Segmentation of Residential Customer Load Profile in Peninsular Malaysia using Jenks Natural Breaks

Khamis, Nurhidayah; Sin, Tan Ching; Hock, Goh Chin

doi:10.1109/pecon.2018.8684113

Cited by 24 publications

(12 citation statements)

References 4 publications

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“…Figure 3 shows a DI graph with influence/dependence areas according to JNB classification and by quadrants as established by [16]. When applying the JNB classification, the goodness of variance fit (GVF) was also determined, reaching a value of 0.9206, being close to 1 the adjustment of the classification is good [48].…”

Section: Key System Variablesmentioning

confidence: 91%

“…The resulting variables, such as bare ground cover, were shown to have high dependence and relative less influence, them strongly depend on the input and link variables, and their effects on the other variables are minimal. When applying the JNB classification, the goodness of variance fit (GVF) was also determined, reaching a value of 0.9206, being close to 1 the adjustment of the classification is good [48].…”

Section: Key System Variablesmentioning

confidence: 96%

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Territorial Prospective to Sustainability: Strategies for Future Successful of Water Resource Management on Andean Basins

Mera-Parra

Massa-Sánchez

Oñate-Valdivieso

et al. 2022

Land

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In Latin America, water resource management in some areas is difficult when all parts of a system are not considered (including its dynamism). Therefore, it becomes necessary to prepare instruments that facilitate management using a comprehensive approach. This study aimed to develop a methodology that allows one to conduct a prospective analysis of water management over delimited territories. The Zamora Huayco basin was chosen as the study area. This work included a survey of physical-natural, socioeconomic, and political-institutional variables, as well as a system structural analysis. Also, the generation of future scenarios and the strategic and tactical orientation for the integrated management of water resources. The results show that, of the 23 variables used, 19 were classified as key system variables. Most of the variables had strong impacts on each other, but at the same time these were highly receptive to changes. The behavior of change, proposed for the different uses and land cover in the basin for 2029, was considered as the objective scenario, highlighting the gain in forest areas and shrub vegetation. The strategic plans proposed in this methodology consider the structuring and collecting information in a single repository, creating communication channels between stakeholders and decision-makers.

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Section: Key System Variablesmentioning

confidence: 91%

Section: Key System Variablesmentioning

confidence: 96%

Territorial Prospective to Sustainability: Strategies for Future Successful of Water Resource Management on Andean Basins

Mera-Parra

Massa-Sánchez

Oñate-Valdivieso

et al. 2022

Land

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show abstract

“…To determine the optimal number of breaks, various break counts were trialed in ascending order, starting with two, on each iteration measuring the Goodness of Variance Fit (GVF). GVF is a value between 0 and 1 and indicates how well the categories produced by the Fischer-Jenks algorithm reflect the ''natural breaks'' in the data (Khamis et al 2018). This iterative process continues until the number of breaks associated with a GVF value beyond a certain threshold is reached, in this case, the value chosen was 0.85.…”

Section: Preprocessingmentioning

confidence: 99%

On the prediction of landslide occurrences and sizes via Hierarchical Neural Networks

Aguilera

Lombardo

Tanyaş

et al. 2022

Stoch Environ Res Risk Assess

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For more than three decades, the part of the geoscientific community studying landslides through data-driven models has focused on estimating where landslides may occur across a given landscape. This concept is widely known as landslide susceptibility. And, it has seen a vast improvement from old bivariate statistical techniques to modern deep learning routines. Despite all these advancements, no spatially-explicit data-driven model is currently capable of also predicting how large landslides may be once they trigger in a specific study area. In this work, we exploit a model architecture that has already found a number of applications in landslide susceptibility. Specifically, we opt for the use of Neural Networks. But, instead of focusing exclusively on where landslides may occur, we extend this paradigm to also spatially predict classes of landslide sizes. As a result, we keep the traditional binary classification paradigm but we make use of it to complement the susceptibility estimates with a crucial information for landslide hazard assessment. We will refer to this model as Hierarchical Neural Network (HNN) throughout the manuscript. To test this analytical protocol, we use the Nepalese area where the Gorkha earthquake induced tens of thousands of landslides on the 25th of April 2015. The results we obtain are quite promising. The component of our HNN that estimates the susceptibility outperforms a binomial Generalized Linear Model (GLM) baseline we used as benchmark. We did this for a GLM represents the most common classifier in the landslide literature. Most importantly, our HNN also suitably performed across the entire procedure. As a result, the landslide-area-class prediction returned not just a single susceptibility map, as per tradition. But, it also produced several informative maps on the expected landslide size classes. Our vision is for administrations to consult these suite of model outputs and maps to better assess the risk to local communities and infrastructure. And, to promote the diffusion of our HNN, we are sharing the data and codes in a githubsec repository in the hope that we would stimulate others to replicate similar analyses.

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“…The inconsistencies among the obtained ACD values are identified using a clustering method called Jenks Natural Breaks algorithm [20]. This algorithm is more suitable for univariate data and finds the best way to categorize the values.…”

Section: Figure 4 Multipath Communicationmentioning

confidence: 99%

“…The Jenks Natural Breaks algorithm is a standard method for dividing a dataset into a certain number of homogenous classes. The classification is achieved by maximizing the variance between classes and minimizing the variance within classes [20], [21]. The nodes causing inconsistent distance estimations are filtered out from the localization process.…”

Section: Introductionmentioning

confidence: 99%

A Method for Fault Tolerant Localization of Heterogeneous Wireless Sensor Networks

Bhat

Santhosh

2021

IEEE Access

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Location information of nodes in Wireless Sensor Networks (WSN) is essential to identify the origins of events and to act on them. Several localization algorithms are developed for this purpose. In this work, we have considered hop based localization algorithms, which are popularly used in WSN applications. These algorithms use a few reference nodes with location information and localize other nodes with reference to these nodes. But, in practical scenarios, some reference nodes may turn faulty and report incorrect location information to other nodes. This reduces the localization accuracy of the entire network. Therefore, it is essential to identify and filter out faulty reference nodes from the localization process. But, in Heterogeneous Wireless Sensor Networks (HWSN), since both faulty nodes and heterogeneous nodes modify hop distances, it becomes even more challenging to identify only faulty nodes among a set of heterogeneous nodes. In this work, we have reported a fault filtering method that can be used with any of the existing hop based localization algorithms for fault-tolerant localization. This method first normalizes the distance estimations using the communication radius of nodes and then uses the Jenks Natural Breaks algorithm for filtering out the nodes producing inconsistent distance estimations. The reported method is incorporated into existing localization algorithms and tested in 2D/3D, isotropic/anisotropic environments. The results show an improvement of 14%, 52%, and 51% in localization accuracy when tested with DV-Hop, Weighted DV-Hop, and HHO-AM algorithms, respectively.

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Segmentation of Residential Customer Load Profile in Peninsular Malaysia using Jenks Natural Breaks

Cited by 24 publications

References 4 publications

Territorial Prospective to Sustainability: Strategies for Future Successful of Water Resource Management on Andean Basins

Territorial Prospective to Sustainability: Strategies for Future Successful of Water Resource Management on Andean Basins

On the prediction of landslide occurrences and sizes via Hierarchical Neural Networks

A Method for Fault Tolerant Localization of Heterogeneous Wireless Sensor Networks

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