In infrastructure management, multi-criteria decision-making (MCDM) has emerged as a decision support tool to integrate various technical information and stakeholder values. Different MCDM techniques and tools have been developed. This paper presents a comprehensive review on the application of MCDM literature in the field of infrastructure management. Approximately 300 published papers were identified that report MCDM applications in the field of infrastructure management during 1980–2012. The reviewed papers are classified into application to the type of infrastructure (e.g. bridges and pipes), and prevalent decision or intervention (e.g. repair and rehabilitate). In addition, the papers were also classified according to MCDM methods used in the analysis. The paper provides taxonomy of those articles and identifies trends and new developments in MCDM methods. The results suggest that there is a significant growth in MCDM applications in infrastructure management applications of MCDM over the last decade...

A review of multi-criteria decision-making methods for infrastructure management

Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction

Review of soft sensor methods for regression applications

Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review

This paper surveys and discusses the application of data-derived soft-sensing techniques in biological wastewater treatment plants. Emphasis is given to an extensive overview of the current status and to the specific challenges and potential that allow for an effective application of these soft-sensors in full-scale scenarios. The soft-sensors presented in the case studies have been found to be effective and inexpensive technologies for extracting and modelling relevant process information directly from the process and laboratory data routinely acquired in biological wastewater treatment facilities. The extracted information is in the form of timely analysis of hard-to-measure primary process variables and process diagnostics that characterize the operation of the plants and their instrumentation. The information is invaluable for an effective utilization of advanced control and optimization strategies. We review data-derived soft-sensors proposed for biological wastewater treatment.Increased amount of measured process data has made data-driven modelling attractive.A general guideline for the data-derived soft-sensor development is presented.Artificial intelligence and multivariate statistical methods are popular in soft-sensor design.Popularity of the on-line prediction applications has increased during recent years.

Review: Data-derived soft-sensors for biological wastewater treatment plants: An overview

The effects of data preprocessing in the determination of coagulant dosing rate

Determining coagulant dosing rates currently depends on Jar-test results and the experience of the operators in many cases. The nature of these practices makes it difficult to cope quickly with the rapid fluctuation of raw water quality, mainly because it takes a relatively long time to obtain Jar-test results. For promptly predicting required coagulant doses in response to water quality changes, a number of researchers have attempted to use the multi-variable regression (MVR) approach. However, the prediction capability of the MVR approach has not been satisfactory. An artificial neural network (ANN) is an excellent estimator of the nonlinear relationship between the accumulated input and output numerical data. Using this characteristic of the ANN, this study has attempted to predict the optimal coagulant dosing rate accurately and quickly. To train the ANN and deduce the MVR equation, a set of 142 units of data chosen from the 2-year operation of a water treatment plant was used. Another set of 72 units of data, not used in training, was also used to check the prediction capability of the trained ANN and MVR equation. Root-mean-square normalized error (RMSE) was used as a quantitative indicator of prediction capability. For the training data set and the raw data set, the RMSEs of the MVR equation were, respectively, 0.0143 and 0.0193 while those of the ANN were 0.0058 and 0.0092, respectively. These results indicate that the ANN reduced the prediction error for the training data by about 59%, and for the raw data by about 52%. Thus, our study demonstrates that the prediction capability of the ANN for raw data is enhanced by twice that of MVR. As the advancement of on-line monitoring techniques enables the ANN to update the weights periodically, its prediction capability can be also continuously enhanced.

Determination of optimal coagulant dosing rate using an artificial neural network

This study is aimed at developing internal reform plans for a water department of Seoul City by benchmarking it with selected best practices of the world. At first a performance indicator system was developed to logically evaluate concerns, problems, and issues of the Seoul water department. Since it is typical of Korean water services providers, the Seoul water department was selected while Tokyo's in Japan and Denver's in America were selected since they were considered as best practices of public waterworks much similar in governing structure to the one in Seoul. The results of benchmarking concluded that Seoul has traditionally been emphasizing more on being a good public servant providing drinking water services, and should be reformed in directions emphasizing 'entrepreneurship' to overcome its current limitations. Based on the results, a road map to reform the Seoul water department was established. Since it is typical in Korea, the reform plans and road map established were further recommended for reforming other drinking water services providers in Korea.

/pdf/a-benchmarking-study-for-reforming-a-korean-water-department-4qh31aglmk.pdf

A benchmarking study for reforming a Korean water department

When one forecasts urban water demand in a long-term, multivariate model can give more benefits thanper capita requirement model However, the former has shortcomings in that statistically high explanatorypower cannot be obtained well, and change in customer behavior cannot be considered If the past waterconsumption effects the future water demand, dynamic model may describe real water consumption databetter than static model, ie the existing multivariate model On these grounds, this study built dynamicmodel using system dynamics From a case study in Seoul and Busan city, dynamic model was expected toforecast water demand more descriptively and reliably

Dynamic Model of a Long-term Water Demand Using System Dynamics

This study was prepared to examine the trend of the Minje family of the Yeo Heung-Min family in the late Goryeo and early Joseon Dynasty. The Minje family continued to serve as servants from the end of the Goryeo Dynasty and produced queens and meritorious subjects even after the founding of the Joseon Dynasty. The Minje family provides an example of the background and existence of the ruling power during the period of change of dynasty. In this paper, paying attention to these points, I examined Minje's personal connections and activities. During the period of change of dynasty, Minje cooperated with the Lee Seong faction and formed a relationship with the forces opposing the establishment of the country. Political activity was based on these relationships. After King Taejong ascended the throne, Minje emerged as a talented person who produced queens and public servants. In addition, the Minje family, which had ties with major political figures, exercised influence over the royal family and politics. This purge is not simply to get rid of Min, who is related by blood. Taejong purged the Min brothers to prevent the monopoly of certain forces in politics. As a result, the Min family moved away from politics.

Political Trends of the Yeongheung-Min Clan Minje Family

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