A Deep Learning Model of Spatial Distance and Named Entity Recognition (SD-NER) for Flood Mark Text Classification

Information on historical flood levels can be communicated verbally, in documents, or in the form of flood marks. The latter are the most useful from the point of view of public awareness building and mathematical modeling of floods. Information about flood marks can be found in documents, but nowadays, they are starting to appear more often on the Internet. The only problem is finding them. The aim of the presented work is to create a new model for classifying Internet sources using advanced text analysis (including named entity recognition), deep neural networks, and spatial analysis. As a novelty in models of this type, it was proposed to use a matrix of minimum distances between toponyms (rivers and towns/villages) found in the text. The resulting distance matrix for Poland was published as open data. Each of the methods used is well known, but so far, no one has combined them into one ensemble machine learning model in such a way. The proposed SD-NER model achieved an F1 score of 0.920 for the binary classification task, improving the model without this spatial module by 17%. The proposed model can be successfully implemented after minor modifications for other classification tasks where spatial information about toponyms is important.

Automated summary

Information on historical flood levels can be communicated verbally, in documents, or in the form of flood marks. The aim of the presented work is to create a new model for classifying Internet sources using advanced text analysis, neural networks, and spatial analysis. The proposed model achieved a high F1 score for the binary classification task, improving the results by utilizing spatial information about toponyms.