scholarly journals Application of Benchmarking and Principal Component Analysis in Measuring Performance of Public Irrigation Schemes in Kenya

2018 ◽  
Author(s):  
Faith Muema ◽  
Patrick G. Home ◽  
James M. Raude
Keyword(s):  
Principal Component Analysis ◽  
Water Supply ◽  
Financial Performance ◽  
Water Use ◽  
Agricultural Productivity ◽  
Principal Component ◽  
Component Analysis ◽  
Irrigation Scheme ◽  
Western Kenya ◽  
Overall Performance

The Inefficient water use, varying and low productivity in Kenya public irrigation schemes is a major concern.  It is therefore necessary to periodically monitor and evaluate the performance of public irrigation schemes. The performance of public irrigation in western Kenya was assessed by combining benchmarking methodology and principal component analysis. The aim was to quantify and rank the performance of pumped public irrigation schemes in Kenya. Eleven benchmarking indicators were computed for the period from 2012 to 2016 and compared to global benchmark values. The indicators used fall under agricultural productivity, water supply and financial performance categories. The computed agricultural productivity was 36%–51% in Ahero, 23%–42% in West Kano and 26%–50% Bunyala irrigation scheme. Water supply performance in Ahero, West Kano and Bunyala irrigation schemes varied from 24% to 58%, 3% to 49% and 19% to 43% respectively. Financial performance varied from 46% to 54% in Ahero, 25% to 32% in West Kano and 54%–56% in Bunyala irrigation scheme. An average overall performance efficiency of 46%, 39% and 31% was obtained in Ahero, Bunyala and West Kano irrigation schemes respectively. The performance of the irrigation schemes is very poor and measures on improving performance are needed.

scholarly journals Application of Benchmarking and Principal Component Analysis in Measuring Performance of Public Irrigation Schemes in Kenya

Agriculture ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 162 ◽  
Author(s):  
Faith Muema ◽  
Patrick Home ◽  
James Raude
Keyword(s):  
Principal Component Analysis ◽  
Performance Indicators ◽  
Principal Component ◽  
Component Analysis ◽  
Performance Score ◽  
Irrigation Scheme ◽  
Western Kenya ◽  
Average Performance ◽  
Management Measures ◽  
Linear Aggregation

The inefficient water use, and variable and low productivity in Kenyan public irrigation schemes is a major concern. It is, therefore, necessary to periodically monitor and evaluate the performance of public irrigation schemes. This prompted evaluation of performance of three rice growing irrigation schemes in western Kenya using benchmarking and principal component analysis. The aim of the study was to quantify and rank the performance of selected irrigation schemes. The performance of the irrigation schemes was evaluated for the period from 2012 to 2016 using eleven performance indicators under agricultural productivity, water supply and financial performance categories. The performance indicators were weighted using principal component analysis and combined to form a single performance score using linear aggregation method. The average performance in the Ahero, West Kano and Bunyala irrigation schemes was 48%, 49% and 56%, respectively. Based on performance score, the Bunyala irrigation scheme is the highest performing rice irrigation scheme in western Kenya. The three irrigation schemes have an average performance. Operation and management measures to improve the current performance of the irrigation schemes are needed.

Support Vector Regression for Missing Data Estimation

2010 ◽  
pp. 117-141
Author(s):  
Tshilidzi Marwala
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Overall Performance ◽  
Accuracy Measures ◽  
Missing Variables ◽  
Missing Data Estimation ◽  
Data Estimation

This chapter develops and compares the merits of three different data imputation models by using accuracy measures. The three methods are auto-associative neural networks, a principal component analysis and support vector regression all combined with cultural genetic algorithms to impute missing variables. The use of a principal component analysis improves the overall performance of the auto-associative network while the use of support vector regression shows promising potential for future investigation. Imputation accuracies up to 97.4% for some of the variables are achieved.

The Comprehensive Evaluation on Financial Performance of the Ceramic Company Based on Principal Component Analysis

2012 ◽  
Vol 512-515 ◽  
pp. 1098-1102 ◽  
Author(s):  
Bao Cheng He ◽  
Hong Tao Jiang
Keyword(s):  
Principal Component Analysis ◽  
Financial Performance ◽  
Performance Indicator ◽  
Comprehensive Evaluation ◽  
Principal Component ◽  
Component Analysis ◽  
Indicator System ◽  
Financial Report ◽  
Components Method ◽  
The Impact

The ceramic company financial performance indicator has multilayers, many dimensions and intersect characters, and the comprehensive evaluation on it is a big challenge. Firstly, this paper introduces principal component analysis theory and tool. Secondly, it constructs the four-dimensional evaluating indicator system based on “debt-paying ability, operation capability, profitability, and development capability”. In the end, based on the financial report data, this paper conducts an empirical principal component analysis on 20 typical ceramic enterprises’ financial performance. The conclusion is that the ceramic business finance performance is mainly decided by four greatest factors including “debt-paying ability, profitability, efficiency, development” and the impact of the four greatest factors upon the finance performance degree is different. This research’s innovation lies in using the principal components method to give the weight to the evaluating indicators objectively, providing not only the new tool for the ceramic enterprise financial performance assessment method’s evolution, but also the new mentality for the ceramic enterprise financial performance’s improvement.

scholarly journals THE IMPACT OF REAL ESTATE INVESTMENTS ON THE PERFORMANCE OF THE ENTITIES LISTED AT THE BVB

2021 ◽  
Vol 30 (30 (1)) ◽  
pp. 177-186
Author(s):  
Silviu Cornel Virgil Chiriac
Keyword(s):  
Principal Component Analysis ◽  
Real Estate ◽  
Financial Performance ◽  
Descriptive Analysis ◽  
Composite Index ◽  
Principal Component ◽  
Component Analysis ◽  
Multidimensional Data ◽  
Data Set ◽  
The Impact

The current paper is part of a wider study which aims at identifying the determining factors of the performances of the entities in the real estate field and the setting up of a composite index of the companies’ performances based on a sample of 29 companies listed at the BVB Bucharest (Bucharest Stock Exchange) in the year 2019 using one of the multidimensional data analysis techniques, the principal component analysis. The descriptive analysis, the principal component analysis for setting up the composite index of the companies performances were applied within the study in order to highlight the most important companies from the point of view of the financial performance. The descriptive analysis of the data set highlights the overview within the companies selected for analysis. The study aims at building a synthetic indicator that will show the financial performance of the companies selected based on 9 financial indicators using the principal component analysis PCA. The 9 indicators considered for the analysis were selected based on specialised articles and they are: ROA – return on assets, which reflect the company’s capacity of using its assets productively, ROE – return on equity, which measures the efficiency of use of the stockholders’ capitals, rotation of total assets, general liquidity ratio, general solvency ratio, general dent-to-equity level, net profit margin, gross return of portfolio.

scholarly journals Key Determinants of Indonesia’s Banks Financial Performance

2019 ◽  
Vol 21 (2) ◽  
pp. 58-67
Author(s):  
Martin Panggabean ◽  
Stefan Batara Panggabean
Keyword(s):  
Principal Component Analysis ◽  
Financial Performance ◽  
Total Variation ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Financial Indicators ◽  
Balance Sheets ◽  
Conventional Banks

Depositors, investors, as well as public in general need easily accessible indicators that are important to differentiate various banks. This research addresses simultaneously two important issues: analyzing and identifying which key publicly available financial indicators of banks are important, as well as approximating the weight of the aforementioned indicators when banks’ comparisons are to be made. Utilizing the recent 2017 database from 90 conventional banks, this study analyzes 17 banking ratios using the method of principal component analysis. The calculations show that five components explain around 75 percent of total variation in the data. Those five components represent indicators on profitability, quality of capital, quality of loans, fee-based activities, and liquid assets in the balance sheets. Further, by combining five principal components, the result shows that even small banks can achieve good financial performances.

Using multivariate principal component analysis of injected water flows to detect anomalous behaviors in a water supply system - a case study

2004 ◽  
Vol 4 (3) ◽  
pp. 169-182 ◽  
Author(s):  
C.V. Palau ◽  
F. Arregui ◽  
A. Ferrer
Keyword(s):  
Principal Component Analysis ◽  
Water Supply ◽  
Control Charts ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Technique ◽  
Supply System ◽  
Water Supply System ◽  
Multivariate Statistical ◽  
Significant Information

The amount of data collected by the SCADA (supervisory control and data acquisition) of an urban water supply system is sometimes difficult to process. A multivariate statistical technique, Principal Component Analysis (PCA) is presented in this paper, which processes this data, simplifying and synthesizing the most significant information. This technique extracts new variables, principal components (PC), that explain the behaviour of injected flow. Multivariate control charts to detect outliers show higher sensitivity than those generated with traditional univariate statistical methods.

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