Application of Layer of Protection Analysis (LOPA) in Verification of Safety Integrity Level of Instrumented System

Incomplete process hazard analysis (PHA) and poor knowledge management have been two major reasons that have caused numerous lamentable disasters in the chemical process industry. To improve the safety integrity of a process system, all risk should be reduced to a tolerable limit. One way of doing it is by adding layers of protection which include inherent safer design, basic process control system (BPCS), alarms, SIS, physical protection, and emergency response procedure. These layers however, are cost to the process industry in term of implementing it as well as maintaining the quality of the layers. Therefore, understanding the required safety integrity level (SIL) of a process is essential in order to meet the tolerable risk target as well as to optimise the cost of a safety system. Meanwhile, layer of protection analysis (LOPA) is a simplified approach to verify SIL of a process system. Nevertheless, the method is relatively new and various modifications have take place by different entities. Therefore, there is a need to maintain the consistency of the LOPA result by adhering to a standard procedure and practise as well as clear direction provided by Centre of Chemical Process Safety (CCPS). This can be done easier by developing a framework for LOPA analyst to follows as well as tailored to the company background and history

The association between intellectual capital and financial performance in the Islamic banking industry: An analysis of the GCC banks

Purpose: The purpose of this study is to empirically investigate the impact of intellectual capital (IC) on the financial performance of Islamic banks operating in the Gulf Cooperation Council (GCC) countries. Design/methodology/approach: The study measures IC by the value added intellectual coefficient model. A regression analysis was used to assess the impact of IC on financial performance. The research sample consisted of Islamic banks operating in the GCC countries during the years 2011, 2012 and 2013. Data originated from the annual reports of Islamic banks. Findings: The results support the thesis that IC has a positive impact on the financial performance of Islamic banks. Even though the average IC is lower than that reported in other studies, the positive effect on financial performance is obvious. The findings also show that human capital (HC) is higher than capital employed (CE) and structural capital (SC). The study reveals that SC has an insignificant impact on the financial performance of the Islamic banks compared to CE and HC. Practical implications: The findings provide empirical evidence that IC affects the Islamic banks financial performance. It helps Islamic banks in the GCC countries to understand how to use their IC efficiently, especially SC as it is yet to be used efficiently. Also, the findings benefit the relevant authorities (e.g. legislators and central banks) who could use them to emphasise strategic policy reforms whenever required. Originality/value: The current research adds to the empirical studies in the GCC countries as it views the region as a collective as opposed to individual countries. It also extends the IC and performance measurement literature of Islamic banks in the GCC countries. Moreover, the current study enriches the limited literature on IC in the context of Islamic banking.The project was funded by the Kuwait Foundation for the Advancement of Sciences.Scopu

Application of Layer of Protection Analysis (LOPA) in Verification of Safety Integrity Level of Instrumented System

Incomplete process hazard analysis (PHA) and poor knowledge management have been two major reasons that have caused numerous lamentable disasters in the chemical process industry. To improve the safety integrity of a process system, all risk should be reduced to a tolerable limit. One way of doing it is by adding layers of protection which include inherent safer design, basic process control system (BPCS), alarms, SIS, physical protection, and emergency response procedure. These layers however, are cost to the process industry in term of implementing it as well as maintaining the quality of the layers. Therefore, understanding the required safety integrity level (SIL) of a process is essential in order to meet the tolerable risk target as well as to optimise the cost of a safety system. Meanwhile, layer of protection analysis (LOPA) is a simplified approach to verify SIL of a process system. Nevertheless, the method is relatively new and various modifications have take place by different entities. Therefore, there is a need to maintain the consistency of the LOPA result by adhering to a standard procedure and practise as well as clear direction provided by Centre of Chemical Process Safety (CCPS). This can be done easier by developing a framework for LOPA analyst to follows as well as tailored to the company background and history

Data fusion technique using wavelet transform and taguchi methods for automatic landslide detection from airborne laser scanning data and QuickBird satellite imagery

Landslide mapping is indispensable for efficient land use management and planning. Landslide inventory maps must be produced for various purposes, such as to record the landslide magnitude in an area and to examine the distribution, types, and forms of slope failures. The use of this information enables the study of landslide susceptibility, hazard, and risk, as well as of the evolution of landscapes affected by landslides. In tropical countries, precipitation during the monsoon season triggers hundreds of landslides in mountainous regions. The preparation of a landslide inventory in such regions is a challenging task because of rapid vegetation growth. Thus, enhancing the proficiency of landslide mapping using remote sensing skills is a vital task. Various techniques have been examined by researchers. This study uses a robust data fusion technique that integrates high-resolution airborne laser scanning data (LiDAR) with high-resolution QuickBird satellite imagery (2.6-m spatial resolution) to identify landslide locations in Bukit Antarabangsa, Ulu Klang, Malaysia. This idea is applied for the first time to identify landslide locations in an urban environment in tropical areas. A wavelet transform technique was employed to achieve data fusion between LiDAR and QuickBird imagery. An object-oriented classification method was used to differentiate the landslide locations from other land use/covers. The Taguchi technique was employed to optimize the segmentation parameters, whereas the rule-based technique was used for object-based classification. In addition, to assess the impact of fusion in classification and landslide analysis, the rule-based classification method was also applied on original QuickBird data which have not been fused. Landslide locations were detected, and the confusion matrix was used to examine the proficiency and reliability of the results. The achieved overall accuracy and kappa coefficient were 90.06% and 0.84, respectively, for fused data. Moreover, the acquired producer and user accuracies for landslide class were 95.86% and 95.32%, respectively. Results of the accuracy assessment for QuickBird data before fusion showed 65.65% and 0.59 for overall accuracy and kappa coefficient, respectively. It revealed that fusion made a significant improvement in classification results. The direction of mass movement was recognized by overlaying the final landslide classification map with LiDAR-derived slope and aspect factors. Results from the tested site in a hilly area showed that the proposed method is easy to implement, accurate, and appropriate for landslide mapping in a tropical country, such as Malaysia

An integrated user-friendly ArcMAP tool for bivariate statistical modeling in geoscience applications

Modelling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modelling. Bivariate statistical analysis (BSA) assists in hazard modelling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time-consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, bivariate statistical modeler (BSM), for BSA technique is proposed. Three popular BSA techniques, such as frequency ratio, weight-of-evidence (WoE), and evidential belief function (EBF) models, are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and created by a simple graphical user interface (GUI), which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve (AUC) is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications

Spatial landslide hazard assessment along the Jelapang Corridor of the North-South Expressway in Malaysia using high resolution airborne LiDAR data

Mapping landslide-prone regions are crucial in natural hazard management and urban development activities in hilly and tropical regions. This research aimed to delineate a spatial prediction of landslide hazard areas along the Jelapang Corridor of the North-South Expressway in Malaysia by using two statistical models, namely, logistic regression (LR) and evidential belief function (EBF). Landslides result in high economic and social loses in Malaysia, particularly to highway concessionaries such as PLUS Expressways Berhad. LR and EBF determine the correlation between conditioning factors and landslide occurrence. EBF can also be applied in bivariate statistical analysis. Thus, EBF can be used to assess the effect of each class of conditioning factors on landslide occurrence. A landslide inventory map with 26 landslide sites was recorded using field measurements. Subsequently, the landslide inventory was randomly divided into two data sets. Approximately 70 % of the data were used for training the models, and 30 % were used for validating the results. Eight landslide conditioning factors were prepared for landslide susceptibility analysis: altitude, slope, aspect, curvature, stream power index, topographic wetness index, terrain roughness index, and distance from river. The landslide probability index was derived from both methods and subsequently classified into five susceptible classes by using the quantile method. The resultant landslide susceptibility maps were evaluated using the area under the curve technique. Results revealed the proficiency of the LR method in landslide susceptibility mapping. The achieved success and prediction rates for LR were 90 and 88 %, respectively. However, EBF was not successful in providing reasonable accurate results. The acquired success and prediction rates for EBF were 53 and 50 %, respectively. Hence, the LR technique can be utilized in landslide hazard studies for land use management and planning

Factors affecting the eco-environment identification through change detection analysis by using remote sensing and GIS: a case study of Tikrit, Iraq

Changes in eco-environment that are caused by climate changes and human exploitation have been a significant problem around the whole world for a long time. The eco-environment of Iraq is exposed to degradation, particularly in the middle and southern parts. By using an approach that combines remote sensing and GIS, this study examines the changes that have occurred during various periods between 1972 and 2010 in the Tikrit district in Iraq and identifies the factors responsible for the degradation. A significant change was observed in the area covered by vegetation and water, especially between 1990 and 2010, which exacerbated desertification as the vegetation and water area decreased by 16 and 59.6%, respectively. Also, the urban area increased with varying paces of growth. In the period 1990–2000, the urban area increased by 8.8% only, which is not surprising considering the population increase. However, between 2000 and 2010, the urban area increased dramatically by 47.5%, due to the war which led to migration from Baghdad (Iraq Capital) to Tikrit. This study proves that climate change, desertification, and immigration due to wars were the major roles in changing the environment. Also, it reveals that geospatial techniques can be successfully used to monitor the effects on the land cover/ use changes and, hence, on the eco-environment

Comparison between satellite-derived rainfall and rain gauge observation over Peninsular Malaysia

Validation of the bias-corrected product of National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre Morphing Technique CMORPH-CRT was conducted using gridded rain gauge dataset of Wong et al. (2011) and rain gauge data from meteorological stations throughout Peninsular Malaysia. The CMORPH-CRT was compared for four contrasting topographic sub-regions of Peninsular Malaysia, i.e. west coast (WC), foothills of Titiwangsa range (FT), inland-valley (IN) and east coast (EC). CMORPH-CRT product with grid resolution of 8 km × 8 km at temporal resolution of 1-hour from 00Z January 1998 to 23Z December 2018 was utilized. The results show that CMORPH-CRT are in agreement with the rain gauge data. The CMORPH-CRT performed best over coastal sub-regions but it underestimated over FT sub-region and overestimated at IN. CMORPH-CRT tend to perform better in moderate rather than heavy rainfall events. For extreme weather events, the CMORPH-CRT had shown capability in observing the formation and decay of low-pressure system in Penang during 4th November 2017 and it is in agreement with rain gauge based SPI index i.e. drought conditions over Peninsular Malaysia

Automatic landslide detection using Dempster–Shafer theory from LiDAR-derived data and orthophotos

A good landslide inventory map is a prerequisite for landslide hazard and risk analysis. In tropical countries, such as Malaysia, preparation of the landslide inventory is a challenging task because of the rapid growth of vegetation. Thus, it is crucial to use rapid and accurate technique and effective parameters. For this purpose, Dempster Shafer theory (DST) was applied in fusing high resolution LiDAR derived data products and Greenness index derived from orthophoto imagery. Two sites were selected, for the implementation and evaluation of the DST model; site “A” for DST implementation and site “B” for the comparison. For model implementation, vegetation index, slope and height were used as effective parameters for identifying automatic landslide detection. Two type of DST based fusions were evaluated; (greenness and height) and (greenness and slope). Furthermore, validation techniques were used to validate the accuracy are confusion matrix and area under the curve. The overall accuracy of the first and second evaluated fusions were (73.4% and 84.33%), and area under the curve were (0.76 and 0.81) respectively. Additionally, the result was compared with Random Forest (RF) based detection approach. The results showed that DST does not require a priori knowledge

Optimized hierarchical rule-based classification for differentiating shallow and deep-seated landslide using high-resolution LiDAR data

Landslide is one of the most devastating natural disasters across the world with serious negative impact on its inhabitants and the environs. Landslide is considered as a type of soil erosion which could be shallow, deep-seated, cut slope, bare soil, and so on. Distinguishing between these types of soil erosions in dense vegetation terrain like Cameron Highlands Malaysia is still a challenging issue. Thus, it is difficult to differentiate between these erosion types using traditional techniques in locations with dense vegetation. Light detection and ranging (LiDAR) can detect variations in terrain and provide detailed topographic information on locations behind dense vegetation. This paper presents a hierarchical rule-based classification to obtain accurate map of landslide types. The performance of the hierarchical rule set classification using LiDAR data, orthophoto, texture, and geometric features for distinguishing between the classes would be evaluated. Fuzzy logic supervised approach (FbSP) was employed to optimize the segmentation parameters such as scale, shape, and compactness. Consequently, a correlation-based feature selection technique was used to select relevant features to develop the rule sets. In addition, in other to differentiate between deep-seated cover under shadow and normal shadow, the band ration was created by dividing the intensity over the green band. The overall accuracy and the kappa coefficient of the hierarchal rule set classification were found to be 90.41 and 0.86%, respectively, for site A. More so, the hierarchal rule sets were evaluated using another site named site B, and the overall accuracy and the kappa coefficient were found to be 87.33 and 0.81%, respectively. Based on these results, it is demonstrated that the proposed methodology is highly effective in improving the classification accuracy. The LiDAR DEM data, visible bands, texture, and geometric features considerably influence the accuracy of differentiating between landslide types such as shallow and deep-seated and soil erosion types like cut slope and bare soil. Therefore, this study revealed that the proposed method is efficient and well-organized for differentiating among landslide and other soil erosion types in tropical forested areas