Your search found 2 records
1 Barlund, I.; da Costa, M. P.; Modak, P.; Mensah, A. M.; Gordon, C.; Babel, M. S.; Dickens, Chris; Jomaa, S.; Ollesch, G.; Swaney, D.; Alcamo, J. 2016. Water pollution in river basins. In United Nations Environment Programme. A snapshot of the world’s water quality: towards a global assessment. Nairobi, Kenya: United Nations Environment Programme. pp.49-80.
Water pollution ; Water quality ; Water governance ; Water resources ; Surface water ; River basins ; Drinking water ; Watersheds ; Sewage ; Faecal coliforms ; Contamination ; Wastewater treatment ; Community involvement ; Sediment ; Catchment areas ; Nutrients ; Case studies / Latin America / Asia / Africa / Europe / North America / Brazil / India / West Africa / Thailand / Tunisia / Czech Republic / Sao Paulo State / Tryambakeshwar / Maharashtra / Johannesburg / Upper Tiete River Basin / Godavari River Basin / Volta River Basin / Chao Phraya River Basin / Vaal River Basin / Medjerda River Basin / Elbe River Basin / Hudson River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H047585)
https://uneplive.unep.org/media/docs/assessments/unep_wwqa_report_web.pdf
https://vlibrary.iwmi.org/pdf/H047585.pdf
https://vlibrary.iwmi.org/pdf/H047585.pdf
(9.82 MB)
2 Varouchakis, E. A.; Solomatine, D.; Perez, G. A. C.; Jomaa, S.; Karatzas, G. P. 2023. Combination of geostatistics and self-organizing maps for the spatial analysis of groundwater level variations in complex hydrogeological systems. Stochastic Environmental Research and Risk Assessment, 37(8):3009-3020. [doi: https://doi.org/10.1007/s00477-023-02436-x]
(Location: IWMI HQ Call no: e-copy only Record No: H052052)
https://link.springer.com/content/pdf/10.1007/s00477-023-02436-x.pdf?pdf=button
https://vlibrary.iwmi.org/pdf/H052052.pdf
https://vlibrary.iwmi.org/pdf/H052052.pdf
(1.27 MB) (1.27 MB)
Successful modelling of the groundwater level variations in hydrogeological systems in complex formations considerably depends on spatial and temporal data availability and knowledge of the boundary conditions. Geostatistics plays an important role in model-related data analysis and preparation, but has specific limitations when the aquifer system is inhomogeneous. This study combines geostatistics with machine learning approaches to solve problems in complex aquifer systems. Herein, the emphasis is given to cases where the available dataset is large and randomly distributed in the different aquifer types of the hydrogeological system. Self-Organizing Maps can be applied to identify locally similar input data, to substitute the usually uncertain correlation length of the variogram model that estimates the correlated neighborhood, and then by means of Transgaussian Kriging to estimate the bias corrected spatial distribution of groundwater level. The proposed methodology was tested on a large dataset of groundwater level data in a complex hydrogeological area. The obtained results have shown a significant improvement compared to the ones obtained by classical geostatistical approaches.
Powered by DB/Text WebPublisher, from
