Home
World Journal of Advanced Research and Reviews
International Journal with High Impact Factor for fast publication of Research and Review articles

Main navigation

  • Home
    • Journal Information
    • Editorial Board Members
    • Reviewer Panel
    • Abstracting and Indexing
    • Journal Policies
    • Our CrossMark Policy
    • Publication Ethics
    • Issue in Progress
    • Current Issue
    • Past Issues
    • Instructions for Authors
    • Article processing fee
    • Track Manuscript Status
    • Get Publication Certificate
    • Join Editorial Board
    • Join Reviewer Panel
  • Contact us
  • Downloads

eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in March 2026 (Volume 29, Issue 3) Submit manuscript

Artificial neural network modelling of cyanide transport in a soil contaminated with cassava wastewater

Breadcrumb

  • Home
  • Artificial neural network modelling of cyanide transport in a soil contaminated with cassava wastewater

Idongesit Friday ETUK *, Benjamin Reuben ETUK and Innocent Oseribho OBOH

Department of Chemical Engineering, University of Uyo, Uyo, Nigeria.
 
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(01), 550-562
Article DOI: 10.30574/wjarr.2024.23.1.1909
DOI url: https://doi.org/10.30574/wjarr.2024.23.1.1909
 
Received on 18 May 2024; revised on 03 July 2024; accepted on 06 July 2024
 
Understanding the transport of cyanide present in cassava wastewater is very crucial especially when choosing the right remediation technique to employ to restore the soil’s fertility. In this study, the concentrations of cyanide for both dry and wet seasons were determined on two sites of area (2.5 m by 2.5 m) respectively. Physico-chemical characterizations were carried out on the soil samples collected. Using the artificial neural network (ANN) approach for this study, a total of 91 samples were used for each season which were divided into 63 samples for training, 15 samples for validation, and 15 samples for testing. Three input parameters consisting of (depth, time, and measured concentration) were used to obtain one output parameter (predicted concentration). The performance of the ANN model was evaluated using metrics such as MSE, RMSE, R, and R2. The results showed that ANN model was able to predict the concentration of cyanide for both seasons as evident in their MSE and RMSE values close to zero and the R2 values close to unity. However, the data set for dry season had better ANN predictability than that of the wet season as shown by the R2 values for the training, validation and testing of the dry season to be 0.9954, 0.9978, and 0.9942, while the wet season is 0.9944, 0.7887 and 0.7793 respectively. Therefore, ANN model could be used to predict the concentration of cyanide at varying depths and times.
 
Cyanide; Cassava Wastewater; Artificial Neural Network (ANN); Prediction; Transport
 
https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2024-1909.pdf

Preview Article PDF

Idongesit Friday ETUK, Benjamin Reuben ETUK and Innocent Oseribho OBOH. Artificial neural network modelling of cyanide transport in a soil contaminated with cassava wastewater. World Journal of Advanced Research and Reviews, 2024, 23(1), 550-562. Article DOI: https://doi.org/10.30574/wjarr.2024.23.1.1909

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.

All statements, opinions, and data contained in this publication are solely those of the individual author(s) and contributor(s). The journal, editors, reviewers, and publisher disclaim any responsibility or liability for the content, including accuracy, completeness, or any consequences arising from its use.

Get Certificates

Get Publication Certificate

Download LoA

Check Corssref DOI details

Issue details

Issue Cover Page

Editorial Board

Table of content

Copyright © 2026 World Journal of Advanced Research and Reviews - All rights reserved

Developed & Designed by VS Infosolution