TY - GEN
T1 - Estimation of groundwater heavy metal pollution indices via an amalgam of stack ensemble learning
AU - Afrifa, George Yamoah
AU - Ansah-Narh, Theophilus
AU - Loh, Yvonne Sena Akosua
AU - Sakyi, Patrick Asamoah
AU - Chegbeleh, Larry Pax
AU - Yidana, Sandow Mark
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The Densu Basin is in the southern part of Ghana, with varied sources of contaminants because of agricultural activities, rapid urbanization, and industrialization. This paper evaluates heavy metal levels and specifically develops a prediction model for decision making. The prediction model adopted is a combination of artificial neural networks (as a first model-learner) and gradient boosting regressor (as a meta-learner). The model performed well with a strong correlation of 99.0 % between the predicted and the actual pollution indices, with root-mean-squared-log-errors of 0.025 and 0.012 for heavy metal pollution index and heavy metal evaluation index, respectively. Also, the results show that Mn and Pb are the two main relevant features necessary for the prediction of heavy metal indices.
AB - The Densu Basin is in the southern part of Ghana, with varied sources of contaminants because of agricultural activities, rapid urbanization, and industrialization. This paper evaluates heavy metal levels and specifically develops a prediction model for decision making. The prediction model adopted is a combination of artificial neural networks (as a first model-learner) and gradient boosting regressor (as a meta-learner). The model performed well with a strong correlation of 99.0 % between the predicted and the actual pollution indices, with root-mean-squared-log-errors of 0.025 and 0.012 for heavy metal pollution index and heavy metal evaluation index, respectively. Also, the results show that Mn and Pb are the two main relevant features necessary for the prediction of heavy metal indices.
KW - Densu basin
KW - Gradient boosting regressor
KW - Groundwater
KW - Heavy metal pollution
KW - Stack ensemble learning
UR - http://www.scopus.com/inward/record.url?scp=85127076930&partnerID=8YFLogxK
U2 - 10.1109/ICECET52533.2021.9698570
DO - 10.1109/ICECET52533.2021.9698570
M3 - Conference contribution
AN - SCOPUS:85127076930
T3 - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021
BT - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021
Y2 - 9 December 2021 through 10 December 2021
ER -