Forecasting of Rainfall across River Basins Using Soft Computing Techniques: The Case Study of the Upper Brahmani Basin (India)
Floods are potential natural disasters that might disrupt human activities, resulting in severe losses of life and property in a region. Excessive rainfall is one of the reasons for flooding, especially in the downstream areas of a catchment. Because of their complexity, understanding and forecastin...
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MDPI AG
2023-01-01
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| Online Access: | https://www.mdpi.com/2073-4441/15/3/499 |
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| author | M. Uma Maheswar Rao Kanhu Charan Patra Suvendu Kumar Sasmal Anurag Sharma Giuseppe Oliveto |
| author_facet | M. Uma Maheswar Rao Kanhu Charan Patra Suvendu Kumar Sasmal Anurag Sharma Giuseppe Oliveto |
| author_sort | M. Uma Maheswar Rao |
| collection | DOAJ |
| description | Floods are potential natural disasters that might disrupt human activities, resulting in severe losses of life and property in a region. Excessive rainfall is one of the reasons for flooding, especially in the downstream areas of a catchment. Because of their complexity, understanding and forecasting rainfalls are challenging. This paper aims to apply the Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting average monthly rainfalls by considering several surface weather parameters as predictors. The Upper Brahmani Basin, which extends over 17,504 km<sup>2</sup>, was considered as a study area. Therefore, an ANFIS model was developed to forecast rainfalls using 37 years of climate data from 1983 to 2020. A hybrid model with six membership functions provided the best forecast for the area under study. The suggested method blends neural network learning capabilities with transparent language representations of fuzzy systems; 75% of data (from 1983 to 2006) was set aside for training and 25% (from 2006 to 2020) for testing. The Gaussian membership function with the hybrid algorithm provided satisfactory accuracy with R-values for training and testing equal to 0.90 and 0.87, respectively. Therefore, a new promising forecasting model was developed for the period from 2021 to 2030. The highest rainfall was forecasted for the period June–August, which is a striking characteristic of the monsoon climate. The study area is relatively close to the equatorial warm climate region. Hence, the proposed model might be of consistent use for regions lying in similar latitudes. |
| first_indexed | 2024-03-11T09:20:00Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T09:20:00Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-8cc4a6a0f6bc47d6a69b95e025e7f21b2023-11-16T18:23:30ZengMDPI AGWater2073-44412023-01-0115349910.3390/w15030499Forecasting of Rainfall across River Basins Using Soft Computing Techniques: The Case Study of the Upper Brahmani Basin (India)M. Uma Maheswar Rao0Kanhu Charan Patra1Suvendu Kumar Sasmal2Anurag Sharma3Giuseppe Oliveto4Department of Civil Engineering, National Institute of Technology Rourkela, Rourkela 769008, IndiaDepartment of Civil Engineering, National Institute of Technology Rourkela, Rourkela 769008, IndiaDepartment of Civil Engineering, National Institute of Technology Rourkela, Rourkela 769008, IndiaDepartment of Civil Engineering, National Institute of Technology Rourkela, Rourkela 769008, IndiaSchool of Engineering, University of Basilicata, Viale dell’Ateneo Lucano, 10, 85100 Potenza, ItalyFloods are potential natural disasters that might disrupt human activities, resulting in severe losses of life and property in a region. Excessive rainfall is one of the reasons for flooding, especially in the downstream areas of a catchment. Because of their complexity, understanding and forecasting rainfalls are challenging. This paper aims to apply the Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting average monthly rainfalls by considering several surface weather parameters as predictors. The Upper Brahmani Basin, which extends over 17,504 km<sup>2</sup>, was considered as a study area. Therefore, an ANFIS model was developed to forecast rainfalls using 37 years of climate data from 1983 to 2020. A hybrid model with six membership functions provided the best forecast for the area under study. The suggested method blends neural network learning capabilities with transparent language representations of fuzzy systems; 75% of data (from 1983 to 2006) was set aside for training and 25% (from 2006 to 2020) for testing. The Gaussian membership function with the hybrid algorithm provided satisfactory accuracy with R-values for training and testing equal to 0.90 and 0.87, respectively. Therefore, a new promising forecasting model was developed for the period from 2021 to 2030. The highest rainfall was forecasted for the period June–August, which is a striking characteristic of the monsoon climate. The study area is relatively close to the equatorial warm climate region. Hence, the proposed model might be of consistent use for regions lying in similar latitudes.https://www.mdpi.com/2073-4441/15/3/499rainfall forecastingupper Brahmani basinadaptive neuro-fuzzy inference system (ANFIS) |
| spellingShingle | M. Uma Maheswar Rao Kanhu Charan Patra Suvendu Kumar Sasmal Anurag Sharma Giuseppe Oliveto Forecasting of Rainfall across River Basins Using Soft Computing Techniques: The Case Study of the Upper Brahmani Basin (India) Water rainfall forecasting upper Brahmani basin adaptive neuro-fuzzy inference system (ANFIS) |
| title | Forecasting of Rainfall across River Basins Using Soft Computing Techniques: The Case Study of the Upper Brahmani Basin (India) |
| title_full | Forecasting of Rainfall across River Basins Using Soft Computing Techniques: The Case Study of the Upper Brahmani Basin (India) |
| title_fullStr | Forecasting of Rainfall across River Basins Using Soft Computing Techniques: The Case Study of the Upper Brahmani Basin (India) |
| title_full_unstemmed | Forecasting of Rainfall across River Basins Using Soft Computing Techniques: The Case Study of the Upper Brahmani Basin (India) |
| title_short | Forecasting of Rainfall across River Basins Using Soft Computing Techniques: The Case Study of the Upper Brahmani Basin (India) |
| title_sort | forecasting of rainfall across river basins using soft computing techniques the case study of the upper brahmani basin india |
| topic | rainfall forecasting upper Brahmani basin adaptive neuro-fuzzy inference system (ANFIS) |
| url | https://www.mdpi.com/2073-4441/15/3/499 |
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