Bottom-up Hierarchical Approach Research Articles

Risk-Based Inspection and Rehabilitation Planning of Service Connections in Intermittent Water Supply Systems for Leakage Management in Arid Regions

Most of the leakage in water distribution systems operating with plastic pipes occurs at service connections (SCs), while the existing tools plan rehabilitation of pipes. With limited water resources, intermittent supplies in arid regions further enhance the failure vulnerability of metal fittings on water mains due to scale formation and large pressure transients. The present research developed a risk-based methodology for the proactive maintenance of SCs in intermittent water supply systems. A five-generation bottom-up hierarchical approach aggregated the basic hydraulic, physical, and water quality factors to determine the vulnerability of structural failures of SCs. Hydraulic parameters (pressure and velocity) were estimated by simulating a distribution network of 366 water mains of diameters ranging from 110 mm to 225 mm serving 371 SCs in a residential neighborhood located in the Qassim region of Saudi Arabia. Age, depth, and length of SCs’ estimated the condition index, while soil corrosivity and condition of the water mains were also counted when assessing the structural failure index for each SC. Water quality parameters, e.g., pH, turbidity, and iron, that can contribute to the vulnerability of an SC’s failure were also included. Fuzzy-based methods first assessed the relative importance weights of the basic input parameters at the bottom of the hierarchy and the risk factors in the middle of the hierarchy. Subsequently, the performance and condition scores were aggregated to develop respective indices. As the consequence of structural failure is high for the SCs serving households with a large number of residents, the final risk index aggregates the vulnerability and consequence at the hierarchy’s top. The developed model was effectively validated by comparing the SCs of high priority with the leaking and repaired SCs in the past. The method will be a useful tool for planning proactive inspection and rehabilitation of SCs of intermittent supply systems to minimize water losses (less than 8% of the national benchmark) in Saudi Arabia and elsewhere.

Implementation of E-Government through the Samsat Mobile Jawa Barat at the Regional Revenue Agency of West Java Province

The purpose of this study was to analyze the application of e-government through Samsat Mobile Jawa Barat at the Regional Revenue Agency of West Java Province. The theory used in this study uses e-government theory which includes (1) content development, (2) competency building, (3) connectivity, (4) cyber law, (5) citizen interface, and (6) capital. The research approach used is a qualitative approach. Data collection techniques by interview, observation and documentation. The data analysis technique is based on Creswell's thinking with a bottom-up hierarchical approach model. The results of this study indicate that the implementation of e-government through Samsat Mobile Jawa Barat in the Regional Revenue Agency of West Java Province has been running quite well. Although based on Idrajati's e-government theory, there are several criteria that need to be improved. Such as content development criteria that still do not provide an iOS-based Samsat Mobile Jawa Barat application, competency building criteria have not yet produced employees who are experts in managing the appearance of the Bapenda West Java website design and content. The connectivity criteria are good because they are in the West Java region. Cyber law criteria must be added specifically, because it only follows the rules of the central government, the citizen interface criteria still need to be developed because it only has two channels, namely the website and the android application. While the criteria for capital is sufficient, it only needs additional sources of capital apart from the APBD.

Leveraging Turbine-Level Data for Improved Probabilistic Wind Power Forecasting

This paper describes two methods for creating improved probabilistic wind power forecasts through the use of turbine-level data. The first is a feature engineering approach whereby deterministic power forecasts from the turbine level are used as explanatory variables in a wind farm level forecasting model. The second is a novel bottom-up hierarchical approach where the wind farm forecast is inferred from the joint predictive distribution of the power output from individual turbines. Notably, the latter produces probabilistic forecasts that are coherent across both turbine and farm levels, which the former does not. The methods are tested at two utility scale wind farms and are shown to provide consistent improvements of up to 5%, in terms of continuous ranked probability score compared to the best performing state-of-the-art benchmark model. The bottom-up hierarchical approach provides greater improvement at the site characterized by a complex layout and terrain, while both approaches perform similarly at the second location. We show that there is a clear benefit in leveraging readily available turbine-level information for wind power forecasting.

A Functional Resonance Analysis Method Driven Resilience Quantification for Socio-Technical Systems

Due to the continuous increment in complexity of the socio-technical systems, decision makers call for new methods which are able to support timely as well as accurate decision-making related to resilience management. The current methods tend to be polarized on: efficiency-thoroughness forcing decision makers in making decisions on the base of resource availability instead of the problem to be solved. This paper presents a new fast-forward, cost-effective, and thorough enough framework to quantify resilience of a complex socio-technical system. The approach extends the functional resonance analysis method (FRAM) with a numerical method for the quantification of the analysis (Q-FRAM). In particular, it has been extended and operationalized the qualitative concepts of functional variability and dumping capacities into a method in which key performance indicators are derived from the model and aggregated into four indicators representing the FRAM resilience cornerstones (anticipate, respond, monitor, learn) through a bottom-up hierarchical approach. Finally, the four indicators are composed in a unique system resilience index that expresses the total variability present in the system at instant t . A numerical example of the use of the framework is provided together with a validation based on a comparison of the proposed approach with the current landscape.