Neural networks and non-parametric methods for improving real-time flood forecasting through conceptual hydrological models

International audienceTime-series analysis techniques for improving the real-time flood forecasts issued by a deterministic lumped rainfall-runoff model are presented. Such techniques are applied for forecasting the short-term future rainfall to be used as real-time input in a rainfall-runoff model and for updating the discharge predictions provided by the model. Along with traditional linear stochastic models, both stationary (ARMA) and non-stationary (ARIMA), the application of non-linear time-series models is proposed such as Artificial Neural Networks (ANNs) and the ?nearest-neighbours' method, which is a non-parametric regression methodology. For both rainfall forecasting and discharge updating, the implementation of each time-series technique is investigated and the forecasting schemes which perform best are identified. The performances of the models are then compared and the improvement in the efficiency of the discharge forecasts achievable is demonstrated when i) short-term rainfall forecasting is performed, ii) the discharge is updated and iii) both rainfall forecasting and discharge updating are performed in cascade. The proposed techniques, especially those based on ANNs, allow a remarkable improvement in the discharge forecast, compared with the use of heuristic rainfall prediction approaches or the not-updated discharge forecasts given by the deterministic rainfall-runoff model alone

Mutlivariate Labeled Cartograms

We combine the approaches of typographic variation in labels with cartograms to create multivariate labelled cartograms. These multivariate labelled cartograms can scale to a large number of entities and a large number of data attributes. We also introduce techniques of positional and proportional encoding which apply attributes to only portions of labels thereby aiding the encoding of many data attributes or quantitative data

Stem and leaf plots extended for text visualizations

© 2017 IEEE. Stem and leaf plots are data dense visualizations that organize large amounts of micro-level numeric data to form larger macro-level visual distributions. These plots can be extended with font attributes and different token lengths for new applications such as n-grams analysis, character attributes, set analysis and text repetition

Typographic sets: Labeled set elements with font attributes

We show that many different set visualization techniques can be extended with the addition of labeled elements using font attributes. Elements labeled with font attributes can: uniquely identify elements; encode membership in ten sets; use size to indicate proportions among set relations; can scale to thousands on clearly labeled elements; and use intuitive mappings to facilitate decoding. The approach can be applied to many different set visualization layouts, including Venn and Euler diagrams, graphs, mosaic plots and cartograms

Font attributes enrich knowledge maps and information retrieval: Skim formatting, proportional encoding, text stem and leaf plots, and multi-attribute labels

© 2016 The Author(s)Typography is overlooked in knowledge maps (KM) and information retrieval (IR), and some deficiencies in these systems can potentially be improved by encoding information into font attributes. A review of font use across domains is used to itemize font attributes and information visualization theory is used to characterize each attribute. Tasks associated with KM and IR, such as skimming, opinion analysis, character analysis, topic modelling and sentiment analysis can be aided through the use of novel representations using font attributes such as skim formatting, proportional encoding, textual stem and leaf plots and multi-attribute labels

Using font attributes in knowledge maps and information retrieval

Font specific attributes, such as bold, italic and case can be used in knowledge mapping and information retrieval to encode additional data in texts, lists and labels to increase data density of visualizations; encode data quantitative data into search lists; and facilitate text skimming and refinement by visually promoting of words of interest

Multivariate label-based thematic maps

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal if Cartography on 23 March 2017, available online: http://www.tandfonline.com/10.1080/23729333.2017.1301346. The rich history of cartography and typography indicates that typographic attributes, such as bold, italic and size, can be used to represent data in labels on thematic maps. These typographic attributes are itemized and characterized for encoding literal, categorical and quantitative data. Label-based thematic maps are shown, including examples that scale to multiple data attributes and a large number of entities. Multiple approaches to handle long labels are considered. Positional and proportional encoding apply attributes to portions of labels for encoding a large number of data attributes or quantitative values. Quantitative evaluation indicates label-based thematic maps may outperform choropleth maps for some tasks. Qualitative evaluation provides guidance for design considerations

Stem & Leaf Plots Extended to Various Ranges of Text

Stem and leaf plots are data dense visualizations that organize large amounts of micro-level numeric data to form larger macro-level visual distributions. These plots can be extended with font attributes and different token lengths for new applications such as n-grams analysis, character attributes, set analysis and text repetition

Evaluation of Visualization by Critiques

In this position paper, we extend design critiques as a form of evaluation to visualization, specifically focusing on unique qualities of critiques that are different than other types of evaluation by inspection, such as heuristic evaluation, models, reviews or written criticism. Critiques can be used to address a broader scope and context of issues than other inspection techniques; and utilize bi-direction dialogue with multiple critics, including non-visualization critics

Using text in visualizations for micro/macro readings

This paper presents techniques for creating micro/macro encoding in text visualization using text and font-attributes. Alphanumeric Marks increase information density in standard plots using alphanumeric markers to provide additional micro-level information. In-Context Representations layer additional macro-level data into traditional text lists and blocks using font-based attributes to make high-level patterns easily perceivable. This is a work in progress with novel design contributions regarding generalized use of text and font attributes in visualization