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Работы, на которые ссылается эта работа

Работ: 48

Работа: In-stream Escherichia Coli Modeling Using high-temporal-resolution data with deep learning and process-based models

  1. Development of a Nowcasting System Using Machine Learning Approaches to Predict Fecal Contamination Levels at Recreational Beaches in Korea

    Yongeun Park, Minjeong Kim, Yakov Pachepsky +4

    Статья2018Цитирований: 4
    ABI

  2. Microbial Water Quality: Monitoring and Modeling

    Yakov Pachepsky, Ana Allende, Laurie Boithias +4

    Статья2018Цитирований: 4
    ABI

  3. The impact of socio-economic development and climate change on E. coli loads and concentrations in Kabul River, Pakistan

    Muhammad Shahid Iqbal, M. M. Majedul Islam, Nynke Hofstra

    Статья2018Цитирований: 4
    ABI

  4. Generalized Likelihood Uncertainty Estimation and Markov Chain Monte Carlo Simulation to Prioritize TMDL Pollutant Allocations

    Anurag Mishra, Ebrahim Ahmadisharaf, Brian Leslie Benham +5

    Статья2018Цитирований: 4
    ABI

  5. Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency

    Sandra Pool, Marc Vis, Jan Seibert

    Статья2018Цитирований: 4
    ABI

  6. Process‐Guided Deep Learning Predictions of Lake Water Temperature

    Jordan S. Read, Xiaowei Jia, Jared Willard +9

    Статья2019Цитирований: 4
    ABI

  7. An Integrated Deep Neural Network Approach for Large-Scale Water Quality Time Series Prediction

    Quanxi Dong, YongZhe Lin, Jing Bi +1

    Статья2019Цитирований: 4
    ABI

  8. A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

    Zhongrun Xiang, Jun Yan, İbrahim Demir

    Статья2020Цитирований: 4
    ABI

  9. Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data

    Kangyang Chen, Hexia Chen, Chuanlong Zhou +12

    Статья2019Цитирований: 4
    ABI

  10. New Methods for Microbiological Monitoring at Riverbank Filtration Sites

    Yasmin Adomat, Gerit-Hartmut Orzechowski, Marc Pelger +5

    Статья2020Цитирований: 4
    ABI

  11. Using convolutional neural network for predicting cyanobacteria concentrations in river water

    JongCheol Pyo, Lan Joo Park, Yakov Pachepsky +3

    Статья2020Цитирований: 4
    ABI

  12. Comparison of Alternative Methods to Simulate Bacteria Concentrations with HSPF Under Low-Flow Conditions

    Choung Hyun Seong, Brian Leslie Benham, Kyle M Hall +1

    Статья2013Цитирований: 4
    ABI

  13. WHY MODEL?

    S. E. VAN DER LEEUW

    Статья2004Цитирований: 4
    ABI

  14. Risk-based decision making to evaluate pollutant reduction scenarios

    Ebrahim Ahmadisharaf, Brian Leslie Benham

    Статья2019Цитирований: 3
    ABI

  15. You Only Train Once: Loss-Conditional Training of Deep Networks

    Alexey Dosovitskiy, Josip Djolonga

    Статья2020Цитирований: 3
    ABI

  16. Prediction of Bacterial Contamination Outbursts in Water Wells through Sparse Coding

    Levi Frolich, Dalit Vaizel‐Ohayon, Barak Fishbain

    Статья2017Цитирований: 2
    ABI

  17. Python (programming language)

    Jay Gajera, Arlene Campos, Yuranga Weerakkody

    Другое2019Цитирований: 2
    ABI

  18. Без названия

    ДругоеЦитирований: 1
    ABI

  19. Без названия

    ДругоеЦитирований: 1
    ABI

  20. Без названия

    ДругоеЦитирований: 1
    ABI

  21. Без названия

    ДругоеЦитирований: 1
    ABI

  22. Без названия

    ДругоеЦитирований: 1
    ABI

  23. Без названия

    ДругоеЦитирований: 1
    ABI