Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis
In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potent...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-04-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016820306840 |
| _version_ | 1819104315292254208 |
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| author | Mohammad Ehteram Ahmad Ferdowsi Mahtab Faramarzpour Ahmed Mohammed Sami Al-Janabi Nadhir Al-Ansari Neeraj Dhanraj Bokde Zaher Mundher Yaseen |
| author_facet | Mohammad Ehteram Ahmad Ferdowsi Mahtab Faramarzpour Ahmed Mohammed Sami Al-Janabi Nadhir Al-Ansari Neeraj Dhanraj Bokde Zaher Mundher Yaseen |
| author_sort | Mohammad Ehteram |
| collection | DOAJ |
| description | In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level. |
| first_indexed | 2024-12-22T02:04:24Z |
| format | Article |
| id | doaj.art-9104d14ada9a45c2a82f462566961241 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-12-22T02:04:24Z |
| publishDate | 2021-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-9104d14ada9a45c2a82f4625669612412022-12-21T18:42:33ZengElsevierAlexandria Engineering Journal1110-01682021-04-0160221932208Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysisMohammad Ehteram0Ahmad Ferdowsi1Mahtab Faramarzpour2Ahmed Mohammed Sami Al-Janabi3Nadhir Al-Ansari4Neeraj Dhanraj Bokde5Zaher Mundher Yaseen6Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, IranDepartment of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, IranDepartment of Civil Engineering, Robat Karim Branch, Islamic Azad University, Tehran, IranDepartment of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, MalaysiaCivil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, SwedenDepartment of Engineering - Renewable Energy and Thermodynamics, Aarhus University, 8000, DenmarkInstitute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam; Corresponding author.In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level.http://www.sciencedirect.com/science/article/pii/S1110016820306840Hybrid artificial intelligenceUrmia LakeWater level predictionMachine learningWater resources |
| spellingShingle | Mohammad Ehteram Ahmad Ferdowsi Mahtab Faramarzpour Ahmed Mohammed Sami Al-Janabi Nadhir Al-Ansari Neeraj Dhanraj Bokde Zaher Mundher Yaseen Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis Alexandria Engineering Journal Hybrid artificial intelligence Urmia Lake Water level prediction Machine learning Water resources |
| title | Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis |
| title_full | Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis |
| title_fullStr | Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis |
| title_full_unstemmed | Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis |
| title_short | Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis |
| title_sort | hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis |
| topic | Hybrid artificial intelligence Urmia Lake Water level prediction Machine learning Water resources |
| url | http://www.sciencedirect.com/science/article/pii/S1110016820306840 |
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