dry months (Harmattan) and lower values during the wet months (Summer) in all zones. From December to February, higher AI values are observed in the southern region, decreasing progressively towards the north, while during March-October, the opposite pattern is observed. The AI showed clear maximum values of 2.06, 1.93, and 1.87 (TOMS) and 2.32, 2.27 and 2.24 (OMI) in the month of January and minimum values in September over the north-central, southern and coastal zones, while showing maximum values of 1.76 (TOMS) and 2.10 (OMI) during March in the Sahel. New empirical algorithms for predicting missing AI data were proposed using TOMS data and multiple linear regression, and the model co-efficient was determined. The generated coefficients were applied to another dataset for cross-validation. The accuracy of the model was determined using the coefficient of determination R2 and the root mean square error (RMSE) calculated at the 95% confidence level. The AI values for the missing years were retrieved, plotted and compared with the measured monthly AI cycle. It is concluded that the meteorological variables can significantly explain the AI variability and can be used efficiently to predict the missing AI data.

The conventional Euler deconvolution methodology requires the user to choose appropriate structural index (SI) in order to compute the source parameters, and this makes the operation tedious and time consuming. To solve the mentioned problem, a method based on Euler’s homogeneity relation for full automation of magnetic data interpretation is presented in this paper. The technique uses multiple linear regression (MLR) methodology to estimate background, horizontal coordinate (x0 and y0), depth and structural index (SI) simultaneously is prepared and used for this task. The technique involves the use of first-order derivatives, independent of analytic signal (AS) and the derivatives are computed directly from the total field grid. It is fast means of magnetic data interpretation and easy to implement.

A study using the subsurface structural patterns technique was carried out to locate, classify and outline the magnetic anomaly source rocks underlying the Omu-Aran Schist belt zone together with determination of the depth of these various source rocks within the study area for sustainability. The processes were carried out through a series of E-W and N-S geomagnetic cross-sections of 58.70 by 56.8 km spread across the study area in part of the Nigerian South-western Precambrian Basement Complex. The primary objective of this study was to use an uncomplicated technique to locate these magnetic anomalies and identify the mineralization potential in the area. The subsurface geological structural patterns in this area were adequately mapped using the Oasis Montaj 2014 source parameter imaging function integrated with Surfer 10 and euldph-x algorithms that automatically compute the depths of the various magnetic anomaly sources using aeromagnetic data acquired from the Nigerian Geological Survey Agency. These anomaly sources as delineated, consist of arrays of north-north-east and south-south-west influencing the underground structures that control the surface geologic characteristics which determine the mineral potential in parts of the north-east, the north-west and the south-western zones of the study area with very low and high values of total magnetic intensity. The large dome-shaped structure that overlies a buried block of inferred meta-sedimentary rocks in this area suggests that subsurface mineral distributions in this area may perhaps be influenced by its location in the highly mineralized Nigerian South-western Basement Complex together with the rich mafic and ultramafic granitic-migmatite Basement Terrain that also underlies the area.

Riverbank filtration (RBF) is a natural water pre – treatment, safer and cost – effective that uses geology instead of chemicals to pre – treat river water. The process occur during the infiltration process of river water passing through riverbank material towards a production well during a pumping test, where the removal of biological and colloids take place. Abstraction of ground water are required for RBF to occur, hence pumping test were done. As pumping test took place, ground water was in continuous discharging and recharging process throughout time. Objective of this research is to study the dynamic changes of ground water during discharging and recharging process for 72 hours by using time – lapse electrical resistivity imaging (TLERI). Resistivity, which uses current to measure the resistivity of subsurface was used during the pumping test to monitor ground water changes and infiltration of river water during RBF process. Results show significant changes on TLERI data from the first session until the last session. Dynamic changes occurred around the riverbank and production well section while others show less significant changes.

Aerosols are among the most interesting topics investigated by researchers because of their complicated characteristics and poor quantification. Moreover, significant uncertainties are associated with changes in the Earth's radiation budget. Previous studies have shown numerous difficulties and challenges in quantifying aerosol influences. In addition, the heterogeneity from aerosol loading and properties, including spatial, temporal, size, and composition features, presents a challenge. In this study, we investigated aerosol characteristics over two regions with different environmental conditions and aerosol sources. The study sites are Penang and Kuching in Malaysia, where a ground–based AErosol RObotic NETwork (AERONET) sun–photometer was deployed. The types of aerosol, such as biomass burning, urban/industrial, marine, and dust aerosols, for both study sites were identified by analyzing aerosol optical depth and angstrom exponent. Seasonal monsoon variation results in different aerosol optical properties, characteristics, and types of aerosols that are dominant in Penang and Kuching. Seasonal monsoon flow trend patterns from a seven–day back–trajectory frequency plotted by the Hybrid Single–Particle Lagrangian Integrated Trajectory model illustrated the distinct origins of trans–boundary aerosol sources. Finally, we improved our findings in Malaysian sites using the AERONET data from Singapore and Indonesia. Similarities in the optical properties of aerosols and the distribution types (referred to as homogeneous aerosol) were observed in the Penang–Singapore and the Kuching–Pontianak sites. The dominant aerosol distribution types were completely different for locations in the western (Penang–Singapore) and eastern (Kuching–Pontianak) parts of the South China Sea. This is a result of spatial and temporal heterogeneity. The spatial and temporal heterogeneities for the western and eastern portions of South China Sea provide information on the natural or anthropogenic processes that take place.

Obtaining continuous aerosol-optical-depth (AOD) measurements is a difficult task due to the cloud-cover problem. With the main motivation of overcoming this problem, an AOD-predicting model is proposed. In this study, the optical properties of aerosols in Penang, Malaysia were analyzed for four monsoonal seasons (northeast monsoon, pre-monsoon, southwest monsoon, and post-monsoon) based on data from the AErosol RObotic NETwork (AERONET) from February 2012 to November 2013. The aerosol distribution patterns in Penang for each monsoonal period were quantitatively identified according to the scattering plots of the Ångström exponent against the AOD. A new empirical algorithm was proposed to predict the AOD data. Ground-based measurements (i.e., visibility and air pollutant index) were used in the model as predictor data to retrieve the missing AOD data from AERONET due to frequent cloud formation in the equatorial region. The model coefficients were determined through multiple regression analysis using selected data set from in situ data. The calibrated model coefficients have a coefficient of determination, R2, of 0.72. The predicted AOD of the model was generated based on these calibrated coefficients and compared against the measured data through standard statistical tests, yielding a R2 of 0.68 as validation accuracy. The error in weighted mean absolute percentage error (wMAPE) was less than 0.40% compared with the real data. The results revealed that the proposed model efficiently predicted the AOD data. Performance of our model was compared against selected LIDAR data to yield good correspondence. The predicted AOD can enhance measured short- and long-term AOD and provide supplementary information for climatological studies and monitoring aerosol variation.

Gulf of Martaban is located at the north of Andaman, and is one of the world most turbid areas. The presence of suspended sediment concentration (SSC) in the water body could reduce the underwater transmittance. This study has been conducted to investigate the variation of SSC over the Gulf of Martaban. Remote sensing reflectance (Rrs) of 667 nm is used as a proxy to represent the sediment SSC variation over the study area. The data for the period of July 2002 to March 2014 acquired from MODIS Aqua 4 km resolution are used in this study. As a result, there is no obvious yearly variation in the SSC cover area. The SSC variation over this study area is found to be seasonal. High homogenous SSC covers area observably during the northeast (NE) monsoon season that occurs from December to January. The sediment cover area could reach the latitude of 15°N that located at the south of the gulf. During southwest (SW) monsoon season that occurs from May to September, low and sparse SSC cover area is observed. As a consequence, the area covered by the SSC is higher during the NE monsoon season as compared to the SW monsoon season. Hence, the SSC cover area during the NE monsoon season is greater than the yearly averaged SSC cover area. Meanwhile the SSC cover area during the rainy SW monsoon season is less than the yearly and NE monsoon season.

This article describes the use of k-means clustering, an unsupervised image classification technique, to help interpret subsurface targets. The k-means algorithm is employed to combine and classify the two-dimensional (2D) inverse resistivity models obtained from three different electrode arrays. The algorithm is initialized through the selection of the number of clusters, number of iterations and other parameters such as stopping criteria. Automatically, it seeks to find groups of closely related resistivity values that belong to the same cluster and are more similar to each other than resistivity values belonging to other clusters. The approach is applied to both synthetic and field data. The 2D postinversions of the resistivity data were preprocessed by resampling and interpolating to the same coordinate. Following the preprocessing, the three images are combined into a single classified image. All the image preprocessing, manipulation and analysis are performed using the PCI Geomatics software package. The results of the clustering and classification are presented as classified images. An assessment of the performance of the individual and combined images for the synthetic models is carried out using an error matrix, mean absolute error and mean absolute percent error. The estimated errors show that images obtained from maximum values of the reconstructed resistivity for the different models give the best representation of the true models. Additionally, the overall accuracy and kappa values show good agreement between the combined classified images and true models. Depending on the model, the overall accuracy ranges from 86 to 99 %, while the kappa coefficient is in the range of 54–98 %. Classified images with kappa coefficients greater than 0.8 show strong agreement, while images with kappa coefficients greater than 0.5 but less than 0.8 give moderate agreement. For the field data, the k-mean classifier produces images that incorporate structural features of the three electrode array configurations. Consequently, some clusters that overwhelmingly correspond to the lithologic units of the investigated areas are better identified than the tomographic images of each data set considered separately, underscoring the relevance of the unsupervised classification technique in this study.

A prediction at a regional scale of groundwater productivity potential mapping in an area is subjected to uncertainties that must be efficiently managed for enhancing decision making. This study explored the potential of a GIS-based Dempster–Shafer theory (DST) model as a spatial prediction model to offer solution to this problem. Seven criteria/factors regarded as positive indicators to the existence of promising groundwater reservoir in a given study area were selected and weighted in a probability-based DST approach to compute degrees of belief functions component indexes. The results of the computed belief function indexes values were processed in GIS environment to generate belief functions maps among which the uncertainty index map established uncertainty result of relatively low range of