Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Accurate prediction of salinity in Chott Djerid shallow aquifers, southern Tunisia: Machine learning model development
A backpropagation neural network (BPNN) was used to predict salinity levels in the Chott Djerid shallow aquifers. A set of 51 water samples was collected from the Chott Djerid plio-quaternary aquifers for geochemical analysis. Major elements and nitrates were ascertained by using high performance liquid-ion chromatography. The BPNN was trained on a dataset of 51 water samples with variable geochemical parameters. Our results indicated a high accuracy when applying a model with 13 inputs, 1 hidden layer (6 neurons) and 1 output (TDS in mg/L). The collected data were split into 80% for training the model and 20% for testing and cross validation. The result was evaluated using various statistical performance criteria (i.e., MSE, RMSE, R2, SSE, SD, Accuracy, Sensitivity, specificity, and Kappa test); it showed that BPNN model properly predicted the salinity of the Chott Djerid plio-quaterny water samples (RMSE = 0.0402; R2 = 0.9721 and SSE = 0.0146). The BPNN was able to capture the complex relationship between salinity levels and other aquifer parameters. The potential application of BPNNs for predicting salinity levels in shallow aquifers was crucial in supporting decision-makers for water management; it provided valuable insights into the salinity fluctuation of the studied shallow aquifers.
Accurate prediction of salinity in Chott Djerid shallow aquifers, southern Tunisia: Machine learning model development
A backpropagation neural network (BPNN) was used to predict salinity levels in the Chott Djerid shallow aquifers. A set of 51 water samples was collected from the Chott Djerid plio-quaternary aquifers for geochemical analysis. Major elements and nitrates were ascertained by using high performance liquid-ion chromatography. The BPNN was trained on a dataset of 51 water samples with variable geochemical parameters. Our results indicated a high accuracy when applying a model with 13 inputs, 1 hidden layer (6 neurons) and 1 output (TDS in mg/L). The collected data were split into 80% for training the model and 20% for testing and cross validation. The result was evaluated using various statistical performance criteria (i.e., MSE, RMSE, R2, SSE, SD, Accuracy, Sensitivity, specificity, and Kappa test); it showed that BPNN model properly predicted the salinity of the Chott Djerid plio-quaterny water samples (RMSE = 0.0402; R2 = 0.9721 and SSE = 0.0146). The BPNN was able to capture the complex relationship between salinity levels and other aquifer parameters. The potential application of BPNNs for predicting salinity levels in shallow aquifers was crucial in supporting decision-makers for water management; it provided valuable insights into the salinity fluctuation of the studied shallow aquifers.
Accurate prediction of salinity in Chott Djerid shallow aquifers, southern Tunisia: Machine learning model development
Zohra Kraiem (Autor:in) / Kamel Zouari (Autor:in) / Najiba Chkir (Autor:in)
2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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