Interceptor drainage modelling to manage high groundwater table on the Abyek Plain, Iran

High groundwater tables and soil salinity are a serious threat to agricultural areas, especially on the Abyek Plain, Iran. An interceptor drainage system was installed to lower the groundwater head and control soil salinity. Simulation is an appropriate approach to investigate possible groundwater behaviour in future conditions and to explore suitable designs for implementation. Ninety‐nine observation wells were installed around the interceptor drainage system in the Abyek Plain to monitor groundwater movement and salinity changes. Groundwater table fluctuation was measured monthly for 3 years from December 2010 until January 2014. A MODFLOW model was calibrated for the study area using the data measured through the observation wells. Assessment of the measured values indicated that the groundwater head was lowered within the 3 years due to the installation of the system. A calibrated model was applied to predict the future conditions of groundwater levels and suggest proper designs. Groundwater level drawdowns were predicted at approximately 1.3 and 1.5 m for August 2018 and August 2025, respectively. The results also revealed that with the installation of additional parallel interceptor drainage at a distance of 1000 m from the existing drainage, the groundwater table could be lowered in a large area of the plain

A novel approach for handedness detection from off-line handwriting using fuzzy conceptual reduction

A challenging area of pattern recognition is the recognition of handwritten texts in different languages and the reduction of a volume of data to the greatest extent while preserving associations (or dependencies) between objects of the original data. Until now, only a few studies have been carried out in the area of dimensionality reduction for handedness detection from off-line handwriting textual data. Nevertheless, further investigating new techniques to reduce the large amount of processed data in this field is worthwhile. In this paper, we demonstrate that it is important to select only the most characterizing features from handwritings and reject all those that do not contribute effectively to the process of handwriting recognition. To achieve this goal, the proposed approach is based mainly on fuzzy conceptual reduction by applying the Lukasiewicz implication. Handwritten texts in both Arabic and English languages are considered in this study. To evaluate the effectiveness of our proposal approach, classification is carried out using a K-Nearest-Neighbors (K-NN) classifier using a database of 121 writers. We consider left/right handedness as parameters for the evaluation where we determine the recall/precision and F-measure of each writer. Then, we apply dimensionality reduction based on fuzzy conceptual reduction by using the Lukasiewicz implication. Our novel feature reduction method achieves a maximum reduction rate of 83.43 %, thus making the testing phase much faster. The proposed fuzzy conceptual reduction algorithm is able to reduce the feature vector dimension by 31.3 % compared to the original "best of all combined features" algorithm.Qatar National Research Fund NPRP 09-864-1-128Scopu

Formal context coverage based on isolated labels: an efficient solution for text feature extraction

International audienceDifferent available data as images, texts, or database may be mapped into an equivalent or approximate binary relation. A text may be considered as a binary relation relating sentences to words, while a numerical table may be represented by a binary relation after using some scaling approach. A social network may be also represented by a formal context. The objective of this paper is to present an original approach for covering a binary relation by formal concepts based on isolated single or multiple properties, i.e., those belonging to only one concept. As a matter of fact, isolated properties are efficiently used for discriminating and labeling concepts. The latter are used for browsing in a corpora, or in a document by navigating through associated labels. By using fringe relations, the presented approach compared to those of the literature has the advantage of offering a relevant feature of a context by significant labels. Carried out experiments show the benefits of the introduced approac

General learning approach for event extraction: Case of management change event

International audienceStarting from an ontology of a targeted financial domain corresponding to transaction, performance and $management\ change$ news, relevant segments of text containing at least a domain keyword are extracted. The linguistic pattern of each segment is automatically generated to serve initially as a learning model. Each pattern is composed of named entities, keywords and articulation words. Some generic named entities like organizations, persons, locations, dates and grammatical annotations are generated by an automatic tool. During the learning step, each relevant segment is manually annotated with respect to the targeted entities (roles) structuring an event of the ontology. Information extraction is processed by associating a role with a specific entity. By alignment of generic entities to specific entities, some strings of a text are automatically annotated. An original learning approach is presented. Experiments with the $management\ change$ event showed how recognition rates are improved by using different generalization tools