Month: 2026-01 | Repository: Giswater/giswater_dbmodel Key deliverables (features and enhancements): - Topological Recovery Enhancements for Water System Migrations: Introduces a SQL script to reconnect arcs to nearest nodes, insert missing nodes, and adjust arcs to improve migration performance. Major bugs fixed: - No explicit major bug fixes recorded for Giswater/giswater_dbmodel in this month; focus was on feature delivery and topology optimization. Overall impact and accomplishments: - Accelerated, more reliable topology recovery during migrations, reducing downstream remediation time and enabling faster data migrations for water systems. - Improved data integrity by ensuring arcs are correctly connected to valid nodes, with a reusable SQL script for future migrations. Technologies/skills demonstrated: - SQL scripting for topology recovery, GIS topology concepts (arcs, nodes), and migration workflow optimization. - Version control readiness (clear commits) and documentation of changes.

Monthly performance summary for 2025-12: Key features delivered: - Active status tracking for inflows and treatments: Added an active flag to inflows data structures with corresponding schema changes and query filters to selectively include only active inflows, improving data integrity and query performance. Commits: 4cbce82c217473df8ddb47609ec1a771868ab318; a7617095fb5ff6a0afd2017e479477a62b8c3a8d. - CSO data views enhancement: Expanded and streamlined CSO data views, introduced multi-result views for richer analysis, and removed obsolete code to simplify the codebase. Commit: 160d091d58ffafaf44d536ee0a6f6d122403eb0c. - Tank elevation data support with invert_level: Added invert_level column to the man_tank table and updated data insertion/update logic to handle tank elevation data, enabling accurate elevation-aware analyses. Commit: cbb2c0f21da183b1afe36e908ad73ba23fcb9ea6. - User-specific sector visibility and access control: Implemented exploit_visibility-driven filtering and refined access policies so users see sectors aligned with their exploitation strategies, improving data relevance and security. Commits: 459d49e96767ddc1c9b788144a7d28c8c310881d; f435ff81e2587a758e0bc27605943fc79e95cedf. - CSO algorithm enhancements and WWTP/Weir data structures: Added new tables for weir and WWTP assessments and enhanced calculation logic for real demand and weight factors in the CSO algorithm. Commit: 4f9c38ca9ca1eaa1849853c317c3cce99fe9bad0. Major bugs fixed: - Database schema/view correctness fixes: Corrects to database/schema updates and CSO view calibration logic to ensure correct table names and accurate calibration values in views. Commits: 7ea87b93891a157373c6920083b188367ddc33c6; 3cad88166b89ea568caa76d0565390427defef10. Overall impact and accomplishments: - Strengthened data integrity, query performance, and analytics capabilities across GIS data models and CSO domain. - Delivered targeted data-model improvements enabling more accurate, faster analyses and secure, user-relevant data access. - Maintained PostgreSQL compatibility for the QGIS plugin and core database layer, ensuring continued usability with modern database features. Technologies/skills demonstrated: - Database schema design and migrations (PostgreSQL), view modeling and calibration logic, data integrity enforcement. - Performance tuning and large-project selectors, as evidenced by optimized queries and multi-result views. - Security-conscious access control via policy-driven visibility filtering. - Domain-specific CSO algorithm enhancements and data-structure expansion for Weir/WWTP assessments. Business value: - Accelerated analytics readiness for CSO and water-treatment workflows, reduced data ambiguity, and safer, more focused user access across exploitation contexts.

Month: 2025-11 | Giswater/giswater_dbmodel delivered notable performance improvements for profile data retrieval and reinforced SQL function reliability through crucial temp table lifecycle fixes. Key efforts focused on optimizing queries and ensuring temp_vnode is correctly created/dropped within getprofilevalues, preserving data integrity and improving user experience.

Monthly performance summary for 2025-10: Stabilized planning-mode behavior in Giswater/giswater_dbmodel by delivering targeted fixes to data integrity and query correctness, and by strengthening the feature replacement plan with robust state type management. These changes reduce planning errors, improve data reliability, and enhance maintainability, delivering clear business value.

September 2025: Giswater/giswater_dbmodel delivered automation, selector enhancements, arc visualization improvements, and code/data integrity refinements. Notable work includes Hydrants Exploitation Automation (proximity-triggered exploitation association via ST_DWithin and PROPOSED HYDRANT insertion), Exploitation Selectors System enhancements for multi-network projects and envelope alignment with psectors, and Arc Analysis/Visualization enhancements with configurable arc length filters and symbol updates. A critical bug fix corrected Node ID sequence referencing by replacing hardcoded schema with a placeholder to ensure correct schema usage. These changes reduce manual work, improve data integrity, and enhance user-centric visualization and multi-network support, driving operational efficiency and governance compliance.

August 2025: Focused on stability, UX improvements, and maintainability for Giswater_qgis_plugin. Delivered dynamic Mapzones Manager UI and macro-level zoom consistency, while hardening the UI layout and cleaning legacy code to reduce risk and support scalable contributions. These changes lay groundwork for a more reliable mapping workflow and easier future enhancements.

July 2025 — Delivered foundational GISWater enhancements with a focus on cm-visits, multi-network data integrity, performance optimizations, and configuration consistency. Key outcomes include: (1) cm-visits groundwork with comprehensive SQL scripts for visits, incidents, and inspection workflows plus internationalization scaffold; (2) robust fix for control valve processing by correcting type casting and joins; (3) enhanced GIS selectors and data integrity across multinetwork configurations, improving filtering, joining, and data governance; (4) node rotation editing and trigger optimization to update only when necessary, improving performance; (5) form field configuration improvements and topology naming refactors to standardize layout definitions and naming across the model. Impact: higher data accuracy, faster feature delivery, better multi-network support, and reduced maintenance overhead. Technologies: SQL scripts, views, functions, internationalization setup, type casting/joins optimization, trigger logic, configuration management.

June 2025 achievements focused on data integrity, performance, and scalable operations in Giswater/giswater_dbmodel. Delivered production data integration for campaign management with extended schemas and dynamic SQL-based CRUD, stabilized the production integration routine to prevent unintended data manipulation, improved spatial query robustness and performance for workcat searches, tuned hydraulic model parameters for more realistic simulations, and standardized inflow export values to ensure consistent data across downstream processes. These workstreams reduce data latency, enhance campaign accuracy, improve model fidelity, and enable scalable mass operations.

May 2025 monthly summary for Giswater/giswater_dbmodel. Focused on delivering foundational CSO analysis features, UI/internationalization enhancements, and legacy data utilities, while hardening data integrity. The team delivered core CSO calculation and overflow modeling capabilities, improved performance for overflow operations, and expanded UI/i18n support, alongside a data repair utility for legacy datasets and a database constraint to prevent duplicate lid usage entries.

April 2025: Focused on stabilizing the Giswater db model, delivering key bug fixes, data-model enhancements, UI/config improvements, performance optimization, and expanded data capabilities. Achieved stronger data integrity, faster views, improved export and configuration workflows, and broader support for CM, campaign, and water balance scenarios. Comprehensive tests and updated sample data underpin the improved reliability.

March 2025: Delivered feature-focused data enhancements to Giswater/giswater_qgis_plugin that improve SWMM workflow setup and data consistency. Key outcomes include the addition of swmm_flow.dat for SWMM flow simulations and the standardization of SWMM-related example naming by renaming rainfall.dat to swmm_rainfall.dat. These updates reduce user setup time, improve reproducibility of SWMM scenarios, and lay groundwork for future SWMM example expansions. No critical bugs fixed this month; the emphasis was on delivering usable data and maintaining repository quality.

February 2025 monthly recap for Giswater/giswater_dbmodel focused on stability, data quality, and user experience enhancements that support reliable network planning workflows and scalable data pipelines.

January 2025 (Giswater/giswater_dbmodel): Delivered security-focused data controls, data model enhancements, data quality and network validation improvements, and performance-oriented refactors. Implemented user-specific sector export restrictions, extended sector metadata with sector_type, progressed EPA data quality checks, refactored database views/queries for reliability and performance, enhanced reporting with headloss metrics and indexing, plus UI and metadata refinements. Also stabilized test suite for sector-related data. Result: stronger data security, integrity, faster reporting, and improved developer experience.

December 2024: Metadata hygiene and debugging support for Giswater_qgis_plugin. Key outcomes include standardized branding and release metadata, plus targeted debugging disablement to isolate issues in client-side filters. These actions enhance release reliability, ease maintenance, and improve triage efficiency.

2024-11 Giswater/giswater_dbmodel monthly summary: Delivered multi-scenario export input capability, key data quality and performance improvements, and stabilization of core data model components. Implemented targeted export/input enhancements to reduce noise and processing time, and fixed critical scenario and data integrity bugs to improve reliability for ongoing scenario analyses. UI/UX and data visibility improvements were introduced to help users craft precise exports and reports with minimal configuration. This cycle demonstrates strong SQL/Postgres refinement, Python-based code cleanup, and thoughtful performance tuning that translates to tangible business value in faster, more accurate exports and more robust data models.

Monthly performance summary for 2024-10 focused on delivering value through feature enhancements and maintaining the plugin's reliability.