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  • IWA Publishing  (2)
  • 1
    Online Resource
    Online Resource
    Influences of anthropogenic activities on water quality in the Saigon River, Ho Chi Minh City
    IWA Publishing ; 2022
    In:  Journal of Water and Health Vol. 20, No. 3 ( 2022-03-01), p. 491-504
    Details
    In: Journal of Water and Health, IWA Publishing, Vol. 20, No. 3 ( 2022-03-01), p. 491-504
    Abstract: Water quality for the surface water along the Saigon River in Ho Chi Minh City was assessed for four groups of water samples collected at the agricultural, industrial, residential, and less impacted areas. A variety of parameters indicating water quality including physicochemical parameters, nutrients, heavy metals, and antibiotic residues were measured for both the rainy and dry seasons, two main tropical seasons in HCM City using the standard methods. The results showed that the river water in the rainy season was detected with significantly higher values of turbidity, BOD5, PO4-P, NH4-N, NO3-N; and lower values of pH, temperature, conductivity, DO, salinity, Cu, Zn, As, Ni, Hg compared to that in the dry season. Sulfamethoxazole and trimethoprim were highly detected in the industrial areas compared to the agricultural and residential areas. Multivariate analyses suggested that the industrial and residential activities were more important contributors to the pollution of the Saigon River than the agricultural activities in HCM City.
    Type of Medium: Online Resource
    ISSN: 1477-8920 , 1996-7829
    URL: Article
    DOI: 10.2166/wh.2022.233
    Language: English
    Publisher: IWA Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 2113236-7
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Deep learning convolutional neural network in rainfall–runoff modelling
    IWA Publishing ; 2020
    In:  Journal of Hydroinformatics Vol. 22, No. 3 ( 2020-05-01), p. 541-561
    Details
    In: Journal of Hydroinformatics, IWA Publishing, Vol. 22, No. 3 ( 2020-05-01), p. 541-561
    Abstract: Rainfall–runoff modelling is complicated due to numerous complex interactions and feedback in the water cycle among precipitation and evapotranspiration processes, and also geophysical characteristics. Consequently, the lack of geophysical characteristics such as soil properties leads to difficulties in developing physical and analytical models when traditional statistical methods cannot simulate rainfall–runoff accurately. Machine learning techniques with data-driven methods, which can capture the nonlinear relationship between prediction and predictors, have been rapidly developed in the last decades and have many applications in the field of water resources. This study attempts to develop a novel 1D convolutional neural network (CNN), a deep learning technique, with a ReLU activation function for rainfall–runoff modelling. The modelling paradigm includes applying two convolutional filters in parallel to separate time series, which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series. The developed modelling framework is evaluated with measured data at Chau Doc and Can Tho hydro-meteorological stations in the Vietnamese Mekong Delta. The proposed model results are compared with simulations of long short-term memory (LSTM) and traditional models. Both CNN and LSTM have better performance than the traditional models, and the statistical performance of the CNN model is slightly better than the LSTM results. We demonstrate that the convolutional network is suitable for regression-type problems and can effectively learn dependencies in and between the series without the need for a long historical time series, is a time-efficient and easy to implement alternative to recurrent-type networks and tends to outperform linear and recurrent models.
    Type of Medium: Online Resource
    ISSN: 1464-7141 , 1465-1734
    URL: Article
    DOI: 10.2166/hydro.2020.095
    Language: English
    Publisher: IWA Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 2020923-X
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