Groundwater qualitative prediction using artificial neural networks and support vector machine model case Study: Sirjan plain
Groundwater resources are one considered as one of the most common and important resources of drinking, agriculture and industry water. Due to the lowering of groundwater levels and its volatility, groundwater quality is of utmost importance. The aim of this study is to identify the predictive abili...
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| Format: | Article |
| Language: | English |
| Published: |
Action for Sustainable Efficacious Development and Awareness
2016-06-01
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| Series: | Environment Conservation Journal |
| Subjects: | |
| Online Access: | https://journal.environcj.in/index.php/ecj/article/view/346 |
| _version_ | 1818776492216156160 |
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| author | Tahereh Darini Amir Jalalkamali |
| author_facet | Tahereh Darini Amir Jalalkamali |
| author_sort | Tahereh Darini |
| collection | DOAJ |
| description | Groundwater resources are one considered as one of the most common and important resources of drinking, agriculture and industry water. Due to the lowering of groundwater levels and its volatility, groundwater quality is of utmost importance. The aim of this study is to identify the predictive ability of artificial neural network of Multi-Layer Perceptron (MLP) and Support Vector Machine model and adaptive neuro-fuzzy inference system in which the quality of groundwater in Sirjan Plain has been predicted. A case study was conducted on the Sirjan Plain located in the city of Sirjan in Kerman province. For this purpose, the data of rainfall, the water level in wells and UTM coordinates of intended wells have been used as input combinations and qualitative parameters of the water of wells as output parameters. After initial processes such as normalization, for double-layer neural network, 85% of data were used for training and 15% for validation, and the same ration were applied to ANFIS and SVM. After reviewing the fitness statistical criteria such as correlation coefficient (R), and Root Mean Square Error (RMSE), it was observed that neural network presented an acceptable result compared to SVM and ANFIS models. |
| first_indexed | 2024-12-18T11:13:47Z |
| format | Article |
| id | doaj.art-7359ec7d6cce4dc8a5e6478aba2fc064 |
| institution | Directory Open Access Journal |
| issn | 0972-3099 2278-5124 |
| language | English |
| last_indexed | 2024-12-18T11:13:47Z |
| publishDate | 2016-06-01 |
| publisher | Action for Sustainable Efficacious Development and Awareness |
| record_format | Article |
| series | Environment Conservation Journal |
| spelling | doaj.art-7359ec7d6cce4dc8a5e6478aba2fc0642022-12-21T21:09:57ZengAction for Sustainable Efficacious Development and AwarenessEnvironment Conservation Journal0972-30992278-51242016-06-01171&210.36953/ECJ.2016.171210Groundwater qualitative prediction using artificial neural networks and support vector machine model case Study: Sirjan plainTahereh Darini 0Amir Jalalkamali 1Department of water engineering, Kerman Branch, Islamic Azad University, KermanDepartment of water engineering, Kerman Branch, Islamic Azad University, KermanGroundwater resources are one considered as one of the most common and important resources of drinking, agriculture and industry water. Due to the lowering of groundwater levels and its volatility, groundwater quality is of utmost importance. The aim of this study is to identify the predictive ability of artificial neural network of Multi-Layer Perceptron (MLP) and Support Vector Machine model and adaptive neuro-fuzzy inference system in which the quality of groundwater in Sirjan Plain has been predicted. A case study was conducted on the Sirjan Plain located in the city of Sirjan in Kerman province. For this purpose, the data of rainfall, the water level in wells and UTM coordinates of intended wells have been used as input combinations and qualitative parameters of the water of wells as output parameters. After initial processes such as normalization, for double-layer neural network, 85% of data were used for training and 15% for validation, and the same ration were applied to ANFIS and SVM. After reviewing the fitness statistical criteria such as correlation coefficient (R), and Root Mean Square Error (RMSE), it was observed that neural network presented an acceptable result compared to SVM and ANFIS models.https://journal.environcj.in/index.php/ecj/article/view/346Quantitative predictiongroundwaterSVMANFISMLP |
| spellingShingle | Tahereh Darini Amir Jalalkamali Groundwater qualitative prediction using artificial neural networks and support vector machine model case Study: Sirjan plain Environment Conservation Journal Quantitative prediction groundwater SVM ANFIS MLP |
| title | Groundwater qualitative prediction using artificial neural networks and support vector machine model case Study: Sirjan plain |
| title_full | Groundwater qualitative prediction using artificial neural networks and support vector machine model case Study: Sirjan plain |
| title_fullStr | Groundwater qualitative prediction using artificial neural networks and support vector machine model case Study: Sirjan plain |
| title_full_unstemmed | Groundwater qualitative prediction using artificial neural networks and support vector machine model case Study: Sirjan plain |
| title_short | Groundwater qualitative prediction using artificial neural networks and support vector machine model case Study: Sirjan plain |
| title_sort | groundwater qualitative prediction using artificial neural networks and support vector machine model case study sirjan plain |
| topic | Quantitative prediction groundwater SVM ANFIS MLP |
| url | https://journal.environcj.in/index.php/ecj/article/view/346 |
| work_keys_str_mv | AT taherehdarini groundwaterqualitativepredictionusingartificialneuralnetworksandsupportvectormachinemodelcasestudysirjanplain AT amirjalalkamali groundwaterqualitativepredictionusingartificialneuralnetworksandsupportvectormachinemodelcasestudysirjanplain |