Abstract
The sudden climate change, that has taken place in recent years, has generated calamitous phenomena linked to hydrogeological instability in many areas of the world. An accurate estimate of rainfall levels is fundamental in smart city application scenarios: it becomes essential to be able to warn of the imminent occurrence of a calamitous event and reduce the risk to human beings. Unfortunately, to date, traditional techniques for rainfall level estimation present numerous critical issues. This paper proposes a new approach to rainfall classification based on the LTE radio channel parameters adopted for the cell selection mechanism. In particular, this study highlights the correlation between the set of radio channel quality monitoring parameters and the relative rainfall intensity levels. Through a pattern recognition approach based on neural networks with Multi-Layer Perceptron (MLP), the proposed algorithm identifies five classes of rainfall levels with an average accuracy of 96 % and a F1 score of 93.6 %.
Highlights
Several smart environment applications have been introduced, including smart transportation [1], smart healthcare [2], smart homes [3] and smart cities [4], due to the rapid growth of urban populations
The degrees of linear separation were assessed using the Fischer Discriminant Ratio (FDR), which allows measuring the degree of linear separation that the given parameter has [35]
A multi-layer perceptron-type neural network (MLP) was applied, providing the parameters used for statistical analysis as an input, and the 5 classes of rainfall levels as an output
Summary
Several smart environment applications have been introduced, including smart transportation [1], smart healthcare [2], smart homes [3] and smart cities [4], due to the rapid growth of urban populations. The key enabler of these smart city applications is possibly the IoT (Internet of Things), which connects everyday objects and devices to network technologies. Today the advanced IoT (Internet of Things) sensing technologies cut across many areas of modern research, industry and daily life [5]–[7]. They facilitate detection, transmission and measurement of various environmental indicators. The data regarding weather information are significantly increasing at a rapid
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