ArticleJournal of environmental health science & engineering2022
Using Artificial Intelligent to Model Predict the Biological Resilience With an Emphasis on Population of cyanobacteria in Jajrood River in The Eastern Tehran, Iran.
Article in Journal of environmental health science & engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Prediction of bio-resilience in water resources such as rivers is important for better management of land-use systems and water resources. This study has proposed the use of artificial intelligent (AI) models for assessing the relationship among the biological conditions in Jajrood River. For this purpose, the qualitative monthly data of the river related to 2008-2018 were applied. Different resilience indicators for preparation of scenarios were determined using the canonical correlation analysis (CCA) method. Appropriate time-series scenarios (5scenarios) were modelled via Gene Expression Programming (GEP) plus Support Vector Machine (SVM), the bio-indicators were predicted. In order to reduce the error, the wavelet hybrids (W-GEP and W-SVM) were also used for modelling. Validation of the models was performed using Nash-Sutcliffe efficiency (E), root mean square error (RMSE), and mean absolute error (MAE). In all the models investigated, Scenario 3 and Scenario 4 had the highest and lowest accuracies as 0.98 and 0.33 in validation, respectively. The third scenario combined with NO3
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