## 5 Experiments
### Results  
#### 5.5.1 Anomaly Detection Results  
In Figure 7, the location of buoys and path of Hurricane Katrina along with the time stamps are shown. The track of Katrina is plotted according to \"best track\" positions and intensities measured in every six hours, which starts on 00:00 UTC 25 August and ends on 18:00 UTC 29 August [13]. The cyclone became Katrina, the 11 th tropical storm of the 2005 Atlantic hurricane season, at 12:00 UTC 24 August when it was over the central Bahamas. Katrina has reached hurricane status near 21:00 UTC 25 August, less than two hours before its center made landfall on the southeastern coast of Florida. As it entered the Gulf of Mexico on 26 August, Katrina entered two rapid intensification periods between 26 and 28 August. Katrina strengthened from a low-end Category 3 hurricane to a Category 5 in less than 12 hours reaching the peak by 12:00 UTC 28 August. The large wind field on 28 August, with hurricane-force winds extended out to about 90 nautical miles from the center, made Katrina extremely intense and exceptionally large [13]. The center of Katrina passed by the buoy 43003 on 06:00 UTC 28 August, the buoy 42001 on 18:00 UTC 28 August, and the buoy 42040 on 09:00 UTC 29 August. The timestamps of top five scoring spatio-temporal anomalous data points detected by algorithms for buoys 42001, 42003, and 42040 are given in Table 2. The anomalies which are in space-time vicinity of the Hurricane Katrina are given in blue for performance comparison. The proposed framework (MC-CNN-LSTM) with window size 12 detected all spatio-temporal anomalies caused by Hurricane Katrina as it passed by buoys. When we examine the results, LDBSCAN seems to give the highest score to data points which are the most related with Hurricane Katrina path after our model, followed by the ST-Outlier detection algorithm, LOF and IsolationForest. Although this heuristic analysis gives a high-level understanding about performance of algorithms, it is better to take all data points and their scores into account for the duration of Hurricane Katrina. The actual Katrina path reported by [PERSON] et al. [13] gives latitude and longitude information of the center reported for every six hours. We have applied linear interpolation to transform it to an hourly basis data to match the buoys' hourly readings data. For each buoy whose location is highly relevant to Katrina's path, we have plotted the anomaly score of each data point against the distance to the center of the Katrina for the time frame starting on 00:00 UTC 25 August and ending on 23:59 UTC 29 August. In order to show comparison in detail, anomaly scores of the proposed framework (MC-CNN-LSTM), LDBSCAN and IsolationForest algorithms are plotted against the distance between buoys and the center of the Katrina in Figure 8. Anomaly score values and distances are min-max normalized in the range of [0, 1] before plotting. Figure 7: Buoy locations and path of Hurricane Katrina. In Figure 8, we can observe that the anomaly scores of LDBSCAN and IsolationForest are not stable and results contain many false positives and false negatives. For the LDBSCAN algorithm, false alarms based on generated anomaly scores are quite visible for buoys 42038, LONF1, and SANF1. For buoy 42038, LDBSCAN generates three highest scores for data points when Katrina center is not the closest. For buoys LONF1 and SANF1, LDBSCAN exhibits high anomaly score values followed by sharp drops which followed by high score values again for data points when Katrina center is very close. On the other hand, IsolationForest, which is the next best performing algorithm according to the results of quantitative evaluation given in Section 5.5.2, shows weaker performance than the proposed framework in capturing the anomalous trend in the test data. Its performance degrades significantly for buoys FWYF1, SANF1, and SMKF1.