Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves
Estimation of discharge flowing through rivers is an important aspect of water resource planning and management. The most common way to address this concern is to develop stage-discharge relationships at various river sections. Various computational techniques have been applied to develop discharge...
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6627011 |
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| author | Majid Niazkar Mohammad Zakwan |
| author_facet | Majid Niazkar Mohammad Zakwan |
| author_sort | Majid Niazkar |
| collection | DOAJ |
| description | Estimation of discharge flowing through rivers is an important aspect of water resource planning and management. The most common way to address this concern is to develop stage-discharge relationships at various river sections. Various computational techniques have been applied to develop discharge ratings and improve the accuracy of estimated discharges. In this regard, the present study explores the application of the novel hybrid multigene genetic programming-generalized reduced gradient (MGGP-GRG) technique for estimating river discharges for steady as well as unsteady flows. It also compares the MGGP-GRG performance with those of the commonly used optimization techniques. As a result, the rating curves of eight different rivers were developed using the conventional method, evolutionary algorithm (EA), the modified honey bee mating optimization (MHBMO) algorithm, artificial neural network (ANN), MGGP, and the hybrid MGGP-GRG technique. The comparison was conducted on the basis of several widely used performance evaluation criteria. It was observed that no model outperformed others for all datasets and metrics considered, which demonstrates that the best method may be different from one case to another one. Nevertheless, the ranking analysis indicates that the hybrid MGGP-GRG model overall performs the best in developing stage-discharge relationships for both single-value and loop rating curves. For instance, the hybrid MGGP-GRG technique improved sum of square of errors obtained by the conventional method between 4.5% and 99% for six out of eight datasets. Furthermore, EA, the MHBMO algorithm, and artificial intelligence (AI) models (ANN and MGGP) performed satisfactorily in some of the cases, while the idea of combining MGGP with GRG reveals that this hybrid method improved the performance of MGGP in this specific application. Unlike the black box nature of ANN, MGGP offers explicit equations for stream rating curves, which may be counted as one of the advantages of this AI model. |
| format | Article |
| id | doaj-art-c3e8bc4fddd94bc49f0139bd2182bbdf |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
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| series | Complexity |
| spelling | doaj-art-c3e8bc4fddd94bc49f0139bd2182bbdf2025-02-03T01:28:43ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66270116627011Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating CurvesMajid Niazkar0Mohammad Zakwan1Department of Civil and Environmental Engineering, Shiraz University, Shiraz, IranCivil Engineering Department, IIT Roorkee, Roorkee, IndiaEstimation of discharge flowing through rivers is an important aspect of water resource planning and management. The most common way to address this concern is to develop stage-discharge relationships at various river sections. Various computational techniques have been applied to develop discharge ratings and improve the accuracy of estimated discharges. In this regard, the present study explores the application of the novel hybrid multigene genetic programming-generalized reduced gradient (MGGP-GRG) technique for estimating river discharges for steady as well as unsteady flows. It also compares the MGGP-GRG performance with those of the commonly used optimization techniques. As a result, the rating curves of eight different rivers were developed using the conventional method, evolutionary algorithm (EA), the modified honey bee mating optimization (MHBMO) algorithm, artificial neural network (ANN), MGGP, and the hybrid MGGP-GRG technique. The comparison was conducted on the basis of several widely used performance evaluation criteria. It was observed that no model outperformed others for all datasets and metrics considered, which demonstrates that the best method may be different from one case to another one. Nevertheless, the ranking analysis indicates that the hybrid MGGP-GRG model overall performs the best in developing stage-discharge relationships for both single-value and loop rating curves. For instance, the hybrid MGGP-GRG technique improved sum of square of errors obtained by the conventional method between 4.5% and 99% for six out of eight datasets. Furthermore, EA, the MHBMO algorithm, and artificial intelligence (AI) models (ANN and MGGP) performed satisfactorily in some of the cases, while the idea of combining MGGP with GRG reveals that this hybrid method improved the performance of MGGP in this specific application. Unlike the black box nature of ANN, MGGP offers explicit equations for stream rating curves, which may be counted as one of the advantages of this AI model.http://dx.doi.org/10.1155/2021/6627011 |
| spellingShingle | Majid Niazkar Mohammad Zakwan Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves Complexity |
| title | Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves |
| title_full | Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves |
| title_fullStr | Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves |
| title_full_unstemmed | Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves |
| title_short | Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves |
| title_sort | assessment of artificial intelligence models for developing single value and loop rating curves |
| url | http://dx.doi.org/10.1155/2021/6627011 |
| work_keys_str_mv | AT majidniazkar assessmentofartificialintelligencemodelsfordevelopingsinglevalueandloopratingcurves AT mohammadzakwan assessmentofartificialintelligencemodelsfordevelopingsinglevalueandloopratingcurves |