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Probable Maximum Precipitation (PMP) Prediction Using Rule-Based Fuzzy Inference System: A Comparison with Classic Methods | ||
| Journal of Hydrosciences and Environment | ||
| دوره 5، شماره 9، شهریور 2021، صفحه 1-7 اصل مقاله (405.14 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/jhe.2022.41522.1073 | ||
| نویسندگان | ||
| Mehdi Azhdary Moghaddam* ؛ Soroosh Sanayee؛ Mohsen Rashki | ||
| University of Sistan and Baluchestan | ||
| چکیده | ||
| Precipitation is predicted for different days of the year using fuzzy logic, the Mamdani fuzzy system, and IF-THEN rules. The input variables include five parameters of relative humidity, cloud cover, wind direction, temperature, and surface pressure, each with three membership functions ranging from 0 to 1. The final answer will likely be the amount of rainfall. All input variables are fuzzy, and two types of membership functions are selected. As many as 51 rules are considered for each station. Finally, the best situation of precipitation is chosen, and PMP obtained is applied to Kahir catchment basin, Sistan and Baluchistan. The fuzzy PMP is then calculated and compared with the Hershfield classic method for calculating PMP. Results show that fuzzy PMP estimation is more accurate and reliable for the studied area than the Hershfield method. All implementations are performed with MATLAB. | ||
| کلیدواژه‌ها | ||
| Fuzzy logic؛ Mamdani fuzzy inference system؛ Probable Maximum Precipitation (PMP)؛ Hershfield classic method | ||
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آمار تعداد مشاهده مقاله: 129 تعداد دریافت فایل اصل مقاله: 62 |
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