{"id":50394533,"url":"https://github.com/farzadasgari/evap","last_synced_at":"2026-05-30T20:03:06.583Z","repository":{"id":317242745,"uuid":"1052988655","full_name":"farzadasgari/evap","owner":"farzadasgari","description":"Lake surface water evaporation modeling using remote-sensed water quality parameters (CHL, CDOM, TSM, temperature) and Bayesian-optimized LSTM/GRU hybrids validated against Penman-FAO.","archived":false,"fork":false,"pushed_at":"2025-09-29T17:53:20.000Z","size":78774,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-09-29T19:34:04.707Z","etag":null,"topics":["bayesian-optimization","cdec","era5","evaporation","gated-recurrent-units","google-earth-engine","gru","hybrid-model","long-short-term-memory","lstm","python","recurrent-neural-networks","remote-sensing","satellite","satellite-imagery","sentinel-2","snap","water","water-engineering","water-quality"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/farzadasgari.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-09-08T20:39:36.000Z","updated_at":"2025-09-29T17:53:24.000Z","dependencies_parsed_at":null,"dependency_job_id":"26b2e517-b567-49d9-9b2d-1dbd648710d0","html_url":"https://github.com/farzadasgari/evap","commit_stats":null,"previous_names":["farzadasgari/evap"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/farzadasgari/evap","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/farzadasgari%2Fevap","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/farzadasgari%2Fevap/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/farzadasgari%2Fevap/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/farzadasgari%2Fevap/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/farzadasgari","download_url":"https://codeload.github.com/farzadasgari/evap/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/farzadasgari%2Fevap/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33707328,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-05-30T02:00:06.278Z","response_time":92,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian-optimization","cdec","era5","evaporation","gated-recurrent-units","google-earth-engine","gru","hybrid-model","long-short-term-memory","lstm","python","recurrent-neural-networks","remote-sensing","satellite","satellite-imagery","sentinel-2","snap","water","water-engineering","water-quality"],"created_at":"2026-05-30T20:03:05.632Z","updated_at":"2026-05-30T20:03:06.578Z","avatar_url":"https://github.com/farzadasgari.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n   \u003ch1\u003eLake Surface Water Evaporation Modeling with Remote Sensing and Hybrid Deep Learning\u003c/h1\u003e\n\u003c/div\u003e\n\n\nThis repository supports a research study on estimating lake surface water evaporation using satellite-derived water quality (WQ) parameters and in-situ meteorological (MG) data. We develop Bayesian Optimization (BO)-tuned deep learning architectures (BO-LSTM, BO-GRU) and compare them with their non-optimized counterparts (LSTM, GRU) and a physically based Penman-FAO formulation (baseline).\n\n\u003e Status: Active research codebase (manuscript in preparation). Structure and APIs may change.\n\n---\n\n## Key Contributions\n\n- Focus on open surface water evaporation.\n- Integrates remote sensing–derived WQ parameters (CHL, CDOM, TSM, temperature) with MG drivers.\n- Hybrid deep learning: BO-LSTM and BO-GRU (Bayesian hyperparameter optimization).\n- Benchmark against Penman-FAO physical model.\n- Feature attribution using SHAP for interpretability.\n- Demonstrates viability of WQ-only predictors where MG data are sparse.\n\n---\n\n## Installation\n\n```bash\npython -m venv .venv\nsource .venv/bin/activate  # or .venv\\Scripts\\activate on Windows\npip install -r requirements.txt\n```\n\n---\n\n## License\n\nMIT License (see [LICENSE](https://github.com/farzadasgari/evap?tab=MIT-1-ov-file)).\n\n---\n\n## Disclaimer\n\nThis repository is a research vehicle. Model outputs should not be treated as operational hydrological guidance without independent verification.\n\n---\n\n## Contact\nFor any inquiries, please contact:\n- std_farzad.asgari@khu.ac.ir\n- khufarzadasgari@gmail.com\n\n---\n\n## Links\n\n### Farzad Asgari\n[![portfolio](https://img.shields.io/badge/my_portfolio-000?style=for-the-badge\u0026logo=ko-fi\u0026logoColor=white)](https://farzadasgari.ir/)\n\n[![Google Scholar Badge](https://img.shields.io/badge/Google%20Scholar-4285F4?logo=googlescholar\u0026logoColor=fff\u0026style=for-the-badge)](https://scholar.google.com/citations?user=Rhue_kkAAAAJ\u0026hl=en)\n\n[![ResearchGate Badge](https://img.shields.io/badge/ResearchGate-0CB?logo=researchgate\u0026logoColor=fff\u0026style=for-the-badge)](https://www.researchgate.net/profile/Farzad-Asgari)\n\n[![linkedin](https://img.shields.io/badge/linkedin-0A66C2?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white)](https://www.linkedin.com/in/farzad-asgari-5a90942b2/)\n\n\n### Seyed Hossein Mohajeri\n[![portfolio](https://img.shields.io/badge/my_portfolio-000?style=for-the-badge\u0026logo=ko-fi\u0026logoColor=white)](https://khu.ac.ir/cv/1139/Seyed-Hossein-Mohajeri)\n\n[![Google Scholar Badge](https://img.shields.io/badge/Google%20Scholar-4285F4?logo=googlescholar\u0026logoColor=fff\u0026style=for-the-badge)](https://scholar.google.com/citations?user=E8PFUBEAAAAJ\u0026hl=en)\n\n[![ResearchGate Badge](https://img.shields.io/badge/ResearchGate-0CB?logo=researchgate\u0026logoColor=fff\u0026style=for-the-badge)](https://www.researchgate.net/profile/Seyed-Mohajeri-2)\n\n[![linkedin](https://img.shields.io/badge/linkedin-0A66C2?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white)](\nhttps://ir.linkedin.com/in/hossein-mohajeri)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffarzadasgari%2Fevap","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffarzadasgari%2Fevap","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffarzadasgari%2Fevap/lists"}