Sánchez-Medina Javier J, Guerra-Montenegro Juan Antonio, Sánchez-Rodríguez David, Alonso-González Itziar G, Navarro-Mesa Juan L
Centro de Innovación para la Sociedad de la Información (CICEI), Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain.
Instituto Universitario para el Desarrollo Tecnológico y la Innovación en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain.
Sensors (Basel). 2019 May 24;19(10):2388. doi: 10.3390/s19102388.
The Canary Islands are a well known tourist destination with generally stable and clement weather conditions. However, occasionally extreme weather conditions occur, which although very unusual, may cause severe damage to the local economy. The ViMetRi-MAC EU funded project has among its goals, managing climate-change-associated risks. The Spanish National Meteorology Agency (AEMET) has a network of weather stations across the eight Canary Islands. Using data from those stations, we propose a novel methodology for the prediction of maximum wind speed in order to trigger an early alert for extreme weather conditions. The methodology proposed has the added value of using an innovative kind of machine learning that is based on the data stream mining paradigm. This type of machine learning system relies on two important features: models are learned incrementally and adaptively. That means the learner tunes the models gradually and endlessly as new observations are received and also modifies it when there is concept drift (statistical instability), in the modeled phenomenon. The results presented seem to prove that this data stream mining approach is a good fit for this kind of problem, clearly improving the results obtained with the accumulative non-adaptive version of the methodology.
加那利群岛是著名的旅游胜地，气候总体稳定宜人。然而，偶尔也会出现极端天气状况，尽管非常罕见，但可能会对当地经济造成严重破坏。由欧盟资助的ViMetRi-MAC项目的目标之一是应对与气候变化相关的风险。西班牙国家气象局（AEMET）在加那利群岛的八个岛屿上设有气象站网络。利用这些气象站的数据，我们提出了一种预测最大风速的新方法，以便对极端天气状况发出早期警报。所提出的方法具有使用基于数据流挖掘范式的创新型机器学习的附加价值。这种类型的机器学习系统依赖于两个重要特性：模型是逐步和自适应学习的。这意味着学习者会随着新观测数据的接收逐渐且持续地调整模型，并且当建模现象中出现概念漂移（统计不稳定）时也会对其进行修改。所呈现的结果似乎证明这种数据流挖掘方法非常适合此类问题，明显改善了采用该方法的累积非自适应版本所获得的结果。
