Long-Term Weather Elements Prediction in Jordan using Adaptive Neuro-Fuzzy Inference System (ANFIS) with GIS Techniques
| Autor: | Omar Suleiman Arabeyyat |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Adaptive neuro fuzzy inference system General Computer Science Correlation coefficient Mean squared error Artificial neural network Computer science Climate change 02 engineering and technology Fuzzy control system Term (time) Hybrid intelligent system 020901 industrial engineering & automation Statistics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Precipitation Time series |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 9 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2018.090213 |
| Popis: | Weather elements are the most important parameters in metrological and hydrological studies especially in semi-arid regions, like Jordan. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used here to predict the minimum and maximum temperature of rainfall for the next 10 years using 30 years’ time series data for the period from 1985 to 2015. Several models were used based on different membership functions, different methods of optimization, and different dataset ratios for training and testing. By combining a neural network with a fuzzy system, the hybrid intelligent system results in a hybrid Neuro-Fuzzy system which is an approach that is good enough to simulate and predict rainfall events from long-term metrological data. In this study, the correlation coefficient and the mean square error were used to test the performance of the used model. ANFIS has successfully been used here to predict the minimum and maximum temperature of rainfall for the coming next 10 years and the results show a good consistence pattern compared to previous studies. The results showed a decrease in the annual average rainfall amounts in the next 10 years. The minimum average annual temperature showed the disappearance of a certain predicted zone by ANFIS when compared to actual data for the period 1985-2015, and the same results behavior has been noticed for the average annual maximum. |
| Databáze: | OpenAIRE |
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