Methodology for development of drought Severity-Duration-Frequency (SDF) Curves

Drought monitoring and early warning are essential elements impacting drought sensitive sectors such as primary production, industrial and consumptive water users. A quantitative estimate of the probability of occurrence and the anticipated severity of drought is crucial for the development of mitigating strategies. The overall aim of this study is to develop a methodology to assess drought frequency and severity and to advance the understanding of monitoring and predicting droughts in the future. Seventy (70) meteorological stations across Victoria, Australia were selected for analysis. To achieve the above objective, the analysis was initially carried out to select the most applicable meteorological drought index for Victoria. This is important because to date, no drought indices are applied across Australia by any Commonwealth agency quantifying drought impacts. An evaluation of existing meteorological drought indices namely, the Standardised Precipitation Index (SPI), the Reconnaissance Drought Index (RDI) and Deciles was first conducted to assess their suitability for the determination of drought conditions. The use of the Standardised Precipitation Index (SPI) was shown to be satisfactory for assessing and monitoring meteorological droughts in Australia. When applied to data, SPI was also successful in detecting the onset and the end of historical droughts. Temporal changes in historic rainfall variability and the trend of SPI were investigated using non-parametric trend techniques to detect wet and dry periods across Victoria, Australia. The first part of the analysis was carried out to determine annual rainfall trends using Mann Kendall (MK) and Sen’s slope tests at five selected meteorological stations with long historical records (more than 100 years), as well as a short sub-set period (1949-2011) of the same data set. It was found that different trend results were obtained for the sub-set. For SPI trend analysis, it was observed that, although different results were obtained showing significant trends, SPI gave a trend direction similar to annual precipitation (downward and upward trends). In addition, temporal trends in the rate of occurrence of drought events (i.e. inter-arrival times) were examined. The fact that most of the stations showed negative slopes indicated that the intervals between events were becoming shorter and the frequency of events was temporally increasing. Based on the results obtained from the preliminary analysis, the trend analyses were then carried out for the remaining 65 stations. The main conclusions from these analyses are summarized as follows; 1) the trend analysis was observed to be highly dependent on the start and end dates of analysis. It is recommended that in the selection of time period for the drought, trend analysis should consider the length xvi of available data sets. Longer data series would give more meaningful results, thus improving the understanding of droughts impacted by climate change. 2) From the SPI and inter-arrival drought trends, it was observed that some of the study areas in Victoria will face more frequent dry period leading to increased drought occurrence. Information similar to this would be very important to develop suitable strategies to mitigate the impacts of future droughts. The main objective of this study was the development of a methodology to assess drought risk for each region based on a frequency analysis of the drought severity series using the SPI index calculated over a 12-month duration. A novel concept centric on drought severity-duration-frequency (SDF) curves was successfully derived for all the 70 stations using an innovative threshold approach. The methodology derived using extreme value analysis will assist in the characterization of droughts and provide useful information to policy makers and agencies developing drought response plans. Using regionalisation techniques such as Cluster analysis and modified Andrews curve, the study area was separated into homogenous groups based on rainfall characteristics. In the current Victorian application the study area was separated into six homogeneous clusters with unique signatures. A set of mean SDF curves was developed for each cluster to identify the frequency and severity of the risk of drought events for various return periods in each cluster. The advantage of developing a mean SDF curve (as a signature) for each cluster is that it assists the understanding of drought conditions for an ungauged or unknown station, the characteristics of which fit existing cluster groups. Non-homogeneous Markov Chain modelling was used to estimate the probability of different drought severity classes and drought severity class predictions 1, 2 and 3 months ahead. The non-homogeneous formulation, which considers the seasonality of precipitation, is useful for understanding the evolution of drought events and for short-term planning. Overall, this model predicted drought situations 1 month ahead well. However, predictions 2 and 3 months ahead should be used with caution. Many parts of Australia including Victoria have experienced their worst droughts on record over the last decade. With the threat of climate change potentially further exacerbating droughts in the years ahead, a clear understanding of the impact of droughts is vital. The information on the probability of occurrence and the anticipated severity of drought will be helpful for water resources managers, infrastructure planners and government policy-makers with future infrastructure planning and with the design and building of more resilient communities

A conceptual framework for mechatronics curriculum using stufflebeam CIPP evaluation model

Evaluation means a study that has been established and implemented to help the observer to assess the value and merit of an object. This evaluation can be performed on any activities that have the aim. The model also visualize a conceptual representation of an activity about their relationship between the various elements that been involved in such activities. By using the model, assessment activities can be conducted completely and significant. The researcher choose Stufflebeam CIPP evaluation model because of its effectiveness to get results of formative and summative, to find the decision, and also problem solving ability. In this paper, the author is trying to define the stage dimensional and elements context, input, process and product in Mechatronics curriculum implementation. The method that been used are the analysis of documents, with reference of previous studies. Reviewing of literature can be used as a guide to researchers. As a results, the researchers will identify the aspects of the study based on the dimensions of context, input, process and product. Conceptual framework was intended to be used in the implementation of the evaluation study of Diploma in Mechatronic Engineering in polytechnic toward the needs of industry in Malaysia. In fact, this aspect of the study can be used to assess the Mechatronics program in terms of overall performance and significant. Stufflebeam CIPP evaluation model been used and designed not only to prove a decision but acts as a fixer where the information can be used as a guide for designing a program

Corporate Governance Practices and Firm Performance: Evidence from Top 100 Public Listed Companies in Malaysia

AbstractCorporate governance practices have been a concerned issue by many Asian countries after the Asian Financial Crisis in 1997 including Malaysia. Due to the crisis, Malaysian Code of Corporate Governance (MCCG) has been introduced as part of the Bursa Malaysia (BMB) listing rules. This study hence focuses on corporate governance practices among Top 100 public listed companies in Bursa Malaysia and the relationship between corporate governance practices with firm performance. Two corporate governance's indicators (Board size and Board Independence) were chosen in testing the hypothesized relationship between corporate governance practices with firm performance, which was measured by return on asset (ROA) and return on equity (ROE). Descriptive and correlation analysis were used to examine the hypotheses in this study. The result showed that board size has significantly weak negative relationship with ROA but it was found to be insignificant to ROE. The other finding indicated that there was no relationship between board independence and firm performance. This study provide useful information for policy makers or regulators in improving the corporate governance policies in the future and also helps in increasing understanding on the relationship between corporate governance practices and firm's performance

Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video

Wireless Capsule Endoscopy (WCE) allows direct visual inspecting of the full digestive system of the patient without invasion and pain, at the price of a long examination by physicians of a large number of photographs. This research presents a new approach to color extraction to differentiate bleeding frames from normal ones and locate more bleeding areas. We have a dual-system suggestion. We use entire color information on the WCE pictures and the pixel-represented clustering approach to get the clustered centers that characterize WCE pictures as words. Then we evaluate the status of a WCE framework using the nearby SVM and K methods (KNN). The classification performance is 95.75% accurate for the AUC 0.9771% and validates the exciting performance for bleeding classification provided by the suggested approach. Second, we present a two-step approach for extracting saliency maps to emphasize bleeding locations with a distinct color channel mixer to build a first-stage salience map. The second stage salience map was taken with optical contrast.We locate bleeding spots following a suitable fusion approach and threshold. Quantitative and qualitative studies demonstrate that our approaches can correctly distinguish bleeding sites from neighborhoods

Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video

Wireless Capsule Endoscopy (WCE) allows direct visual inspecting of the full digestive system of the patient without invasion and pain, at the price of a long examination by physicians of a large number of photographs. This research presents a new approach to color extraction to differentiate bleeding frames from normal ones and locate more bleeding areas. We have a dual-system suggestion. We use entire color information on the WCE pictures and the pixel-represented clustering approach to get the clustered centers that characterize WCE pictures as words. Then we evaluate the status of a WCE framework using the nearby SVM and K methods (KNN). The classification performance is 95.75% accurate for the AUC 0.9771% and validates the exciting performance for bleeding classification provided by the suggested approach. Second, we present a two-step approach for extracting saliency maps to emphasize bleeding locations with a distinct color channel mixer to build a first-stage salience map. The second stage salience map was taken with optical contrast.We locate bleeding spots following a suitable fusion approach and threshold. Quantitative and qualitative studies demonstrate that our approaches can correctly distinguish bleeding sites from neighborhood

Machine learning techniques to detect bleeding frame and area in wireless capsule endoscopy video

Wireless Capsule Endoscopy (WCE) allows direct visual inspecting of the full digestive system of the patient without invasion and pain, at the price of a long examination by physicians of a large number of photographs. This research presents a new approach to color extraction to differentiate bleeding frames from normal ones and locate more bleeding areas. We have a dual-system suggestion. We use entire color information on the WCE pictures and the pixel-represented clustering approach to get the clustered centers that characterize WCE pictures as words. Then we evaluate the status of a WCE framework using the nearby SVM and K methods (KNN). The classification performance is 95.75% accurate for the AUC 0.9771% and validates the exciting performance for bleeding classification provided by the suggested approach. Second, we present a two-step approach for extracting saliency maps to emphasize bleeding locations with a distinct color channel mixer to build a first-stage salience map. The second stage salience map was taken with optical contrast.We locate bleeding spots following a suitable fusion approach and threshold. Quantitative and qualitative studies demonstrate that our approaches can correctly distinguish bleeding sites from neighborhoods