A Quantitative Groundwater Resource Management under Uncertainty Using a Retrospective Optimization Framework
| Autor: | Gislar Edgar Kifanyi, Samuel Nii Odai, Julius M. Ndambuki |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Optimization problem
010504 meteorology & atmospheric sciences 0208 environmental biotechnology Geography Planning and Development TJ807-830 Context (language use) Aquifer 02 engineering and technology Management Monitoring Policy and Law TD194-195 01 natural sciences Renewable energy sources groundwater management uncertainty Retrospective Optimization Framework Resource (project management) Resource management GE1-350 0105 earth and related environmental sciences geography geography.geographical_feature_category Hydrogeology Environmental effects of industries and plants Renewable Energy Sustainability and the Environment Building and Construction 020801 environmental engineering Water resources Environmental sciences Environmental science Water resource management Groundwater |
| Zdroj: | Sustainability; Volume 9; Issue 1; Pages: 2 Sustainability, Vol 9, Iss 1, p 2 (2016) |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su9010002 |
| Popis: | Water resources are a major concern for any socio-economic development. As the quality of many surface fresh water sources increasingly deteriorate, more pressure is being imparted into groundwater aquifers. Since groundwater and the aquifers that host it are inherently vulnerable to anthropogenic impacts, there is a need for sustainable pumping strategies. However, groundwater resource management is challenging due to the heterogeneous nature of aquifer systems. Aquifer hydrogeology is highly uncertain, and thus it is imperative that this uncertainty is accounted for when managing groundwater resource pumping. This, therefore, underscores the need for an efficient optimization tool which can sustainably manage the resource under uncertainty conditions. In this paper, we apply a procedure which is new within the context of groundwater resource management—the Retrospective Optimization Approximation (ROA) method. This method is capable of designing sustainable groundwater pumping strategies for aquifers which are characterized by uncertainty arising due to scarcity of input data. ROA framework solves and evaluates a sequence of optimization sub-problems in an increasing number of realizations. We used k-means clustering sampling technique for the realizations selection. The methodology is demonstrated through application to an hypothetical example. The optimization problem was solved and analyzed using “Active Set” algorithm implemented under MATLAB environment. The results indicate that the ROA sampling based method is a promising approach for optimizing groundwater pumping rates under conditions of hydrogeological uncertainty. |
| Databáze: | OpenAIRE |
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