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Delineation of Ground water Vulnerability to Agricultural Contaminants using Neuro-fuzzy Techniques

Delineation of Ground water Vulnerability to Agricultural Contaminants using Neuro-fuzzy Techniques. Barnali Dixon 1 , H. D. Scott 2 , J. V. Brahana 2 , A. Mauromoustakos 2 , J. C. Dixon 2. 1 University of South Florida, 2 University of Arkansas. Introduction.

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Delineation of Ground water Vulnerability to Agricultural Contaminants using Neuro-fuzzy Techniques

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  1. Delineation of Ground water Vulnerability to Agricultural Contaminants using Neuro-fuzzy Techniques Barnali Dixon1, H. D. Scott2, J. V. Brahana2, A. Mauromoustakos2, J. C. Dixon2 1 University of South Florida, 2 University of Arkansas

  2. Introduction • Delineation of vulnerable areas and selective applications of animal wastes/fertilizer in those areas can minimize contamination of ground water (GW). • However, assessment of GW vulnerability or delineation of the monitoring zones is not easy since uncertainty is inherent in all methods of assessing GW vulnerability

  3. Study Area

  4. Location of the Major Watersheds

  5. Sources of Uncertainties • Errors in obtaining data • The natural spatial and temporal variability of the hydrogeologic parameters in the field • The numerical approximation and computerization

  6. Characteristics of the Models • Capability to deal with uncertainties • Tolerate imprecision • Extract information from incomplete data sets • Incorporate expert’s opinion directly into the model • Regional Scale • The models use existing data bases • Integrated in a GIS

  7. Specific Objectives • Integrate the Neuro-fuzzy techniques in a GIS platform to predict ground water vulnerability in a large watershed

  8. Primary Data Layers Used • Watershed Boundaries • Location of springs/wells • Water quality • Geology • Soils • Landuse and landcover (LULC)* • DEMs * model inputs

  9. Secondary Data Layers Used • Soil hydrologic group* • Soil structure (pedality points)* • Depth of the soil profile* (excluding Cr and R) • Slopes • Elevation * model inputs

  10. Description of the Input Data Layers Data Scale/resolution Comments

  11. Spatial Distribution of Major Soil Series

  12. Spatial Distribution of Soil Structure (Pedality Points) Low = 14 – 17, Moderate = 20 – 30, Moderately high= 31 – 40, High = 40 – 50 and very high> 51

  13. Spatial Distribution of Soil Profile Depth Depth (inches) : Shallow = 9 – 30, Moderately shallow = 31 – 50, Moderately deep = 51 – 69, Deep = 70 – 85 and Very Deep = > 85

  14. Spatial Distribution of Soil Hydrologic Groups

  15. Spatial Distribution of Landuse

  16. Spatial Distribution of Geology

  17. Spatial Distribution of Slopes

  18. Neruo-fuzzy Approach

  19. Necessary steps • Training data • Testing data

  20. Why hybrid? • Schultz and Wieland (1997) suggested that NN could parsimoniously represent non-linear systems and seem to be robust and flexible under data driven situations and allow deeper professional insight into the model. • Fuzzy logic provides an opportunity to incorporate experts’ opinion and robust under uncertainty.

  21. Assessment of Models • Comparison of models and Field data • Coincidence analyses • Coincidence with inputs

  22. Results and Discussion

  23. Spatial Distribution of Vulnerability from the Preliminary Neuro-Fuzzy

  24. Slopes vs. Vulnerability Categories

  25. Geology vs. Vulnerability Categories

  26. Soil Hydrologic Group vs. Vulnerability Categories

  27. 35,000 30,000 Non Classified (0) 25,000 High ( 1) Moderately high (2) Moderate (3) Area (ha) 20,000 Low (4) 15,000 10,000 5,000 0 Agriculture Urban Shrubs and Forest Water Confined Brush Animal Operation Landuse Categories Landuse vs. Vulnerability Categories

  28. Soil Depth vs. Vulnerability Categories

  29. Soil Structure (Pedality Points) vs. Vulnerability Categories

  30. Soil Series vs. Vulnerability Categories

  31. Nitrate-N Contamination Level vs. Vulnerability Categories

  32. Spatial Distribution of Wells with Nitrate-N Contamination Level

  33. Summary • Soils with high water transmitting capacity, hydrologic group C, deep soil horizon coincided with highly vulnerable areas • Soils with moderately high water transmitting capacity, hydrologic group C, deep soil horizon coincided with moderate vulnerability • Soils with low water transmitting capacity, hydrologic group B, deep soil horizon coincided with low vulnerability category

  34. Summary cont... • Majority of the soils with high vulnerability coincides with agriculture • Incorporation of landuse in the model need to be fine tuned i.e. potential use of agricultural inputs should be accounted for • Use of the Neuro-fuzzy techniques saved time required to develop the preliminary model • Further modification and fine tuning needed

  35. Questions?

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