Evaluation of 1-D tracer concentration profile in a small river by means of Multi-Layer Perceptron Neural Networks

A. Piotrowski, S. G. Wallis, J. J. Napiórkowski, P. M. Rowiński

    Research output: Contribution to journalArticle

    Abstract

    The prediction of temporal concentration profiles of a transported pollutant in a river is still a subject of ongoing research efforts worldwide. The present paper is aimed at studying the possibility of using Multi-Layer Perceptron Neural Networks to evaluate the whole concentration versus time profile at several cross-sections of a river under various flow conditions, using as little information about the river system as possible. In contrast with the earlier neural networks based work on longitudinal dispersion coefficients, this new approach relies more heavily on measurements of concentration collected during tracer tests over a range of flow conditions, but fewer hydraulic and morphological data are needed. The study is based upon 26 tracer experiments performed in a small river in Edinburgh, UK (Murray Burn) at various flow rates in a 540 m long reach. The only data used in this study were concentration measurements collected at 4 cross-sections, distances between the cross-sections and the injection site, time, as well as flow rate and water velocity, obtained according to the data measured at the 1st and 2nd cross-sections. The four main features of concentration versus time profiles at a particular cross-section, namely the peak concentration, the arrival time of the peak at the cross-section, and the shapes of the rising and falling limbs of the profile are modeled, and for each of them a separately designed neural network was used. There was also a variant investigated in which the conservation of the injected mass was assured by adjusting the predicted peak concentration. The neural network methods were compared with the unit peak attenuation curve concept. In general the neural networks predicted the main features of the concentration profiles satisfactorily. The predicted peak concentrations were generally better than those obtained using the unit peak attenuation method, and the method with mass-conservation assured generally performed better than the method that did not account for mass-conservation. Predictions of peak travel time were also better using the neural networks than the unit peak attenuation method. Including more data into the neural network training set clearly improved the prediction of the shapes of the concentration profiles. Similar improvements in peak concentration were less significant and the travel time prediction appeared to be largely unaffected.

    Original languageEnglish
    Pages (from-to)1883-1896
    Number of pages14
    JournalHydrology and Earth System Sciences
    Volume11
    Issue number6
    Publication statusPublished - 2007

    Fingerprint

    cross section
    tracer
    river
    prediction
    travel time
    arrival time
    river system
    limb
    evaluation
    method
    hydraulics
    pollutant
    experiment
    water
    rate

    Cite this

    Piotrowski, A., Wallis, S. G., Napiórkowski, J. J., & Rowiński, P. M. (2007). Evaluation of 1-D tracer concentration profile in a small river by means of Multi-Layer Perceptron Neural Networks. Hydrology and Earth System Sciences, 11(6), 1883-1896.
    Piotrowski, A. ; Wallis, S. G. ; Napiórkowski, J. J. ; Rowiński, P. M. / Evaluation of 1-D tracer concentration profile in a small river by means of Multi-Layer Perceptron Neural Networks. In: Hydrology and Earth System Sciences. 2007 ; Vol. 11, No. 6. pp. 1883-1896.
    @article{9b7ece6d202e4681b7f4d1c93a3a0076,
    title = "Evaluation of 1-D tracer concentration profile in a small river by means of Multi-Layer Perceptron Neural Networks",
    abstract = "The prediction of temporal concentration profiles of a transported pollutant in a river is still a subject of ongoing research efforts worldwide. The present paper is aimed at studying the possibility of using Multi-Layer Perceptron Neural Networks to evaluate the whole concentration versus time profile at several cross-sections of a river under various flow conditions, using as little information about the river system as possible. In contrast with the earlier neural networks based work on longitudinal dispersion coefficients, this new approach relies more heavily on measurements of concentration collected during tracer tests over a range of flow conditions, but fewer hydraulic and morphological data are needed. The study is based upon 26 tracer experiments performed in a small river in Edinburgh, UK (Murray Burn) at various flow rates in a 540 m long reach. The only data used in this study were concentration measurements collected at 4 cross-sections, distances between the cross-sections and the injection site, time, as well as flow rate and water velocity, obtained according to the data measured at the 1st and 2nd cross-sections. The four main features of concentration versus time profiles at a particular cross-section, namely the peak concentration, the arrival time of the peak at the cross-section, and the shapes of the rising and falling limbs of the profile are modeled, and for each of them a separately designed neural network was used. There was also a variant investigated in which the conservation of the injected mass was assured by adjusting the predicted peak concentration. The neural network methods were compared with the unit peak attenuation curve concept. In general the neural networks predicted the main features of the concentration profiles satisfactorily. The predicted peak concentrations were generally better than those obtained using the unit peak attenuation method, and the method with mass-conservation assured generally performed better than the method that did not account for mass-conservation. Predictions of peak travel time were also better using the neural networks than the unit peak attenuation method. Including more data into the neural network training set clearly improved the prediction of the shapes of the concentration profiles. Similar improvements in peak concentration were less significant and the travel time prediction appeared to be largely unaffected.",
    author = "A. Piotrowski and Wallis, {S. G.} and Napi{\'o}rkowski, {J. J.} and Rowiński, {P. M.}",
    year = "2007",
    language = "English",
    volume = "11",
    pages = "1883--1896",
    journal = "Hydrology and Earth System Sciences",
    issn = "1027-5606",
    publisher = "European Geosciences Union",
    number = "6",

    }

    Piotrowski, A, Wallis, SG, Napiórkowski, JJ & Rowiński, PM 2007, 'Evaluation of 1-D tracer concentration profile in a small river by means of Multi-Layer Perceptron Neural Networks', Hydrology and Earth System Sciences, vol. 11, no. 6, pp. 1883-1896.

    Evaluation of 1-D tracer concentration profile in a small river by means of Multi-Layer Perceptron Neural Networks. / Piotrowski, A.; Wallis, S. G.; Napiórkowski, J. J.; Rowiński, P. M.

    In: Hydrology and Earth System Sciences, Vol. 11, No. 6, 2007, p. 1883-1896.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - Evaluation of 1-D tracer concentration profile in a small river by means of Multi-Layer Perceptron Neural Networks

    AU - Piotrowski, A.

    AU - Wallis, S. G.

    AU - Napiórkowski, J. J.

    AU - Rowiński, P. M.

    PY - 2007

    Y1 - 2007

    N2 - The prediction of temporal concentration profiles of a transported pollutant in a river is still a subject of ongoing research efforts worldwide. The present paper is aimed at studying the possibility of using Multi-Layer Perceptron Neural Networks to evaluate the whole concentration versus time profile at several cross-sections of a river under various flow conditions, using as little information about the river system as possible. In contrast with the earlier neural networks based work on longitudinal dispersion coefficients, this new approach relies more heavily on measurements of concentration collected during tracer tests over a range of flow conditions, but fewer hydraulic and morphological data are needed. The study is based upon 26 tracer experiments performed in a small river in Edinburgh, UK (Murray Burn) at various flow rates in a 540 m long reach. The only data used in this study were concentration measurements collected at 4 cross-sections, distances between the cross-sections and the injection site, time, as well as flow rate and water velocity, obtained according to the data measured at the 1st and 2nd cross-sections. The four main features of concentration versus time profiles at a particular cross-section, namely the peak concentration, the arrival time of the peak at the cross-section, and the shapes of the rising and falling limbs of the profile are modeled, and for each of them a separately designed neural network was used. There was also a variant investigated in which the conservation of the injected mass was assured by adjusting the predicted peak concentration. The neural network methods were compared with the unit peak attenuation curve concept. In general the neural networks predicted the main features of the concentration profiles satisfactorily. The predicted peak concentrations were generally better than those obtained using the unit peak attenuation method, and the method with mass-conservation assured generally performed better than the method that did not account for mass-conservation. Predictions of peak travel time were also better using the neural networks than the unit peak attenuation method. Including more data into the neural network training set clearly improved the prediction of the shapes of the concentration profiles. Similar improvements in peak concentration were less significant and the travel time prediction appeared to be largely unaffected.

    AB - The prediction of temporal concentration profiles of a transported pollutant in a river is still a subject of ongoing research efforts worldwide. The present paper is aimed at studying the possibility of using Multi-Layer Perceptron Neural Networks to evaluate the whole concentration versus time profile at several cross-sections of a river under various flow conditions, using as little information about the river system as possible. In contrast with the earlier neural networks based work on longitudinal dispersion coefficients, this new approach relies more heavily on measurements of concentration collected during tracer tests over a range of flow conditions, but fewer hydraulic and morphological data are needed. The study is based upon 26 tracer experiments performed in a small river in Edinburgh, UK (Murray Burn) at various flow rates in a 540 m long reach. The only data used in this study were concentration measurements collected at 4 cross-sections, distances between the cross-sections and the injection site, time, as well as flow rate and water velocity, obtained according to the data measured at the 1st and 2nd cross-sections. The four main features of concentration versus time profiles at a particular cross-section, namely the peak concentration, the arrival time of the peak at the cross-section, and the shapes of the rising and falling limbs of the profile are modeled, and for each of them a separately designed neural network was used. There was also a variant investigated in which the conservation of the injected mass was assured by adjusting the predicted peak concentration. The neural network methods were compared with the unit peak attenuation curve concept. In general the neural networks predicted the main features of the concentration profiles satisfactorily. The predicted peak concentrations were generally better than those obtained using the unit peak attenuation method, and the method with mass-conservation assured generally performed better than the method that did not account for mass-conservation. Predictions of peak travel time were also better using the neural networks than the unit peak attenuation method. Including more data into the neural network training set clearly improved the prediction of the shapes of the concentration profiles. Similar improvements in peak concentration were less significant and the travel time prediction appeared to be largely unaffected.

    UR - http://www.scopus.com/inward/record.url?scp=36949001609&partnerID=8YFLogxK

    M3 - Article

    VL - 11

    SP - 1883

    EP - 1896

    JO - Hydrology and Earth System Sciences

    JF - Hydrology and Earth System Sciences

    SN - 1027-5606

    IS - 6

    ER -