Rostami, Mahnaz (2014) Drought Prediction using Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System Techniques in Mond Basin of Fars Province. Masters thesis, University of Zabol.
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Abstract
Substantially increasing demand for water consumption driven by population growth On the one hand and on the other hand limited water resources makes water scarcity a crucial problem in Iran. Therefore, Drought Prediction is essential for the efficient use of water management, irrigation systems and management of dam utilization. In recent years, use of Artificial intelligence methods for modeling of Hydrological phenomenon’s that is including complexity and uncertainly, is considered scholars. In this research, performances of Artificial Neural Networks (ANN) and Adaptive Neuro- Fuzzy Inference System (ANFIS) for Drought Prediction Techniques in Mond Basin of Fars Province have been comparatively evaluated on the basis of the monthly data for a 32-year period (1978-2012) including rainfall, temperature and drought indices SPI and ¬ PN. The best combination of the model inputs was selected based on rainfall and temperature at the current month. In addition, the training data length of %70 and the testing data length of %30 were determined. After conducting prediction by using ANN and ANFIS models, the performances of these models were evaluated on the basis of statistical criteria of Nash index (E), correlation coefficient (R) and Root Mean Square Error (RMSE). The obtained results indicated higher accuracy of ANN model rather than ANFIS model in order to Drought Prediction Techniques in Mond Basin of Fars Province.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Drought Index- Artificial Neural Network - Neuro-Fuzzy Inference System - Mond Basin |
| Subjects: | G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography G Geography. Anthropology. Recreation > GE Environmental Sciences |
| Depositing User: | admin admin1 admin2 |
| Date Deposited: | 19 Apr 2016 04:42 |
| Last Modified: | 19 Apr 2016 04:42 |
| URI: | http://eprints.uoz.ac.ir/id/eprint/369 |
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