![]() ![]() ![]() As a significant example in this direction, we show that our relation implies a trade-off between recall and precision under certain conditions on the accuracy. Our relation provides an analytical tool that promises to be useful in theoretical and applied work in information retrieval. In the present paper we present for the first time a general mathematical relation linking classification accuracy with precision and recall. On the other hand, the field of information retrieval has two classical performance evaluation metrics: precision, the fraction of the items retrieved by the system that are interest-ing to the user, and recall, the fraction of the items of interest to the user that are retrieved by the system. ![]() A natural performance metric in this context is classification accuracy, defined as the fraction of the system's interesting/uninteresting predictions that agree with the user's assessments. The results encourage further analysis on how mining techniques could contribute to further our understanding of lightning dependencies on atmospheric parameters.įrom a machine learning perspective, information retrieval may be viewed as a problem of classifying items into one of two classes corresponding to interesting and uninteresting items respectively. Apart from discussing the prediction skill of the model, data mining techniques are also used to compare the patterns of data distribution, both spatially and temporally among the stations. The model also improves upon three competitive baselines for generating lightning warnings: (i) a simple but objective baseline forecast, based on the persistence method, (ii) the widely-used method based on a threshold of the vertical electrostatic field magnitude at ground level, and, finally (iii) a scheme based on CAPE threshold. Furthermore, the importance of the input parameters fits with the broad physical understanding of surface processes driving thunderstorms (e.g., the surface temperature and the relative humidity will be important factors for the instability and moisture availability of the thunderstorm environment). Evaluation results show that the model has statistically considerable predictive skill for lead times up to 30 min. The produced warnings are validated using the data from lightning location systems. We developed a four-parameter model based on four commonly available surface weather variables (air pressure at station level (QFE), air temperature, relative humidity, and wind speed). We have used machine learning techniques to successfully hindcast nearby and distant lightning hazards by looking at single-site observations of meteorological parameters. Knowledge discovery and data mining methods can be used for seeking characteristics of data and their teleconnections in complex data clusters. Lightning discharges in the atmosphere owe their existence to the combination of complex dynamic and microphysical processes. Lastly, future research on the development of lightning-safe rural dwellings or shelters, especially in lightning prone areas, is needed. Investigations into determining the most effective way to utilise existing monitoring networks – but with warning dissemination to rural communities – are also required. We recommend a call for the integration of indigenous and scientific knowledge as well as for the development of a participatory early warning system. A large proportion of the population of African countries resides in rural areas, where citizens participate in subsistence farming, and built infrastructure is not lightning safe. However, despite these developments, rural communities in South Africa, and indeed in Africa, remain vulnerable to lightning, the occurrence of which is predicted to increase with climate change. South Africa has made considerably more progress in the field of lightning research than other African countries and possesses one of the three ground-based lightning detection networks in the southern hemisphere. ![]() Technological advances have contributed towards improving lightning detection and monitoring activities in many countries. The latest available lightning detection techniques and technologies are reviewed and include current research in South Africa and South Africa’s lightning detection challenges. In comparison to the rest of the world, South Africa has one of the highest incidences of lightning-related injuries and deaths. Globally, lightning causes significant injury, death, and damage to infrastructure annually. ![]()
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