Keywords

Data Mining, Hydrogeology, Acquifer, Clustering, Traffic Light Panel (TLP), Post- processing. Decision-making

Location

Session G2: Data Mining for Environmental Sciences (s-DMTES IV)

Start Date

17-6-2014 10:40 AM

End Date

17-6-2014 12:00 PM

Abstract

Groundwater wells are one of the most important water resources in the world. Control and management of these resources are of high importance due to the implicit need of water as the main resource for life. This research focuses on a hydrogeological analysis with clustering, which is one of the most popular data mining methods, including In the classical data mining scheme, last step corresponds to the effective production of knowledge. In this paper, special focus on that part is done, by means of post-processing tools. The main goal is to discover prototypical profiles from the acquifer Pedro González in Margarita Island (Venezuela), in order to understand the prototypical water conditions regarding quality and supply level. The database contains 36 groundwater wells and their hydrogeological variables, i.e., electrical conductivity, static level, pH and geographical coordinates that were collected in five annual measurement campaigns. Clustering methods were used to discover profiles and a typology of three types of wells was extracted. Post-processing tools were used to get a conceptualization of the resulting classes and comprehensible profiles were finally described. The Class Panel Graph (CPG) and the Traffic Light Panel (TLP) were used to post-process the classes and understand the resulting profiles through symbolic visualization. The TLP was presented to the expert to support a multidisciplinary discussion and to create the mechanisms for a detailed understanding of the evolution of the aquifer. Results reported that the aquifer is in a critical situation in both water quality and supply levels. From this research, public administration performed some technical actions to improve the performance of the aquifer and its preservation. At present, predictive models local to profiles are developed.

 
Jun 17th, 10:40 AM Jun 17th, 12:00 PM

Discovering Comprehensible Hydrogeological Profiles in the Margarita Island's Aquifers including Post-processing in a data mining process

Session G2: Data Mining for Environmental Sciences (s-DMTES IV)

Groundwater wells are one of the most important water resources in the world. Control and management of these resources are of high importance due to the implicit need of water as the main resource for life. This research focuses on a hydrogeological analysis with clustering, which is one of the most popular data mining methods, including In the classical data mining scheme, last step corresponds to the effective production of knowledge. In this paper, special focus on that part is done, by means of post-processing tools. The main goal is to discover prototypical profiles from the acquifer Pedro González in Margarita Island (Venezuela), in order to understand the prototypical water conditions regarding quality and supply level. The database contains 36 groundwater wells and their hydrogeological variables, i.e., electrical conductivity, static level, pH and geographical coordinates that were collected in five annual measurement campaigns. Clustering methods were used to discover profiles and a typology of three types of wells was extracted. Post-processing tools were used to get a conceptualization of the resulting classes and comprehensible profiles were finally described. The Class Panel Graph (CPG) and the Traffic Light Panel (TLP) were used to post-process the classes and understand the resulting profiles through symbolic visualization. The TLP was presented to the expert to support a multidisciplinary discussion and to create the mechanisms for a detailed understanding of the evolution of the aquifer. Results reported that the aquifer is in a critical situation in both water quality and supply levels. From this research, public administration performed some technical actions to improve the performance of the aquifer and its preservation. At present, predictive models local to profiles are developed.