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Evangelos Rozos, Panayiotis Dimitriadis and Vasilis Bellos
Machine learning has been employed successfully as a tool virtually in every scientific and technological field. In hydrology, machine learning models first appeared as simple feed-forward networks that were used for short-term forecasting, and have evol...
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Salam A. Abbas, Yunqing Xuan and Ryan T. Bailey
In this article, we present the use of the coupled land surface model and groundwater flow model SWAT-MODFLOW with the decision support tool WEAP (Water Evaluation and Planning software) to predict future surface-water abstraction scenarios in a complex ...
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Elisa Mammoliti, Davide Fronzi, Costanza Cambi, Francesco Mirabella, Carlo Cardellini, Emiliano Patacchiola, Alberto Tazioli, Stefano Caliro and Daniela Valigi
Carbonate aquifers are characterised by strong heterogeneities and their modelling is often a challenging aspect in hydrological studies. Understanding carbonate aquifers can be more complicated in the case of strong seismic events which have been widely...
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Hernán D. Salas, Juliana Valencia, Alejandro Builes-Jaramillo and Alejandro Jaramillo
The synoptic mode of variability (SMV) refers to changes in atmospheric conditions over periods ranging from 2 to 10 days. In tropical regions, this variability is driven by tropical waves that have a clear signal on the wavenumber?frequency power spectr...
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Daniel Jato-Espino and Shray Pathak
This paper concerns the design of a geographic location system to identify urban road sections susceptible to runoff accumulation through the analysis of the efficiency of surface drainage networks. To this end, a combination of Geographic Information Sy...
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