Add slc ml cge in doc

manual_jb/content/analyses.md

@@ -20,26 +20,27 @@
 18. [Interdependent Network Design Problem](analyses/indp)
 19. [Joplin Computable General Equilibrium (CGE)](analyses/joplin_cge)
 20. [Joplin empirical restoration](analyses/joplin_empirical_restoration)
-21. [Mean damage](analyses/mean_dmg)
-22. [Monte Carlo failure probability](analyses/mc_failure_prob)
-23. [Multi-objective retrofit optimization](analyses/multi_retrofit_optimization)
-24. [Network cascading interdependency functionality](analyses/nci_functionality)
-25. [Nonstructural building damage](analyses/non_structural_building_dmg)
-26. [Pipeline damage](analyses/pipeline_dmg)
-27. [Pipeline damage with repair rate](analyses/pipeline_dmg_w_repair_rate)
-28. [Pipeline functionality](analyses/pipeline_functionality)
-29. [Pipeline repair cost](analyses/pipeline_repair_cost)
-30. [Pipeline restoration](analyses/pipeline_restoration)
-31. [Population dislocation](analyses/populationdislocation)
-32. [Portfolio recovery](analyses/portfolio_recovery)
-33. [Residential building recovery](analyses/residential_building_recovery)
-34. [Road damage](analyses/road_dmg)
-35. [Salt Lake City Computable General Equilibrium (CGE)](analyses/slc_cge.md)
-36. [Seaside Computable General Equilibrium (CGE)](analyses/seaside_cge)
-37. [Social Vulnerability](analyses/social_vulnerability)
-38. [Tornado electric power network (EPN) damage](analyses/tornadoepn_dmg)
-39. [Transportation recovery](analyses/transportation_recovery)
-40. [Water facility damage](analyses/waterfacility_dmg)
-41. [Water network functionality](analyses/wfn_functionality)
-42. [Water facility repair cost](analyses/water_facility_repair_cost)
-43. [Water facility restoration](analyses/water_facility_restoration)
+21. [Machine Learning Enabled Computable General Equilibrium (CGE) - Salt Lake City](analyses/ml_slc_cge.md)
+22. [Mean damage](analyses/mean_dmg)
+23. [Monte Carlo failure probability](analyses/mc_failure_prob)
+24. [Multi-objective retrofit optimization](analyses/multi_retrofit_optimization)
+25. [Network cascading interdependency functionality](analyses/nci_functionality)
+26. [Nonstructural building damage](analyses/non_structural_building_dmg)
+27. [Pipeline damage](analyses/pipeline_dmg)
+28. [Pipeline damage with repair rate](analyses/pipeline_dmg_w_repair_rate)
+29. [Pipeline functionality](analyses/pipeline_functionality)
+30. [Pipeline repair cost](analyses/pipeline_repair_cost)
+31. [Pipeline restoration](analyses/pipeline_restoration)
+32. [Population dislocation](analyses/populationdislocation)
+33. [Portfolio recovery](analyses/portfolio_recovery)
+34. [Residential building recovery](analyses/residential_building_recovery)
+35. [Road damage](analyses/road_dmg)
+36. [Salt Lake City Computable General Equilibrium (CGE)](analyses/slc_cge.md)
+37. [Seaside Computable General Equilibrium (CGE)](analyses/seaside_cge)
+38. [Social Vulnerability](analyses/social_vulnerability)
+39. [Tornado electric power network (EPN) damage](analyses/tornadoepn_dmg)
+40. [Transportation recovery](analyses/transportation_recovery)
+41. [Water facility damage](analyses/waterfacility_dmg)
+42. [Water network functionality](analyses/wfn_functionality)
+43. [Water facility repair cost](analyses/water_facility_repair_cost)
+44. [Water facility restoration](analyses/water_facility_restoration)

manual_jb/content/analyses/ml_slc_cge.md

@@ -0,0 +1,61 @@
+# Machine Learning Enabled Computable General Equilibrium (CGE) - Salt Lake City
+
+**Description**
+
+The "Machine Learning Enabled Computable General Equilibrium (CGE) - Salt Lake City" analysis merges advanced machine learning with traditional CGE models to offer unprecedented insights into the economic impacts of disaster scenarios on Salt Lake City. Trained on a comprehensive dataset of numerous simulated disasters and their economic effects, this hybrid approach excels in predicting the intricate dynamics of the city's economy under various crises.
+
+A computable general equilibrium (CGE) model is based on fundamental economic principles. A CGE model uses multiple data sources to reflect the interactions of households, firms, and relevant government entities as they contribute to economic activity. The model is based on (1) utility-maximizing households that supply labor and capital, using the proceeds to pay for goods and services (both locally produced and imported) and taxes; (2) the production sector, with perfectly competitive, profit-maximizing firms using intermediate inputs, capital, land, and labor 
+to produce goods and services for both domestic consumption and export; (3) the government sector that collects taxes and uses tax revenues in order to finance the provision of public services; and (4) the rest of the world.
+
+
+The output of this analysis are CSV files with domestic supply, gross income, before- and post-disaster factor demand and household count.
+
+**Contributors**
+
+- Science: Charles Nicholson, Nushra Zannat, Hwayoung Jeon, Tao Lu, Harvey Cutler, Anita Pena
+- Implementation: NCSA IN-CORE Dev Team
+
+
+**Input parameters**
+
+key name | type | name | description
+--- | --- | --- | ---
+`result_name` | `str` | Output File Name prefix | Sets the file name prefix for output files.
+
+**Input datasets**
+
+key name | type | name | description
+--- | --- | --- | ---
+`sector_shocks` <sup>*</sup> | [`incore:capitalShocks`](https://incore.ncsa.illinois.edu/semantics/api/types/incore:capitalShocks) | Capital shocks | Building states to capital <br>shocks per sector.
+
+**Output datasets**
+
+key name | type | name | description
+--- | --- | --- | ---
+`domestic-supply` <sup>*</sup> | [`incore:Employment`](https://incore.ncsa.illinois.edu/semantics/api/types/incore:Employment) | Supply results | A dataset containing domestic supply results (format: CSV).
+`gross-income` <sup>*</sup> | [`incore:Employment`](https://incore.ncsa.illinois.edu/semantics/api/types/incore:Employment) | Gross income | A dataset of resulting gross income (format: CSV).
+`pre-disaster-factor-demand` <sup>*</sup> | [`incore:FactorDemand`](https://incore.ncsa.illinois.edu/semantics/api/types/incore:FactorDemand) | Factor demand | A dataset of factor demand before disaster (format: CSV).
+`post-disaster-factor-demand` <sup>*</sup> | [`incore:FactorDemand`](https://incore.ncsa.illinois.edu/semantics/api/types/incore:FactorDemand) | Factor demand | A dataset of factor demand after disaster (format: CSV).
+`household-count` <sup>*</sup> | [`incore:HouseholdCount`](https://incore.ncsa.illinois.edu/semantics/api/types/incore:HouseholdCount) | Household count | A dataset of household count (format: CSV).
+
+<small>(* required)</small>
+
+**Execution**
+
+code snippet:
+
+```
+    # Create ML enabled Salt Lake City CGE Model
+    ml_enabled_cge = MlEnabledCgeSlc(client)
+
+    # Set analysis input datasets
+    ml_enabled_cge.load_remote_input_dataset("sector_shocks", sector_shocks)
+
+    # Set any optional analysis parameters
+    ml_enabled_cge.set_parameter("result_name", "slc_7_region")
+
+    # Run Salt Lake City CGE model analysis
+    ml_enabled_cge.run_analysis()
+```
+
+full analysis: [ml_enabled_slc_cge.ipynb](https://github.com/IN-CORE/incore-docs/blob/main/notebooks/ml_enabled_slc_cge.ipynb)