A novel AI-based approach for modelling the fate, transportation and prediction of chromium in rivers and agricultural crops: A case study in Iran
Chromium (Cr) pollution caused by the discharge of industrial wastewater into rivers poses a significant threat to the environment, aquatic and human life, as well as agricultural crops irrigated by these rivers. This paper employs artificial intelligence (AI) to introduce a new framework for modeli...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Ecotoxicology and Environmental Safety |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S014765132300773X |
| _version_ | 1797683212680429568 |
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| author | Ali Montazeri Benyamin Chahkandi Mohammad Gheibi Mohammad Eftekhari Stanisław Wacławek Kourosh Behzadian Luiza C. Campos |
| author_facet | Ali Montazeri Benyamin Chahkandi Mohammad Gheibi Mohammad Eftekhari Stanisław Wacławek Kourosh Behzadian Luiza C. Campos |
| author_sort | Ali Montazeri |
| collection | DOAJ |
| description | Chromium (Cr) pollution caused by the discharge of industrial wastewater into rivers poses a significant threat to the environment, aquatic and human life, as well as agricultural crops irrigated by these rivers. This paper employs artificial intelligence (AI) to introduce a new framework for modeling the fate, transport, and estimation of Cr from its point of discharge into the river until it is absorbed by agricultural products. The framework is demonstrated through its application to the case study River, which serves as the primary water resource for tomato production irrigation in Mashhad city, Iran. Measurements of Cr concentration are taken at three different river depths and in tomato leaves from agricultural lands irrigated by the river, allowing for the identification of bioaccumulation effects. By employing boundary conditions and smart algorithms, various aspects of control systems are evaluated. The concentration of Cr in crops exhibits an accumulative trend, reaching up to 1.29 µg/g by the time of harvest. Using data collected from the case study and exploring different scenarios, AI models are developed to estimate the Cr concentration in tomato leaves. The tested AI models include linear regression (LR), neural network (NN) classifier, and NN regressor, yielding goodness-of-fit values (R2) of 0.931, 0.874, and 0.946, respectively. These results indicate that the NN regressor is the most accurate model, followed by the LR, for estimating Cr levels in tomato leaves. |
| first_indexed | 2024-03-12T00:11:17Z |
| format | Article |
| id | doaj.art-a0485777b91349d6810fdae5ed901acf |
| institution | Directory Open Access Journal |
| issn | 0147-6513 |
| language | English |
| last_indexed | 2024-03-12T00:11:17Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecotoxicology and Environmental Safety |
| spelling | doaj.art-a0485777b91349d6810fdae5ed901acf2023-09-16T05:28:23ZengElsevierEcotoxicology and Environmental Safety0147-65132023-09-01263115269A novel AI-based approach for modelling the fate, transportation and prediction of chromium in rivers and agricultural crops: A case study in IranAli Montazeri0Benyamin Chahkandi1Mohammad Gheibi2Mohammad Eftekhari3Stanisław Wacławek4Kourosh Behzadian5Luiza C. Campos6Department of Civil Engineering, Shahrood University of Technology, Shahrood, IranSchool of Civil Engineering, University of Tehran, Tehran, IranAssociation of Talent under Liberty in Technology (TULTECH), 10615 Tallinn, Estonia; Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, Studentská 1402/2, 461 17 Liberec 1, Czech RepublicDepartment of Chemistry, Faculty of Sciences, University of Neyshabur, Neyshabur, IranInstitute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, Studentská 1402/2, 461 17 Liberec 1, Czech RepublicSchool of Computing and Engineering, University of West London, London W5 5RF, UK; Department of Civil, Environmental and Geomatic Engineering, University College London, Gower St, London WC1E 6BT, UKDepartment of Civil, Environmental and Geomatic Engineering, University College London, Gower St, London WC1E 6BT, UK; Corresponding author.Chromium (Cr) pollution caused by the discharge of industrial wastewater into rivers poses a significant threat to the environment, aquatic and human life, as well as agricultural crops irrigated by these rivers. This paper employs artificial intelligence (AI) to introduce a new framework for modeling the fate, transport, and estimation of Cr from its point of discharge into the river until it is absorbed by agricultural products. The framework is demonstrated through its application to the case study River, which serves as the primary water resource for tomato production irrigation in Mashhad city, Iran. Measurements of Cr concentration are taken at three different river depths and in tomato leaves from agricultural lands irrigated by the river, allowing for the identification of bioaccumulation effects. By employing boundary conditions and smart algorithms, various aspects of control systems are evaluated. The concentration of Cr in crops exhibits an accumulative trend, reaching up to 1.29 µg/g by the time of harvest. Using data collected from the case study and exploring different scenarios, AI models are developed to estimate the Cr concentration in tomato leaves. The tested AI models include linear regression (LR), neural network (NN) classifier, and NN regressor, yielding goodness-of-fit values (R2) of 0.931, 0.874, and 0.946, respectively. These results indicate that the NN regressor is the most accurate model, followed by the LR, for estimating Cr levels in tomato leaves.http://www.sciencedirect.com/science/article/pii/S014765132300773XArtificial IntelligenceBio-magnificationChromiumFate and transport modellingHeavy metal predictionWater-food-pollution nexus |
| spellingShingle | Ali Montazeri Benyamin Chahkandi Mohammad Gheibi Mohammad Eftekhari Stanisław Wacławek Kourosh Behzadian Luiza C. Campos A novel AI-based approach for modelling the fate, transportation and prediction of chromium in rivers and agricultural crops: A case study in Iran Ecotoxicology and Environmental Safety Artificial Intelligence Bio-magnification Chromium Fate and transport modelling Heavy metal prediction Water-food-pollution nexus |
| title | A novel AI-based approach for modelling the fate, transportation and prediction of chromium in rivers and agricultural crops: A case study in Iran |
| title_full | A novel AI-based approach for modelling the fate, transportation and prediction of chromium in rivers and agricultural crops: A case study in Iran |
| title_fullStr | A novel AI-based approach for modelling the fate, transportation and prediction of chromium in rivers and agricultural crops: A case study in Iran |
| title_full_unstemmed | A novel AI-based approach for modelling the fate, transportation and prediction of chromium in rivers and agricultural crops: A case study in Iran |
| title_short | A novel AI-based approach for modelling the fate, transportation and prediction of chromium in rivers and agricultural crops: A case study in Iran |
| title_sort | novel ai based approach for modelling the fate transportation and prediction of chromium in rivers and agricultural crops a case study in iran |
| topic | Artificial Intelligence Bio-magnification Chromium Fate and transport modelling Heavy metal prediction Water-food-pollution nexus |
| url | http://www.sciencedirect.com/science/article/pii/S014765132300773X |
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