scholarly journals Shannon Entropy for Measuring Spatial Complexity Associated with Mean Annual Runoff of Tertiary Catchments of the Middle Vaal Basin in South Africa

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 366 ◽  
Author(s):  
Masengo Ilunga
Keyword(s):  
South Africa ◽  
Information Gain ◽  
Annual Runoff ◽  
Data Sets ◽  
Information Redundancy ◽  
Spatial Complexity ◽  
Surface Water Resources ◽  
Gain Loss ◽  
Relative Change ◽  
Hydrological Phases

This study evaluates essentially mean annual runoff (MAR) information gain/loss for tertiary catchments (TCs) in the Middle Vaal basin. Data sets from surface water resources (WR) of South Africa 1990 (WR90), 2005 (WR2005) and 2012 (WR2012) referred in this study as hydrological phases, are used in this evaluation. The spatial complexity level or information redundancy associated with MAR of TCs is derived as well as the relative change in entropy of TCs between hydrological phases. Redundancy and relative change in entropy are shown to coincide under specific conditions. Finally, the spatial distributions of MAR iso-information transmission (i.e., gain or loss) and MAR iso-information redundancy are established for the Middle Vaal basin.

scholarly journals Tsallis Entropy for Assessing Spatial Uncertainty Associated with Mean Annual Runoff of Quaternary Catchments of the Middle Vaal Basin in South Africa

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1050
Author(s):  
Masengo Ilunga
Keyword(s):  
South Africa ◽  
Shannon Entropy ◽  
Annual Runoff ◽  
Spatial Uncertainty ◽  
Data Sets ◽  
Data Set ◽  
Information Index ◽  
Surface Water Resources ◽  
Convergence Point

This study assesses mainly the uncertainty of the mean annual runoff (MAR) for quaternary catchments (QCs) considered as metastable nonextensive systems (from Tsalllis entropy) in the Middle Vaal catchment. The study is applied to the surface water resources (WR) of the South Africa 1990 (WR90), 2005 (WR2005) and 2012 (WR2012) data sets. The q-information index (from the Tsalllis entropy) is used here as a deviation indicator for the spatial evolution of uncertainty for the different QCs, using the Shannon entropy as a baseline. It enables the determination of a (virtual) convergence point, zone of positive and negative uncertainty deviation, zone of null deviation and chaotic zone for each data set. Such a determination is not possible on the basis of the Shannon entropy alone as a measure for the MAR uncertainty of QCs, i.e., when they are viewed as extensive systems. Finally, the spatial distributions for the zones of the q-uncertainty deviation (gain or loss in information) of the MAR are derived and lead to iso q-uncertainty deviation maps.

Impact of Climate Change on Water Resources in the Shiyang River Basin and the Adaptive Measures for Energy Conservation and Emission Reduction

2013 ◽  
Vol 405-408 ◽  
pp. 2167-2171 ◽  
Author(s):  
Zhou Li ◽  
Xiao Yan Li ◽  
Juan Sun
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Resources ◽  
Energy Conservation ◽  
Climate Change Impacts ◽  
Warming Trend ◽  
Annual Runoff ◽  
Climate Scenarios ◽  
Surface Water Resources ◽  
Precipitation And Temperature

Climate is an important factor which formed and affected surface water resources. Through sensitivity analysis of natural runoff towards climate change, assuming the main factors effect runoff are precipitation and temperature, then according to the possible tendency of climate changes in the future, set climate scenarios, and use the hydrological model simulate the changes trend of runoff under different climate scenarios, thereby analyze the climate change impacts on surface water resources. The results show that annual runoff will be increased with the increasing annual precipitation, and it will be reduced with rise of annual temperature, the sensitivity that annual runoff towards the change of precipitation and temperature are equally notable, both of them are two major factors impact on the change of runoff and the precipitation change impacts on annual runoff will be even more obvious in flood season. Last, with the global warming trend, put forward the corresponding adaptive measures of energy conservation and emissions reduction。

Inverse Fractal Statistics in Turbulence and Finance

2003 ◽  
Vol 17 (22n24) ◽  
pp. 4003-4012 ◽  
Author(s):  
Mogens H. Jensen ◽  
Anders Johansen ◽  
Ingve Simonsen
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Exit Time ◽  
Optimal Investment ◽  
Financial Data ◽  
Data Sets ◽  
Investment Horizon ◽  
Scaling Exponents ◽  
Fractal Statistics ◽  
Gain Loss

We consider inverse statistics in turbulence and financial data. By inverse statistics, also sometimes called exit time statistics, we "turn" the variables around such that the fluctuating variable becomes the fixed variable, while the fixed variable becomes fluctuating. In that sense we can probe distinct regimes of the data sets. In the case of turbulence, we obtain a new set of (multi)-scaling exponents which monitor the dissipation regime. In the case of economics, we obtain a distribution of waiting times needed to achieve a predefined level of return. Such a distribution typically goes through a maximum at a time called the optimal investment horizon[Formula: see text], since this defines the most likely waiting time for obtaining a given return ρ. By considering equal positive and negative levels of return, we report on a quantitative gain-loss asymmetry most pronounced for short horizons.

scholarly journals The economic impact of a rural land tax on selected commercial farms in Kwazulu-Natal, South Africa

2012 ◽  
Vol 11 (3) ◽  
pp. 372-387 ◽  
Author(s):  
MAG Darroch ◽  
RB Lee ◽  
GF Ortmann
Keyword(s):  
South Africa ◽  
Local Government ◽  
Economic Impact ◽  
Data Sets ◽  
Tax Rates ◽  
Rural Land ◽  
Tax Rate ◽  
Economic Profit ◽  
Kwazulu Natal ◽  
Commercial Farms

This study investigates the economic impact of a land tax implemented under the Local Government Municipal Property Rates Act No. 6 of 2004 on commercial farms using five case studies with five-year data sets in the Mtonjaneni and Umgeni municipal districts of KwaZulu-Natal. The case farms’ ability to pay annual rates between 0.25 per cent and 1 per cent of the value of improved land using real annual economic profit with and without rebates of up to 70 per cent proposed by the Department: Provincial and Local Government ranged from zero to five out of five years, with a mean of two out of five years. A 2 per cent land tax rate with such rebates could also be financed only in two out of five years on average. These results suggest that proposed annual land tax rates of 1.5 per cent (Mtonjaneni) or 1 per cent (Umgeni) on these specific farms would markedly reduce the incentive to invest in farm improvements

Investigating the effects of solar modelling using different solar irradiation data sets and sources within South Africa

Solar Energy ◽  
2012 ◽  
Vol 86 (9) ◽  
pp. 2354-2365 ◽  
Author(s):  
J. Dekker ◽  
M. Nthontho ◽  
S. Chowdhury ◽  
S.P. Chowdhury
Keyword(s):  
South Africa ◽  
Solar Irradiation ◽  
Data Sets

scholarly journals Applicability of CHIRPS-based satellite rainfall estimates for South Africa

2021 ◽  
Vol 63 (3) ◽  
pp. 1-12
Author(s):  
J A du Plessis ◽  
J K Kibii
Keyword(s):  
South Africa ◽  
Temporal Distribution ◽  
Pairwise Comparison ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
Good Precision ◽  
Alternative Source

Long-term rainfall data with good spatial and temporal distribution is essential for all climate-related analyses. The availability of observed rainfall data has become increasingly problematic over the years due to a limited and deteriorating rainfall station network, occasioned by limited reporting and/or quality control of rainfall and, in some cases, closure of these stations. Remotely sensed satellite-based rainfall data sets offer an alternative source of information. In this study, daily and monthly rainfall data derived from Climate Hazards Group InfraRed Precipitation (CHIRPS) is compared with observed rainfall data from 46 stations evenly distributed across South Africa. Various metrics, based on a pairwise comparison between the observed and CHIRPS data, were applied to evaluate CHIRPS performance in the estimation of daily and monthly rainfall. The results show that CHIRPS data correlate well with observed monthly rainfall data for all stations used, having an average coefficient of determination of 0.6 and bias of 0.95. This study concludes that monthly CHIRPS data corresponds well, with good precision and relatively little bias when compared to observed monthly rainfall data, and can therefore be considered for use in conjunction with observed rainfall data where no or limited data is available in South Africa for hydrological analysis.

Decision tree classification: Ranking journals using IGIDI

2019 ◽  
Vol 46 (3) ◽  
pp. 325-339
Author(s):  
Muhammad Shaheen ◽  
Tanveer Zafar ◽  
Sajid Ali Khan
Keyword(s):  
Decision Tree ◽  
Gini Index ◽  
Information Gain ◽  
Diversity Index ◽  
Selection Method ◽  
Attribute Selection ◽  
Data Sets ◽  
Average Value ◽  
Decision Tree Classification ◽  
Selection Measures

Selection of an attribute for placement of the decision tree at an appropriate position (e.g. root of the tree) is an important decision. Many attribute selection measures such as Information Gain, Gini Index and Entropy have been developed for this purpose. The suitability of an attribute generally depends on the diversity of its values, relevance and dependency. Different attribute selection measures have different criteria for measuring the suitability of an attribute. Diversity Index is a classical statistical measure for determining the diversity of values, and according to our knowledge, it has never been used as an attribute selection method. In this article, we propose a novel attribute selection method for decision tree classification. In the proposed scheme, the average of Information Gain, Gini Index and Diversity Index are taken into account for assigning a weight to the attributes. The attribute with the highest average value is selected for the classification. We have empirically tested our proposed algorithm for classification of different data sets of scientific journals and conferences. We have developed a web-based application named JC-Rank that makes use of our proposed algorithm. We have also compared the results of our proposed technique with some existing decision tree classification algorithms.

Unsupervised Feature Selection Based on Spectral Clustering with Maximum Relevancy and Minimum Redundancy Approach

2021 ◽  
Vol 35 (11) ◽  
pp. 2150031
Author(s):  
Bahareh Khozaei ◽  
Mahdi Eftekhari
Keyword(s):  
Feature Selection ◽  
Spectral Clustering ◽  
Information Gain ◽  
State Of The Art ◽  
Nearest Neighbors ◽  
Data Sets ◽  
Unsupervised Feature Selection ◽  
Significant Difference ◽  
Cluster A ◽  
Novel Approaches

In this paper, two novel approaches for unsupervised feature selection are proposed based on the spectral clustering. In the first proposed method, spectral clustering is employed over the features and the center of clusters is selected as well as their nearest-neighbors. These features have a minimum similarity (redundancy) between themselves since they belong to different clusters. Next, samples of data sets are clustered employing spectral clustering so that to the samples of each cluster a specific pseudo-label is assigned. After that according to the obtained pseudo-labels, the information gain of the features is computed that secures the maximum relevancy. Finally, the intersection of the selected features in the two previous steps is determined that simultaneously guarantees both the maximum relevancy and minimum redundancy. Our second proposed approach is very similar to the first one whose only but significant difference with the first method is that it selects one feature from each cluster and sorts all the features in terms of their relevancy. Then, by appending the selected features to a sorted list and ignoring them for the next step, the algorithm continues with the remaining features until all the features to be appended into the sorted list. Both of our proposed methods are compared with state-of-the-art methods and the obtained results confirm the performance of our proposed approaches especially the second one.

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