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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Surficial And Deep Earth Material Prediction From Geochemical Compositions, Hassan Talebi, Ute Mueller, Raimon Tolosana-Delgado, Eric C. Grunsky, Jennifer M. Mckinley, Patrice De Caritat
Surficial And Deep Earth Material Prediction From Geochemical Compositions, Hassan Talebi, Ute Mueller, Raimon Tolosana-Delgado, Eric C. Grunsky, Jennifer M. Mckinley, Patrice De Caritat
Research outputs 2014 to 2021
Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based …
Resistivity Profiles Of Perth Soil In Australia In Leak-Detection Test, Lopa M. Pandey, Sanjay K. Shukla, Daryoush Habibi
Resistivity Profiles Of Perth Soil In Australia In Leak-Detection Test, Lopa M. Pandey, Sanjay K. Shukla, Daryoush Habibi
Research outputs 2014 to 2021
The use of a suitable leak-detection system for the prevention and mitigation of pollution due to lining system failures is integral to the proper management of landfill facilities. This paper briefly summarises various methods of leak detection that are currently being practised. The details of a newly developed leak-detection technique are also presented. The tests were conducted to evaluate the performance of the new technique by use of controlled leakage. The resulting resistivity profiles for Perth soil in Australia were obtained at 10 min. This method is found to be effective in detecting and locating liner leakage issues within 30 …
Geospatial Data Pre-Processing On Watershed Datasets: A Gis Approach, Sreedhar Nallan, Leisa Armstrong, Barry Croke, Amiya K. Tripathy
Geospatial Data Pre-Processing On Watershed Datasets: A Gis Approach, Sreedhar Nallan, Leisa Armstrong, Barry Croke, Amiya K. Tripathy
Research outputs 2014 to 2021
Spatial data mining helps to identify interesting patterns from the spatial data sets. However, geo spatial data requires substantial data pre-processing before data can be interrogated further using data mining techniques. Multi-dimensional spatial data has been used to explain the spatial analysis and SOLAP for pre-processing data. This paper examines some of the methods for pre-processing of the data using Arc GIS 10.2 and Spatial Analyst with a case study dataset of a watershed.
Assessment Of Climate Change Effect On Water Harvesting Structures In Rainfed Regions: Geospatial Data Mining Approach, Sreedhar Nallan, Leisa Armstrong
Assessment Of Climate Change Effect On Water Harvesting Structures In Rainfed Regions: Geospatial Data Mining Approach, Sreedhar Nallan, Leisa Armstrong
Research outputs 2012
Advances in the Information and Technologies (ICT) may assist researchers in the assessment of watershed development programmes by developing better visualization understanding of their impacts. GIS based research studies utilizing remote sensing images can facilitate in identifying the potential zones for watershed development and enable improved ground water resources. Effective watershed management is dependent on a number of factors such as demography, climate, soil, land use and topography and these are affected by changing climatic conditions. For example, changes in rainfall intensity and volume in rain fed areas can influence the effectiveness of the placement of watershed structures. Existing methods …