Stormwater quality performance using bioretention system: a preliminary study / Norshafa Elyza Muha and Lariyah Mohd Sidek

Bioretention system, also known as rain garden is a new technology for urban stormwater management that was introduced in Urban Storm Water Management Manual for Malaysia (MSMA). In Malaysia, the application of bioretention system is recommended; however there are no performance data available for field scale installations. Two pilot projects at Humid Tropics Centre (HTC), Kuala Lumpur and UNITEN, Putrajaya Campus are models of lot-scale application in Malaysia. Water quality analysis was done to determine water quality level after it has flowed through the bioretention systems. Grab samples were collected during storms at inlets and outlets and were sent to a analytical laboratory for water quality analysis to be performed. Result from analysis showed that the water tested nearly reached Water Quality Index’s Class I and Class II level of classification. Further monitoring and analysis will be made to observe the continuing performance and behavior of the system in the conditions typically found in Malaysi

A review on Reliability, Security and Memory Management of Numerous Operating Systems

With the improvement of technology and the growing needs of computer systems, it is needed to ensure that operating systems are able to provide the required functionalities. To provide these functionality operating systems are designed to maintain some design factors such as scalability, security, reliability, performance, memory management, energy efficiency. However, none of these factors can be achieved directly without facing any challenges. This research studied several design issues that are connected to each other in terms of providing an effective result. Therefore, this review article tried to reveal the major issues, which are independently more complex to solve at once. Finally, this research provides a guideline to overcome the challenges for future researchers by studying many research articles based on these design issues

Multi Objective PSO with Passive Congregation for Load Balancing Problem

High-level architecture (HLA) and Distributed Interactive Simulation (DIS) are commonly used for the distributed system. However, HLA suffers from a resource allocation problem and to solve this issue, optimization of load balancing is required. Efficient load balancing can minimize the simulation time of HLA and this optimization can be done using the multi-objective evolutionary algorithms (MOEA). Multi-Objective Particle Swarm Optimization (MOPSO) based on crowding distance (CD) is a popular MOEA method used to balance HLA load. In this research, the efficiency of MOPSO-CD is further improved by introducing the passive congregation (PC) method. Several simulation tests are done on this improved MOPSO-CD-PC method and the results showed that in terms of Coverage, Spacing, Non-dominated solutions and Inverted generational distance metrics, the MOPSO-CD-PC performed better than the previous MOPSO-CD algorithm. Hence, it can be a useful tool to optimize the load balancing problem in HLA

Knowledge Based Expert System for Minimising Stormwater Erosion and Sedimentation in Malaysian Construction Sites

Construction activities generate enormous amount of erosion and sediments that are the result of soil disturbance during construction activities, thus, will pollute the adjacent water bodies and make it unfeasible for different uses. This paper aimed to develop and create the main features of an expert system prototype (ESCES) for minimising erosion and sedimentation due to stormwater generated from the construction activities by recommending a feasible BMPs. Multi criteria Analysis (MCA) technique has been integrated so as to select the best control measure among many stormwater control alternatives. A questionnaire has been distributed to the relevant experts so as to rank the stormwater control measures to be used in the MCA technique. Using Visual Basic 6, Graphical User Interfaces (GUIs) were developed. The knowledge and experience were acquired from various textural sources (i.e. guidelines, manuals, literature, and humanexpert). Results from this study showed that the Best Management Practices (BMPs) recommended have good suited the site characteristics as the relevant experts have showed their convenience to the developed prototype since they asked to fill in simple questionnaire after developing the system and presented to them. The conclusion drawn from this study indicates that  the ESCES can be considered as “Green Technology Tool” since it helps in protecting  the environment and preserve good quality of water adjacent to the construction sites in Malaysia

Design and Analysis of High Gain Low Power CMOS Comparator

The comparator is the most significant component of the analog-to-digital converter, voltage regulator, switching circuits, communication blocks etc. Depending on the various design schemes, comparator performance varied upon target applications. At present, low power, high gain, area efficient and high-speed comparator designed methods are necessary for complementary metal oxide semiconductor (CMOS) industry. In this research, a low power and high gain CMOS comparator are presented which utilized two-stage differential input stages with replication of DC current source to achieve higher gain, higher phase margin, higher bandwidth, and lower power consumption. The simulated results showed that, by using a minimum power supply of 1.2 V, the comparator could generate higher gain 77.45 dB with a phase margin of 60.08°. Moreover, the modified design consumed only 2.84 µW of power with a gain bandwidth of 30.975 MHz. In addition, the chip layout area of the modified comparator is found only 0.0033 mm2

Risk analysis of water grid systems using threat modeling

Critical infrastructure systems consist of physical and cyber assets that are essential to the operation of the economy and the government. As one of the most important critical infrastructures worldwide, the water sector has become vulnerable to new risks in the form of cyber threats that can severely impact public health, and are difficult to detect. A water grid system (WGS) plays an important role in guarding the business processes of the water sector against possible threats and risks. Threat modeling can be used to analyze threats to the WGS. It is applied to identify points of access to the assets and devices of the system, classify threats to them, assess the risks posed by them, and suggest mitigation measures. Each threat is classified based on its type according to the STRIDE methodology, and the results of the threat classification can be used to assess the level of risk by using the DREAD methodology. This yields a risk rating for each threat that can be used to devise mitigation measures to minimize the risk posed by it. Through the threat modeling stage, it is known that the high-risk threats on WGSs are tampering with a risk score of 14, denial of service threats with a risk score of 13, and repudiation threats with a risk score of 12. The results of the ranking are used to formulate recommendations in the form of mitigation controls against these threats

Mean Field Bias Correction to Radar QPE as Input to Flood Modeling for Malaysian River Basins

The occurrence of unprecedented flood events has increased in Malaysia recently. To mitigate the impact of the disaster, the National Flood Forecasting and Warning System (NaFFWS) has endeavored to improve the system so as to produce more accurate and reliable early warning to the public. The paper describes the use of radar composites from the radar network in Peninsular Malaysia to produce quantitative precipitation estimates (QPE) as input to the NaFFWS flood model. The processing of the raw radar data and the conversion of rain rate are described. The comparison between radar QPE and gauge rainfall shows that radar QPE underestimates the gauge rainfall, and the results are better at the western parts of Peninsular Malaysia compared to the eastern parts of Peninsular Malaysia. The comparison between Marshall Palmer (MP) and Rosenfeld (RF) conversion equations shows that there is not much difference in performance between the two equations. Both underestimate the rainfall, although RF estimates higher radar QPE for high rainfall intensity. The underestimated radar QPE is improved by calibration process via the Mean Field Bias (MFB) correction technique. The study introduced zoning into smaller regions for the MFB factors derivation.  Results indicated that the radar QPE is much improved after the calibration process. Simulation of flood event in December 2021 for the case study of Langat River basin indicates the improvement of correlation coefficient from 0.67 to 0.99 after the calibration process via MFB for smaller zones

Mean Field Bias Correction to Radar QPE as Input to Flood Modeling for Malaysian River Basins

The occurrence of unprecedented flood events has increased in Malaysia recently. To mitigate the impact of the disaster, the National Flood Forecasting and Warning System (NaFFWS) has endeavored to improve the system so as to produce more accurate and reliable early warning to the public. The paper describes the use of radar composites from the radar network in Peninsular Malaysia to produce quantitative precipitation estimates (QPE) as input to the NaFFWS flood model. The processing of the raw radar data and the conversion of rain rate are described. The comparison between radar QPE and gauge rainfall shows that radar QPE underestimates the gauge rainfall, and the results are better at the western parts of Peninsular Malaysia compared to the eastern parts of Peninsular Malaysia. The comparison between Marshall Palmer (MP) and Rosenfeld (RF) conversion equations shows that there is not much difference in performance between the two equations. Both underestimate the rainfall, although RF estimates higher radar QPE for high rainfall intensity. The underestimated radar QPE is improved by calibration process via the Mean Field Bias (MFB) correction technique. The study introduced zoning into smaller regions for the MFB factors derivation.  Results indicated that the radar QPE is much improved after the calibration process. Simulation of flood event in December 2021 for the case study of Langat River basin indicates the improvement of correlation coefficient from 0.67 to 0.99 after the calibration process via MFB for smaller zones

Generalized regression neural network for prediction of peak outflow from dam breach

Several techniques have been used for estimation of peak outflow from breach when dam failure occurs. This study proposes using a generalized regression artificial neural network (GRNN) model as a new technique for peak outflow from the dam breach estimation and compare the results of GRNN with the results of the existing methods. Six models have been built using different dam and reservoir characteristics, including depth, volume of water in the reservoir at the time of failure, the dam height and the storage capacity of the reservoir. To get the best results from GRNN model, optimized for smoothing control factor values has been done and found to be ranged from 0.03 to 0.10. Also, different scenarios for dividing data were considered for model training and testing. The recommended scenario used 90% and 10% of the total data for training and testing, respectively, and this scenario shows good performance for peak outflow prediction compared to other studied scenarios. GRNN models were assessed using three statistical indices: Mean Relative Error (MRE), Root Mean Square Error (RMSE) and Nash – Sutcliffe Efficiency (NSE). The results indicate that MRE could be reduced by using GRNN models from 20% to more than 85% compared with the existing empirical methods

Modelling erosion and landslides induced by farming activities at hilly areas, Cameron Highlands, Malaysia

This work was conducted at hilly farms of Cameron Highlands to model the incidences of soil erosion and landslides using historical data and field observations. IfSAR data with spatial resolution of 5 m was used which enable clear observation and delineation of the geographic features within the study area. Field visits were conducted to various places where landslides occurred on agricultural farms in order to validate the model. Also, the rate of soil erosions was evaluated using geospatial techniques. The potential landslide event and its probability of occurrence were combined using bivariate statistical analysis. The results revealed that most of the landslides incidents were occurred at areas with intensive agricultural activities with no proper erosion control measures. It was gathered that more than 75% of landslides occurred in agricultural activities areas are under sheltered farms. The annual soil erosion rates in both Telom and Bertom Catchments ware 38 ton /ha/year and 73.9 ton /ha/year respectively. It was revealed that, there is high risk of erosion-induced landslides in agricultural farms. However, the erosion induced landslide map shows that most the landslide occurred close to the rivers. This indicated that both agricultural operations and proximity to rivers are influencing factors for the incidences