RBFNN-based model for heavy metal prediction for different climatic and pollution conditions

Adnan Elzwayie, Ahmed El-shafie, Zaher Mundher Yaseen, Haitham Abdulmohsin Afan, Mohammed Falah Allawi

    Research output: Contribution to journalArticle

    22 Citations (Scopus)

    Abstract

    Heavy metal toxicity is a matter of considerable concern for environmental researchers. A highly cause of heavy metal toxicity in the aquatic environments is considered a serious issue that required full attention to understand in order to solve it. Heavy metal accumulation is a vital parameter for studying the water quality. Therefore, there is a need to develop an accurate prediction model for heavy metal accumulation. Recently, the artificial neural networks have been examined for similar prediction applications and showed great potential to tackle and detect its nonlinearity behavior. In this paper, radial basis function neural network algorithm has been utilized to investigate and mimic the relationship of heavy metals with the climatic and pollution conditions in lake water bodies. Thus, the present study was implemented in different climatic conditions (tropical “Malaysia” and arid “Libya”) as well as polluted and non-polluted lakes. Weekly records of physiochemical parameters data (e.g., pH, EC, WT, DO, TDS, TSS, CL, NO3, PO4 and SO4) and climatological parameters (e.g., air temperature, humidity and rainfall) were utilized as an input data for the modeling, whereas the heavy metal concentration was the output of the model. Three different scenarios for modeling the input architecture considering the climate, pollution or both have been investigated. In general, results obtained from all the scenarios are positively encouraging with high-performance accuracy. Furthermore, the results showed that an isolated model for each condition achieves a better prediction accuracy level rather than developing one general model for all conditions.

    Original languageEnglish
    Pages (from-to)1-13
    Number of pages13
    JournalNeural Computing and Applications
    DOIs
    Publication statusAccepted/In press - 13 Jan 2016

    Fingerprint

    Heavy metals
    Pollution
    Toxicity
    Lakes
    Neural networks
    Water quality
    Rain
    Atmospheric humidity
    Air
    Water
    Temperature

    Keywords

    • Heavy metals
    • Polluted and non-polluted lakes
    • Prediction modeling
    • Radial basis function neural network
    • Sensitivity analysis
    • Tropical and arid zone

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software

    Cite this

    RBFNN-based model for heavy metal prediction for different climatic and pollution conditions. / Elzwayie, Adnan; El-shafie, Ahmed; Yaseen, Zaher Mundher; Afan, Haitham Abdulmohsin; Allawi, Mohammed Falah.

    In: Neural Computing and Applications, 13.01.2016, p. 1-13.

    Research output: Contribution to journalArticle

    Elzwayie, Adnan ; El-shafie, Ahmed ; Yaseen, Zaher Mundher ; Afan, Haitham Abdulmohsin ; Allawi, Mohammed Falah. / RBFNN-based model for heavy metal prediction for different climatic and pollution conditions. In: Neural Computing and Applications. 2016 ; pp. 1-13.
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