Spatio-Temporal Mining of Core Regions: Study of Rainfall Patterns in Monsoonal India

2011 ◽  
Author(s):  
Kollukuduru Sravanthi ◽  
K.S. Rajan
Keyword(s):  
Temporal Mining ◽  
Rainfall Patterns ◽  
Spatio Temporal

SharkFin: Spatio-temporal mining of software adoption and penetration

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Evangelos E. Papalexakis ◽  
Tudor Dumitras ◽  
Duen Horng Chau ◽  
B. Aditya Prakash ◽  
Christos Faloutsos
Keyword(s):  
Software Adoption ◽  
Temporal Mining ◽  
Spatio Temporal

scholarly journals Stochastic reconstruction of spatio-temporal rainfall patterns by inverse hydrologic modelling

2019 ◽  
Vol 23 (1) ◽  
pp. 225-237 ◽  
Author(s):  
Jens Grundmann ◽  
Sebastian Hörning ◽  
András Bárdossy
Keyword(s):  
Flash Flood ◽  
Rainfall Pattern ◽  
Small Scale ◽  
Monitoring Networks ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Hydrologic Modelling ◽  
Stochastic Reconstruction ◽  
Rainfall Patterns ◽  
Spatio Temporal

Abstract. Knowledge of spatio-temporal rainfall patterns is required as input for distributed hydrologic models used for tasks such as flood runoff estimation and modelling. Normally, these patterns are generated from point observations on the ground using spatial interpolation methods. However, such methods fail in reproducing the true spatio-temporal rainfall pattern, especially in data-scarce regions with poorly gauged catchments, or for highly dynamic, small-scale rainstorms which are not well recorded by existing monitoring networks. Consequently, uncertainties arise in distributed rainfall–runoff modelling if poorly identified spatio-temporal rainfall patterns are used, since the amount of rainfall received by a catchment as well as the dynamics of the runoff generation of flood waves is underestimated. To address this problem we propose an inverse hydrologic modelling approach for stochastic reconstruction of spatio-temporal rainfall patterns. The methodology combines the stochastic random field simulator Random Mixing and a distributed rainfall–runoff model in a Monte Carlo framework. The simulated spatio-temporal rainfall patterns are conditioned on point rainfall data from ground-based monitoring networks and the observed hydrograph at the catchment outlet and aim to explain measured data at best. Since we infer a three-dimensional input variable from an integral catchment response, several candidates for spatio-temporal rainfall patterns are feasible and allow for an analysis of their uncertainty. The methodology is tested on a synthetic rainfall–runoff event on sub-daily time steps and spatial resolution of 1 km2 for a catchment partly covered by rainfall. A set of plausible spatio-temporal rainfall patterns can be obtained by applying this inverse approach. Furthermore, results of a real-world study for a flash flood event in a mountainous arid region are presented. They underline that knowledge about the spatio-temporal rainfall pattern is crucial for flash flood modelling even in small catchments and arid and semiarid environments.

scholarly journals Spatio-temporal mining of keywords for social media cross-social crawling of emergency events

2019 ◽  
Vol 23 (3) ◽  
pp. 425-447
Author(s):  
Andrea Autelitano ◽  
Barbara Pernici ◽  
Gabriele Scalia
Keyword(s):  
Social Media ◽  
Emergency Events ◽  
Temporal Mining ◽  
Spatio Temporal

Spatio-temporal rainfall patterns around Atlanta, Georgia and possible relationships to urban land cover

Urban Climate ◽  
2017 ◽  
Vol 21 ◽  
pp. 27-42 ◽  
Author(s):  
Jordan McLeod ◽  
Marshall Shepherd ◽  
Charles E. Konrad
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Urban Land Cover ◽  
Rainfall Patterns ◽  
Spatio Temporal
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