Multiple regression and group method of data handling-based models for predicting arsenic concentration in sedimentary phosphate rock

Marine sedimentary phosphate rock is the primary source for manufacturing phosphate fertilizers. It is composed mainly of phosphorus and other elements. Some of these elements, including heavy metals, occur as trace elements. Notably, arsenic, classified as a Group I human carcinogen, is among them....

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Published in:International journal of environmental science and technology (Tehran) Vol. 21; no. 9; pp. 6531 - 6552
Main Authors: Dassamiour, M., Samai, D., Faghmous, N., Boustila, R.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024
Subjects:
algorithms
Aquatic Pollution
arsenic
calcium oxide
carcinogens
data collection
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Science and Engineering
equations
heavy metals
humans
neurons
Original Paper
phosphorus
prediction
regression analysis
rock phosphate
Soil Science & Conservation
Waste Water Technology
Water Management
Water Pollution Control
Accuracy
Sensitivity analysis
Neural networks
Heavy metals
Combinatorial algorithm
ISSN:1735-1472, 1735-2630
Online Access:Get full text
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Summary:Marine sedimentary phosphate rock is the primary source for manufacturing phosphate fertilizers. It is composed mainly of phosphorus and other elements. Some of these elements, including heavy metals, occur as trace elements. Notably, arsenic, classified as a Group I human carcinogen, is among them. This research aims to develop an explicit model equation for predicting the concentration of arsenic in phosphate rock using backward stepwise multiple regression and two-group method of data handling algorithms: the combinatorial and type neural networks. A database of 277 datasets was compiled from thirteen reputable references for this purpose. The models’ input data are the major oxide contents (P 2 O 5 , CaO, MgO, SiO 2 , Al 2 O 3 , Fe 2 O 3 , K 2 O, and Na 2 O) in the phosphate samples. Three models of multiple regression and twenty-one models were constructed by combining various parameters, like the transformation function of input data and the neuron activation function. The performance of the proposed models was evaluated using metrics such as root mean square error, mean absolute error, and the coefficient of determination. In addition, sensitivity analysis was performed to inspect the impact of input variables on the model output. The results showed that the combinatorial algorithm provides the best model for predicting arsenic concentration with the highest level of accuracy in prediction. The validation results suggest that the combinatorial algorithm can be considered a promising approach for predicting heavy metal concentrations in phosphate rock with improved accuracy, and the model’s explicit form makes its practical application highly feasible.
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ISSN:1735-1472
1735-2630
DOI:10.1007/s13762-023-05452-0