[Arranged chronologically according to the file order shown above]
Sandhu, P. and Finch, R. Artificial Neural Networks and Their Applications. In: Methodology for Flow and Salinity Estimates in the Sacramento-San Joaquin Delta and Suisun Marsh: Sixteenth Annual Progress Report to the State Water Resources Control Board; California Department of Water Resources: Sacramento, CA, USA, 1995.
Sandhu, P. and Finch, R. Application of artificial neural networks to the Sacramento-San Joaquin Delta. In The 1995 4th International Conference on Estuarine and Coastal Modeling 1995 Oct (pp. 490-503).
Hutton, P. and Sandhu, P. Disinfection By-product Formation. In: Methodology for Flow and Salinity Estimates in the Sacramento-San Joaquin Delta and Suisun Marsh: Sixteenth Annual Progress Report to the State Water Resources Control Board; California Department of Water Resources: Sacramento, CA, USA, 1995.
Sandhu, P. Emulation of DWRDSM Using Artificial Neural Networks and Estimation of Sacramento River Flow. In: Methodology for Flow and Salinity Estimates in the Sacramento-San Joaquin Delta and Suisun Marsh: Seventeenth Annual Progress Report to the State Water Resources Control Board; California Department of Water Resources: Sacramento, CA, USA, 1996.
Sandhu, P. and Wilson, D. Marginal Export Cost and MDO Replacement. In: Methodology for Flow and Salinity Estimates in the Sacramento-San Joaquin Delta and Suisun Marsh: Eighteenth Annual Progress Report to the State Water Resources Control Board; California Department of Water Resources: Sacramento, CA, USA, 1997.
Wilson, D. Artificial Neural Networks and MEC Estimates. In: Methodology for Flow and Salinity Estimates in the Sacramento-San Joaquin Delta and Suisun Marsh: Nineteenth Annual Progress Report to the State Water Resources Control Board; California Department of Water Resources: Sacramento, CA, USA, 1998.
Chung F, Sandhu N, Wilson D, Finch R. Modeling flow salinity relationships in the Sacramento-San Joaquin delta using artificial neural networks. Technical report OSP-99-1, Department of Water resources office of SWP planning, California; 1999.
Pranger, T. Artificial Neural Network Development. In: Methodology for Flow and Salinity Estimates in the Sacramento-San Joaquin Delta and Suisun Marsh: Twenty-First Annual Progress Report to the State Water Resources Control Board; California Department of Water Resources: Sacramento, CA, USA, 2000.
Hutton, P. and Seneviratne S. An Initial Assessment of Delta Carriage Water Requirements Using a New CALSIM Flow-Salinity Routine. In: Methodology for Flow and Salinity Estimates in the Sacramento-San Joaquin Delta and Suisun Marsh: Twenty-Second Annual Progress Report to the State Water Resources Control Board; California Department of Water Resources: Sacramento, CA, USA, 2001.
Rajkumar T, Johnson ML. Prediction of salinity in San Francisco Bay-Delta using neural network. In2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat. No. 01CH37236) 2001 Oct 7 (Vol. 1, pp. 329-334). IEEE.
Wilbur, R. and Munevar, A. Integration of CALSIM and Artificial Neural Networks Models for Sacramento-San Joaquin Delta Flow-Salinity Relationships. In: Methodology for Flow and Salinity Estimates in the Sacramento-San Joaquin Delta and Suisun Marsh: Twenty-Second Annual Progress Report to the State Water Resources Control Board; California Department of Water Resources: Sacramento, CA, USA, 2001.
Rajkumar T, Thompson D. Optimization of a model to predict salinity intrusion in San Francisco Bay Estuary using a genetic algorithm. Integrated assessment and decision support. In: Proceedings of the first biennial meeting of the international environmental modelling and software society, vol. II, Lugano,2002. p. 148–53.
Mierzwa, M. CALSIM versus DSM2 ANN and G-model Comparisons. In: Methodology for Flow and Salinity Estimates in the Sacramento-San Joaquin Delta and Suisun Marsh: Twenty-Third Annual Progress Report to the State Water Resources Control Board; California Department of Water Resources: Sacramento, CA, USA, 2002.
Seneviratne, S. Short-Term Improvements to Artificial Neural Network Implementation in CALSIM. In: Methodology for Flow and Salinity Estimates in the Sacramento-San Joaquin Delta and Suisun Marsh: Twenty-Third Annual Progress Report to the State Water Resources Control Board; California Department of Water Resources: Sacramento, CA, USA, 2002.
Senevirante, S.; Wu, S. Enhanced Development of Flow-Salinity Relationships in the Delta Using Artificial Neural Networks: Incorporating Tidal Influence. In: Methodology for Flow and Salinity Estimates in the Sacramento-San Joaquin Delta and Suisun Marsh: 28th Annual Progress Report to the State Water Resources Control Board; California Department of Water Resources: Sacramento, CA, USA, 2007.
Chen, L. and Roy SB. DASM-T: Delta ANN Simulation Model for Turbidity, Phase 1 Results, Report prepared for the Metropolitan Water District of Southern California, 2012.
Chen L, Roy SB. Delta Turbidity ANN Model (DASM-T) Development Using DSM2: Phase 2 Results, Report prepared for the Metropolitan Water District of Southern California, 2013.
Chen L, Roy SB. Delta turbidity ANN model (DASM-T) development using DSM2: phase 3 results. Final Report to Metropolitan Water District of Southern California. 2014.
Roh, D.M.; He, M.; Bai, Z.; Sandhu, P.; Chung, F.; Ding, Z.; Qi, S.; Zhou, Y.; Hoang, R.; Namadi, P.; et al. Physics-Informed Neural Networks-Based Salinity Modeling in the Sacramento–San Joaquin Delta of California. Water 2023, 15, 2320. https://doi.org/10.3390/w15132320
Chen L, Roy SB, Hutton PH. Emulation of a process-based estuarine hydrodynamic model. Hydrological Sciences Journal. 2018 Apr 4;63(5):783-802.
He, M.; Zhong, L.; Sandhu, P.; Zhou, Y. Emulation of a Process-Based Salinity Generator for the Sacramento–San Joaquin Delta of California via Deep Learning. Water 2020, 12, 2088. https://doi.org/10.3390/w12082088
Jayasundara NC, Seneviratne SA, Reyes E, Chung FI. Artificial neural network for Sacramento–San Joaquin Delta flow–salinity relationship for CalSim 3.0. Journal of Water Resources Planning and Management. 2020 Apr 1;146(4):04020015.
Olivetti S, Gil MA, Sridharan VK, Hein AM. Merging computational fluid dynamics and machine learning to reveal animal migration strategies. Methods in Ecology and Evolution. 2021 Jul;12(7):1186-200.
Qi S, Bai Z, Ding Z, Jayasundara N, He M, Sandhu P, Seneviratne S, Kadir T. Enhanced artificial neural networks for salinity estimation and forecasting in the Sacramento-San Joaquin delta of California. Journal of Water Resources Planning and Management. 2021 Oct 1;147(10):04021069.
RMA. Delta Emergency Response Tool (Delta-ERT): Machine Learning Technical Documentation, Prepared for California Department of Water Resources. P.6, September 2021.
He, M., Zhou, Y., Kim, H., Nader-Tehrani, P., and Sandhu, P. DSM2 Water Temperature Modeling Input Extension: 1922–2015. In: Methodology for Flow and Salinity Estimates in the Sacramento-San Joaquin Delta and Suisun Marsh: 43rd Annual Progress Report to the State Water Resources Control Board; California Department of Water Resources: Sacramento, CA, USA, 2022.
Kim HS, He M, Sandhu P. Suspended sediment concentration estimation in the Sacramento‐San Joaquin Delta of California using long short‐term memory networks. Hydrological Processes. 2022 Oct;36(10):e14694.
Namadi P, He M, Sandhu P. Salinity-constituent conversion in South Sacramento-San Joaquin Delta of California via machine learning. Earth Science Informatics. 2022 Sep;15(3):1749-64.
Qi, S.; He, M.; Bai, Z.; Ding, Z.; Sandhu, P.; Zhou, Y.; Namadi, P.; Tom, B.; Hoang, R.; Anderson, J. Multi-Location Emulation of a Process-Based Salinity Model Using Machine Learning. Water 2022a, 14, 2030. https://doi.org/10.3390/w14132030
Qi, S.; He, M.; Bai, Z.; Ding, Z.; Sandhu, P.; Chung, F.; Namadi, P.; Zhou, Y.; Hoang, R.; Tom, B.; et al. Novel Salinity Modeling Using Deep Learning for the Sacramento–San Joaquin Delta of California. Water 2022b, 14, 3628. https://doi.org/10.3390/w14223628
Tillotson MD, Hassrick J, Collins AL, Phillis C. Machine learning forecasts to reduce risk of entrainment loss of endangered salmonids at large-scale water diversions in the Sacramento–San Joaquin Delta, California. San Francisco Estuary and Watershed Science. 2022;20(2).
Namadi P, He M, Sandhu P. Modeling ion constituents in the Sacramento-San Joaquin Delta using multiple machine learning approaches. Journal of Hydroinformatics. 2023 Nov 1;25(6):2541-60.
Qi, S.; He, M.; Hoang, R.; Zhou, Y.; Namadi, P.; Tom, B.; Sandhu, P.; Bai, Z.; Chung, F.; Ding, Z.; et al. Salinity Modeling Using Deep Learning with Data Augmentation and Transfer Learning. Water 2023, 15, 2482. https://doi.org/10.3390/w15132482
Roh, D.M.; He, M.; Bai, Z.; Sandhu, P.; Chung, F.; Ding, Z.; Qi, S.; Zhou, Y.; Hoang, R.; Namadi, P.; et al. Physics-Informed Neural Networks-Based Salinity Modeling in the Sacramento–San Joaquin Delta of California. Water 2023, 15, 2320. https://doi.org/10.3390/w15132320
Ahmadi A, Kazemi MH, Daccache A, Snyder RL. SolarET: A generalizable machine learning approach to estimate reference evapotranspiration from solar radiation. Agricultural Water Management. 2024 Apr 30;295:108779.
Swyers NM, Blake AR, Stumpner P, Burau JR, Burdick SM, Anwar MS. A machine learning tool for design of behavioral fish barriers in the Sacramento-San Joaquin River Delta. US Geological Survey; 2024.
Avouris DM, Hestir EL, Fleck J, Hansen JA, Bergamaschi BA. An integrated sensor network and data driven approach to satellite remote sensing of dissolved organic matter. Earth and Space Science. 2025 Sep;12(9):e2024EA004048.
Saha, G.K., Namadi, P.; He, M.; Sandhu, P. Machine Learning-Based Harmful Algal Blooms (HABs) Modeling in the Sacramento-San Joaquin Delta, 2024 Bay-Delta Science Conference, Sacramento, CA, USA. September 30-October 2, 2024.
Ahmadi A, Daccache A, He M, Namadi P, Bafti AG, Sandhu P, Bai Z, Snyder RL, Kadir T. Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learning. Journal of Hydrology: Regional Studies. 2025 Jun 1;59:102339.
Ateljevich, E. and DeGeorge, J. Analysis of Delta Salinity during Extended Drought – Pilot Project. Final Project Report for Delta Stewardship Council, Sacramento, CA, USA. 2025.
He, M.; Sandhu, P.; Namadi, P.; Reyes, E.; Guivetchi, K.; Chung, F. Protocols for Water and Environmental Modeling Using Machine Learning in California. Hydrology 2025, 12, 59. https://doi.org/10.3390/hydrology12030059
Namadi, P.; He, M.; Sandhu, P. Advancing Ion Constituent Simulations in California’s Sacramento–San Joaquin Delta Using Machine Learning Tools. Water 2025, 17, 1511. https://doi.org/10.3390/w17101511
Wang Z, Leung M, Mukhopadhyay S, Sunkara SV, Steinschneider S, Herman J, Abellera M, Kucharski J, Nederhoff K, Ruggiero P. A hybrid statistical–dynamical framework for compound coastal flooding analysis. Environmental Research Letters. 2025 Dec 6;20(1):014005.
Wang Z, Leung M, Mukhopadhyay S, Sunkara SV, Steinschneider S, Herman J, Abellera M, Kucharski J, Ruggiero P. Compound coastal flooding in San Francisco Bay under climate change. npj Natural Hazards. 2025 Jan 13;2(1):3.