Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens

The monitoring and forecasting of environmental conditions is a task to which much effort and resources are devoted by the scientific community and relevant authorities. Representative examples arise in meteorology, oceanography, and environmental engineering. As a consequence, high volumes of data are generated, which include data generated by earth observation systems and different kinds of models. Specific data models, formats, vocabularies and data access infrastructures have been developed and are currently being used by the scientific community. Due to this, discovering, accessing and analyzing environmental datasets requires very specific skills, which is an important barrier for their reuse in many other application domains. This paper reviews earth science data representation and access standards and technologies, and identifies the main challenges to overcome in order to enable their integration in semantic open data infrastructures. This would allow non-scientific information technology practitioners to devise new end-user solutions for citizen problems in new application domainsThis research was co-funded by (i) the TRAFAIR project (2017-EU-IA-0167), co-financed by the Connecting Europe Facility of the European Union, (ii) the RADAR-ON-RAIA project (0461_RADAR_ON_RAIA_1_E) co-financed by the European Regional Development Fund (ERDF) through the Iterreg V-A Spain-Portugal program (POCTEP) 2014-2020, and (iii) the Consellería de Educación, Universidade e Formación Profesional of the regional government of Galicia (Spain), through the support for research groups with growth potential (ED431B 2018/28)S

Landslide Prediction with Model Switching

Landslides could cause huge damages to properties and severe loss of lives. Landslides can be detected by analyzing the environmental data collected by wireless sensor networks (WSNs). However, environmental data are usually complex and undergo rapid changes. Thus, if landslides can be predicted, people can leave the hazardous areas earlier. A good prediction mechanism is, thus, critical. Currently, a widely-used method is Artificial Neural Networks (ANNs), which give accurate predictions and exhibit high learning ability. Through training, the ANN weight coefficients can be made precise enough such that the network works in analogy to a human brain. However, when there is an imbalanced distribution of data, an ANN will not be able to learn the pattern of the minority class; that is, the class having very few data samples. As a result, the predictions could be inaccurate. To overcome this shortcoming of ANNs, this work proposes a model switching strategy that can choose between different predictors, according to environmental states. In addition, ANN-based error models have also been designed to predict future errors from prediction models and to compensate for these errors in the prediction phase. As a result, our proposed method can improve prediction performance, and the landslide prediction system can give warnings, on average, 44.2 min prior to the occurrence of a landslide

具有模式切換之土石流預測;Landslide Prediction with Model Switching

[[abstract]]土石流會對生命與財產造成巨大的損失。在土石流預警系統中，可以通過分析無線傳感器網絡收集的環境數據來檢測土石流。然而，環境數據通常是複雜的並且變化快速。因此，如果可以提前預測到土石流發生，人們可以提前離開危險區域。因此，一個準確的預測方法至關重要。目前，廣泛使用的方法是人工神經網絡，它提供了準確的預測並表現出高學習能力。通過訓練網路，可以使人工神經網絡的權重係數精確到足以使網絡與人類大腦相似。然而，當訓練樣本的數據分佈不平衡時，人工神經網絡將無法學習少數類別的模式，即數據樣本很少的類別。因而，預測可能不准確。以土石流應用為例，少數類別通常為危險區段。為了克服人工神經網絡的這個缺點，本篇論文提出了一種模式切換策略，可以根據環境狀態在不同的神經網絡預測器之間進行選擇切換，來解決樣本數據分佈不平衡的問題。除此之外，我們建立了一個基於人工神經網絡的誤差模型，用以預測本文所提出的預測模型的未來誤差，並在預測階段補償這個誤差，使預測的結果更接近實際情況。實驗結果呈現本文所提出的方法可以提高預測效果，並且土石流預測系統可以在土石流發生前平均44.2分鐘發出警告。 Landslides could cause huge damages to properties and severe loss of lives. Landslides can be detected by analyzing the environment data collected via wireless sensor networks (WSN). However, environment data are usually complex and undergo rapid changes. Thus, if landslides can be predicted, people can leave the hazardous areas earlier. A good prediction mechanism is thus critical. Currently, a widely-used method is Artificial Neural Networks (ANNs), which give accurate predictions and exhibit high learning ability. Through training, the ANNs weight coefficients can be made precise enough so that the network works in analogy to a human brain. However, when we have an imbalanced distribution of data, ANNs will not be able to learn the pattern of minority class, that is, the class of very few data samples. As a result, the predictions could be inaccurate. To overcome this shortcoming of ANNs, this work proposes a model switching strategy that can choose between different predictors according to environmental states. In addition, we construct the ANNs based error model to predict the future errors of our proposed prediction model and compensate for these errors in the prediction phase. As a result, our proposed method can improve prediction performance, and the landslide prediction system can give warnings in an average of 44.2 minutes prior to landslide occurrence