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- W4221099800 abstract "Aquifer management under sparse data is studied by mapping water quality indices in this chapter particularly when data sparsity stems from poor or nonexistent planning practices. This chapter explores aggregating multiple maps on water quality by data fusion techniques. The 10 indices under study are as follows: Groundwater Quality Index GroundWater Quality Index, four Carcinogenic Risk Indices by considering Dermal and Oral pathways and impacts on adults and children (CRI-DA, CRI-OA, CRI-DC, and CRI-OA), and Noncarcinogenic Risk Indices comprising four indices (NRI-DA, NRI-OA, NRI-DC, and NRI-OA). An Inclusive Multiple Modeling strategy is presented at two levels including data fusion. At Level 1: a simple data fusion scheme is used with unsupervised learning from the data to aggregate the above 10 indices to one single index; at Level 2, two strategies are investigated: using Artificial Neural Network in which Strategy 1 uses the 10 indices as its inputs and Strategy 2 uses raw data but both use the output of Level 1 as their target values. The results show that Strategy 1 performs as well as Strategy 2 with the most direct results. The approach offers a proof of concept for a methodology to obtain surrogate target values for the aggregated index." @default.
- W4221099800 created "2022-04-03" @default.
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- W4221099800 date "2022-01-01" @default.
- W4221099800 modified "2023-09-26" @default.
- W4221099800 title "Mapping and aggregating groundwater quality indices for aquifer management using Inclusive Multiple Modeling practices" @default.
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- W4221099800 doi "https://doi.org/10.1016/b978-0-323-85698-0.00026-5" @default.
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