Identification of dry and rainy periods using telecommunication ...

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12 nd International Conference on Urban Drainage, Porto Alegre/Brazil, 10-15 September 2011 Figure 1 The pre-processing of received signal level (RSL) from Microwave Links (MWL) decomposes the RSL into the baseline (B) and the rain-induced attenuation (Atot). Pre-processing with an online-algorithm (left) only uses past information, but can be applied in real-time data analysis and operation. The corresponding offline algorithm uses both past and future observations. In this study, we therefore developed three novel methods to classify every measurement of the signal strength as either belonging to wet or dry periods: i) a moving window algorithm, ii) a statistical classification algorithm using random forests and iii) an algorithm based on a Gaussian factor graph, which is a rather novel signal-processing method. Our main innovations are that we suggest algorithms for offline and online data processing, compare their performance to that of others published algorithms in literature and investigate how the different methods cope with different data qualities, temporal and power resolution. Our results show minimal classification errors of precipitation for the random forest, although each algorithm has its pros and cons. Interestingly, the major influence factor is the quantization of the MWL signal. As expected, the results are very much dependent on the training procedure and data of the algorithms. CASE STUDY AREA IN ZURICH, CH MWL network: For our study, 14 links from a telecommunication network in the region of Zurich were available (Figure 2). For details see Rieckermann et al. (2009). The RSL was recorded as the instantaneous power from ten MWL with a “fine” quantization of 0.1 [dBm] and four with a “coarse” one (1 [dBm]: No. 2, 3, 6, 11) and a temporal resolution of about 2.5 minutes. The RSL were recorded from April 2009 until May 2010. Rain gauges: Eleven rain gauges are present in the study area and the data were obtained from the local sewer operator, ERZ, and MeteoSwiss. We aggregated the tipping bucket recordings (volume: 0.1 mm) over ten minutes to account for the spatial and temporal variability of rainfall with regard to the path-averaged values of the MWL. This way, the measurable intensities were multiples of 0.6 mm/h. To evaluate the performance of the algorithms, we used the rain gauge closest to an individual MWL as the ground truth. This is naturally not exact, because, in addition to measurement errors, the measurements of point rainfall from a gauge do not correspond to the path-average rain intensities from MWLs. However, absolute values are not our primary interest in this study. Also, one rain gauge represents from less than 1 kilometre to up to 8 kilometres of MWL length, which is in the lower range of the values found in the literature (Berne and Uijlenhoet, 2007; Leijnse et al., 2007; Zinevich et al., 2008). Weather radar: Radar data are provided by MeteoSwiss (SMA) as a composite of the three Page 2 of 12
12 nd International Conference on Urban Drainage, Porto Alegre/Brazil, 10-15 September 2011 Figure 2 Location of the selected ORANGE microwave links and the location of the available rain gauges. weather radars in Switzerland (Germann et al., 2006). We deliberately chose not to use the radar data as ground truth in our study, because data from weather radars are rather uncertain (Krämer et al., 2005; Upton et al., 2005). However, to test their usefulness to inform the online and offline classification algorithms (see below), we filtered the data and set all radar measurements below 0.6 mm/h to zero. CLASSIFYING RECEIVED SIGNAL LEVELS INTO WET AND DRY PERIODS The moving window algorithm and its modification Schleiss and Berne (2010) proposed a method to separate dry from rainy periods based on the assumption that, during dry periods, the standard deviation of the received signal level is bounded by some constant value that does not change with time and only depends on the physical characteristics (i.e., frequency, length, type of antenna) of the MWL (Schleiss and Berne, 2010). This leads to a simple decision rule: IF (σ(t)) > σ0 THEN “Wet” (W) ELSE “Dry” (D), where σ(t) is the standard deviation of the received signal level RSL(t) in the moving window Wt=[t-w, t], w>0, and σ0 is a threshold parameter that has to be estimated from the data. For our investigation, we chose a length of Wt, �Wt = { 20, 30 min}. The threshold σ0 was computed from previously recorded data. Typically, several months of previously recorded data are required, because the fraction of rainy periods is small: σ0= q1-r{�ˆ (t)} where q is the quantile and r is the fraction of rainy periods, which we determined to 7.2% based on SMA rain data from May to December 2009. The attenuation baseline B(t) is then estimated in real-time using the following algorithm: (1): For each time index t � D, set B(t)= RSLWt (2): For each time index t � W, set B(t)=B(t-k), where k is the smallest value such that tk � D. We label this original algorithm by Schleiss and Berne (2010) “S1a” for �Wt= 20 min and “S1b” for �Wt= 30 min. We found that S1a and S1b show quite high class errors for the W category, which is probably because the temporal resolution of the RSL used in the original method was 6 seconds, while we have a typical resolution of two or three minutes. To improve the detection of rain, we thus modified the above decision rule by introducing an additional threshold for the mean of the moving window, mean(RSLWt) and �Wt= 20 min (S2). Page 3 of 12
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