Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology
| Autor: | Shahaboddin Shamshirband, Chandrabhushan Roy, Shervin Motamedi, Dalibor Petković, Roslan Hashim, Milan Gocic, Siew Cheng Lee |
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| Rok vydání: | 2016 |
| Předmět: |
Atmospheric Science
Adaptive neuro fuzzy inference system 010504 meteorology & atmospheric sciences Meteorology Artificial neural network Neuro-fuzzy Cloud cover Feature selection Genetic programming 02 engineering and technology 01 natural sciences Support vector machine 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0105 earth and related environmental sciences Extreme learning machine Mathematics |
| Zdroj: | Atmospheric Research. 171:21-30 |
| ISSN: | 0169-8095 |
| DOI: | 10.1016/j.atmosres.2015.12.002 |
| Popis: | Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure ( e a ), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions. |
| Databáze: | OpenAIRE |
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