Enhanced Artificial Neural Networks for Salinity Estimation and Forecasting in the Sacramento-San Joaquin Delta of California-Reference-Cited by-同舟云学术

Enhanced Artificial Neural Networks for Salinity Estimation and Forecasting in the Sacramento-San Joaquin Delta of California

Author:

Qi Siyu¹,Bai Zhaojun²,Ding Zhi³,Jayasundara Nimal⁴^ORCID,He Minxue⁵,Sandhu Prabhjot⁶,Seneviratne Sanjaya⁵,Kadir Tariq⁵

Affiliation:

1. Graduate Student, Dept. of Electrical and Computer Engineering, Univ. of California, Davis, CA 95616 (corresponding author).

2. Professor, Dept. of Computer Science, Univ. of California, Davis, CA 95616.

3. Professor, Dept. of Electrical and Computer Engineering, Univ. of California, Davis, CA 95616.

4. Engineer, Bay Delta Office, California Dept. of Water Resources, 1416 9th St., Sacramento, CA 95614. ORCID: .

5. Senior Engineer, Bay Delta Office, California Dept. of Water Resources, 1416 9th St., Sacramento, CA 95614.

6. Supervising Engineer, Bay Delta Office, California Dept. of Water Resources, 1416 9th St., Sacramento, CA 95614.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Management, Monitoring, Policy and Law,Water Science and Technology,Geography, Planning and Development,Civil and Structural Engineering

Link

Reference45 articles.

1. Abadi M. et al. 2015. “TensorFlow: Large-scale machine learning on heterogeneous systems.” Accessed March 21 2019. https://www.tensorflow.org/.

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