Chlorine Decay Simulation in Water Distribution System Using EPANET

Chlorine is used as a disinfectant in the water treatment process so that treated water is delivered safely to consumers. However, chlorine concentration decays when water flows from the treatment plant to the supply point, due to the reaction with natural organic matter and the inner surface of the pipe. Low chlorine concentration may encourage bacteria re-growth, while high chlorine concentration can result in the formation of harmful chemical components. Therefore, this study aims to simulate the complex process of chlorine decay using EPANET. This exercise enables the determination the chlorine concentration dosage required to maintain the desired requirement given by the World Health Organization (WHO) and the Ministry of Health, Malaysia (MOH). A successful model with an extended period of simulations of 72 hours enable the mapping of spatial and temporal variations of flow and residue chlorine concentrations at all links and nodes. Constant chlorine dosage of 3.96 mg/l at node R1 has successfully satisfy the requirement given by WHO and MOH. The residue chlorine concentrations at the nodes and links in the water distribution system also depends on the water usage at node 5, the size of service reservoir and service tank and distance from the reservoir

Systematic review on research trends on sensor-based leak detection methods in water distribution systems

A substantial amount of treated water is lost every year due to leakages in water distribution systems. Leakages can be identified and reduced using leakage detection methods, which can be broadly split into computer-based and sensor-based methods. This systematic review focuses on trends in sensor-based leakage detection methods published between 2000 and 2019, following the methodology proposed by PRISMA 2009 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). We conducted a database search using Scopus, obtaining a total of 78 relevant article papers. We categorized the articles based on the primary leakage detection methods discussed, yielding 33 article papers on acoustic methods, 31 article papers on non-acoustic methods, and the remaining article papers on wireless sensor networks (WSN). The highest number of article papers were published in the “Journal of Sound and Vibration”. Between 2000 and 2007 we observed that acoustic leak detection methods were the most widely researched methods within the published literature. After 2008, non-acoustic leak detection methods became more prominent, subsequently followed by an increase in research focusing on WSNs. During the transition period between acoustic methods and WSNs, non-acoustic leak detection methods started to emerge, showing promising results in detecting leakages. Research interest in WSNs substantially increased in 2016. The application of WSN methods for leakage detection shows a promising advancement in sensor-based leakage detection methods and opportunities for improvement in the future

Emerging trends in flood and landslide research: single vs multi-hazard disaster analysis using GIS

Floods and landslides, which cause significant loss of human life and economic loss, are the most reported catastrophic events worldwide. The Geographical Information System (GIS) has been recognized as one of the most effective tools in disaster related analysis. Therefore, this article uses GIS to review the development of landslide and flood research for the past 20 years. The main elements in this review are to scrutinize the trend and scope of studies related to disaster mapping around the globe. Amongst the criteria reviewed are; details of the study area, articles that received many citations, journals with high Impact Factor scores, scope breakdown based on single and multi-hazard analysis and the theme of the study. The methodology used in this Systematic Literature Review is based on the PRISMA guidelines. Results from the review found that studies related to disaster mapping are increasing every year. This trend is influenced by data availability, efforts to produce better disaster management, frequent disaster occurrences due to climate change and evolution of GIS to analyse spatial data. Nevertheless, articles related to multi-hazard analysis are still limited, and this study suggests conducting and publishing more studies related to multi-hazard assessment in the future. This review also shows that GIS has been used widely for various types of application in disaster analysis. Articles on disaster risk assessment have been the most common. This review will help other researchers in the field of disaster management to better understand the current trend of studies related to disaster mapping

Traffic sign classification using transfer learning: An investigation of feature-combining model

The traffic sign classification system is a technology to help drivers to recognise the traffic sign hence reducing the accident. Many types of learning models have been applied to this technology recently. However, the deployment of learning models is unknown and shown to be non-trivial towards image classification and object detection. The implementation of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features as well as can save lots of training time. Besides, the feature-combining model exhibited great performance in the TL method in many applications. Nonetheless, the utilisation of such methods towards traffic sign classification applications are not yet being evaluated. The present study aims to exploit and investigate the effectiveness of transfer learning feature-combining models, particularly to classify traffic signs. The images were gathered from GTSRB dataset which consists of 10 different types of traffic signs i.e. warning, stop, repair, not enter, traffic light, turn right, speed limit (80km/s), speed limit (50km/s), speed limit (60km/s), and turn left sign board. A total of 7000 images were then split to 70:30 for train and test ratio using a stratified method. The VGG16 and VGG19 TL-features models were used to combine with two classifiers, Random Forest (RF) and Neural Network. In summary, six different pipelines were trained and tested. From the results obtained, the best pipeline was VGG16+VGG19 with RF classifier, which was able to yield an average classification accuracy of 0.9838. The findings showed that the feature-combining model successfully classifies the traffic signs much better than the single TL-feature model. The investigation would be useful for traffic signs classification applications i.e. for ADAS system

Probability structure and return period calculations for multi-day monsoon rainfall events at Subang, Malaysia

2013 Fall.Includes bibliographical references.Flooding is the most common natural disaster in Malaysia, as a result of heavy rainfall. Malaysia is located in the equatorial zone and experiences a tropical climate with two seasons classified as the Northeast (November to May) and Southwest (May to September) monsoons. Both monsoons bring moisture, and multi-day rainfall events that cause particularly devastating floods on large watersheds. The objectives of this study are the following: (1) examine the probability structure of multi-day rainfall events; (2) determine the most suitable distribution function to represent the multi-day rainfall amounts; (3) select the most appropriate model to simulate the sequence of daily rainfall using the discrete autoregressive family models; and (4) develop and test an approach to calculate the return period of multi-day rainfall events with respect to the duration and amount. Daily monsoon rainfall data recorded at Subang Airport are gathered from the Malaysian Meteorological Department. Subang Airport is located near Kuala Lumpur (the capital city of Malaysia) and has a long and reliable daily rainfall record, with 18,993 daily measurements from 1960 to 2011. The majority of wet and dry events at Subang Airport from 1960 to 2011 are multi-days, with the fraction of 57% and 51%, respectively. The analysis of conditional probabilities for t-consecutive wet and dry days shows that the probability of occurrence for multi-day wet and dry days is increasing as the event duration increases. For example, the probability of rain on any random day is 0.53; and the conditional probability of rain the second day increases to 0.63. Also, the probability of dry on any random day is 0.47; and the probability of the second dry day increases to 0.58. The probability of rain and dry days increases gradually with rainfall duration. This finding shows that the occurrence of rain and dry is time-dependent. The autocorrelation coefficient for the daily rainfall amounts is very low at 0.0283. It is concluded that this parameter is independent from one day to another. The two parameter gamma function is most suitable to fit the daily rainfall precipitation data and the cumulative rainfall from t-consecutive rainy days up to 6 days. A graphical method, i.e. the 1:1 plot confirms the goodness-of-fit of the gamma function. Two discrete autoregressive models are tested in this study, i.e., the low order Discrete Auto Regressive [DAR(1)] and the low order Discrete Auto Regressive and Moving Average [DARMA(1,1)]. These models require data stationarity, therefore the analysis is done separately for the Northeast and Southwest monsoons. The model selection is based on the four-step process suggested by Salas and Pielke (2003). The comparisons between the observed and calculated autocorrelation coefficient and the low sum of squared errors for the probability distributions confirm that DARMA(1,1) is most suitable to simulate daily rainfall sequences at Subang Airport for both monsoons. The return period for 1-day and multi-day rainfall events is defined as a function of wet run length and rainfall amount. A test of return period calculations up to 20 years based on the mean wet and dry run lengths shows good agreement between calculation and observations of multi-day rainfall amounts up to 150 mm. A very long sequence of daily rainfall (1,000,000 days) is generated to extend the analysis of multi-day events with cumulative rainfall up to 350 mm, which gives an estimated return period of more than 2,000 years. The mean, standard deviation, maximum daily rainfall, lag-1 ACF coefficient and maximum wet and dry run lengths of the generated daily rainfall sequence using DARMA(1,1) are also comparable with the observed data. The December 2006 rainstorm event at Kota Tinggi, Johor is used as an example of the application of the algorithms developed in this study. This multi-day rainstorm totaling 350 mm caused devastating floods in the area. The December 2006 rainstorm is extremely rare because the cumulative rainfall amount from the multi-day event gives an estimated return period of greater than 2,000 years. The method proposed in this study is helpful for the design of levees on large watersheds (size of more than 1,000 km2) because multi-day rainstorms are the main cause of flooding to the area. For example, the return period to overtop the current levee at Kota Tinggi is 220 years when considering a 1-day rainstorm, but this period of return decreases to 24 years when considering 4-day rainstorms

Laboratory study on breakwater / Mohd Farid Ahmad @ Majid, Nur Shazwani Muhammad and Salina Albas

Breakwater functioned as an energy dissipater that protects the beach area from wave attacks. This research is intended to test and introduce a newly designed open type breakwater which consists of multi layer horizontal plates. The objectives of this research are to study the effectiveness of the new configuration of multi layer breakwater with respect to the wave characteristics and the breakwater dimensions, to evaluate the ability of the multi layer breakwater in dissipating energy in terms of energy loss percentage and to compare the performance of the multi layer breakwater in reflecting wave with a vertical sea wall. Nine multi layer breakwater models with different widths and a model of vertical sea wall were fabricated and tested for this research. Simulations for all models were carried out for two water depths and four regular wave periods. The data obtained from laboratory exercise were analysed using 3-point method introduced by Mansard and Funke (1980). The results were discussed into several separate sections, namely wave steepness, relative gap, relative width and energy loss. It can be concluded from the findings that transmission coefficient was independent of gap spacing but dependant on breakwater width, wave steepness and wave period. Reflection coefficient was independent of breakwater width and gap spacing but dependant on wave steepness and wave period. The multi layer breakwater was able to dissipate energy significantly and more energy was dissipated in fully submerged condition. The proposed breakwater is suitable to be used when the wave period is short. Another important finding was the multi layer breakwater was not able to reflect wave strongly as compared to vertical sea wall

Chlorine decay and formation of THM in Malaysia’s water distribution system

Chlorine is a popular disinfectant used in Malaysia in the treatment process of drinking water supply because of its effectiveness. The concentration of chlorine deteriorates upon travelling in the system due to its reaction with organic matter to produce carcinogenic substances known as disinfection by-products (DBP) such as trihalomethanes (THM). This study was conducted to investigate chlorine decay and THM formation in a state's drinking water distribution system in Malaysia specifically across a 24.9 km distance. EPANET 2.0 Software program was used to perform hydraulics and water quality analysis using the extended period simulation (EPS) for 24 hours demand pattern. A simulation of the water distribution system was performed to identify the formation of THM and its relationship between chlorine and total organic carbon (TOC). The value of THM was maintained at a lower level at the water treatment plant (WTP) than at the endpoint of the distribution system. At the endpoint, which was at the targeted industrial area, the level of THM was found to increase and the obtained data showed that its formation occurred along the investigated distribution system. THM formation manifested as the natural organic matter (NOM) presence along the pipe continuously reacted with chlorine which was dosed in the distribution system

Identification of potential sites for runoff water harvesting

Runoff water harvesting (RWH) is a potential solution for areas suffering from water scarcity, such as the western desert of Iraq. Site selection based on RWH ranking using a combination of a watershed modelling system, geographic information systems and remote sensing techniques may enable authorities and water engineers to determine potential solutions to water scarcity. In this work, these methods were employed to produce eight thematic maps of the volume of annual floods, basin area, basin length, maximum flow distance, drainage frequency density, lineament frequency density, basin slope and stream order. These maps were used to rank and classify probable sites based on equal weight and statistical weight. The results were then used to classify the selected sites into four classes, namely sites with very high, high, moderate and low RWH potential. The proposed method was shown to be beneficial in the identification of potential RWH sites. © 2017 ICE Publishing: All rights reserved

Wavelet-ANN versus ANN-Based Model for Hydrometeorological Drought Forecasting

Malaysia is one of the countries that has been experiencing droughts caused by a warming climate. This study considered the Standard Index of Annual Precipitation (SIAP) and Standardized Water Storage Index (SWSI) to represent meteorological and hydrological drought, respectively. The study area is the Langat River Basin, located in the central part of peninsular Malaysia. The analysis was done using rainfall and water level data over 30 years, from 1986 to 2016. Both of the indices were calculated in monthly scale, and two neural network-based models and two wavelet-based artificial neural network (W-ANN) models were developed for monthly droughts. The performance of the SIAP and SWSI models, in terms of the correlation coefficient (R), was 0.899 and 0.968, respectively. The application of a wavelet for preprocessing the raw data in the developed W-ANN models achieved higher correlation coefficients for most of the scenarios. This proves that the created model can predict meteorological and hydrological droughts very close to the observed values. Overall, this study helps us to understand the history of drought conditions over the past 30 years in the Langat River Basin. It further helps us to forecast drought and to assist in water resource management

Research Trends of Hydrological Drought: A Systematic Review

The frequency and severity of global drought-induced impacts have led to raising awareness of the need for improved river management. Although academic publications on drought have proliferated, a systematic review of literature has not yet been conducted to identify trends, themes, key topics, and authorships. This study aims to evaluate the scientific evidence for the hydrological drought characteristics and the methodologies by performing as a framework. This systematic review performed three-stage screening of literature review for current applicable hydrological drought studies that have been conducted since the year of 2000 concerning methodologies, literature research gaps, and trends, and contribute to future studies. The analysis shows the increasing trends of research and publications in the hydrological drought assessment. The primary research themes are hydrological drought is drought severity, drought vulnerability, and drought forecast. Despite the current research findings, spatial and temporal variability, low flow analysis and regional modelling are the most important to encourage a holistic approach and international collaborations. The finding identified the shortcomings of most research, which are the use of non-standardized methodological and distinct sample sizes, resulting in data summary challenges and unrealistic comparisons