Assessing Intelligent Models in Forecasting Monthly Rainfall by Means of Teleconnection Patterns (Case Study: Khorasan Razavi Province)

آب و خاک

Article 1, Volume 29, Issue 2, May 2015, Pages 274-283   

Document Type: Research Article

DOI: 10.22067/jsw.v0i0.25357

Authors

Farzaneh Nazarieh^* ; H. Ansari

Ferdowsi University of Mashhad

Abstract

Introduction: Rainfall is affected by changes in the global sea level change, especially changes in sea surface temperature SST Sea Surface Temperature and sea level pressure SLP Sea level Pressure. Climate anomalies being related to each other at large distance is called teleconnection. As physical relationships between rainfall and teleconnection patterns are not defined clearly, we used intelligent models for forecasting rainfall. The intelligent models used in this study included Fuzzy Inference Systems, neural network and Neuro-fuzzy. In this study, first the teleconnection indices that could affect rainfall in the study area were identified. Then intelligent models were trained for rainfall forecasting. Finally, the most capable model for forecasting rainfall was presented. The study area for this research is the Khorasan Razavi Province. In order to present a model for rainfall forecasting, rainfall data of seven synoptic stations including Mashhad, Golmakan, Nishapur, Sabzevar, Kashmar, Torbate and Sharks since 1991 to 2010 were used.
Materials and Methods: Based on previous studies about Teleconnection Patterns in the study area, effective Teleconnection indexes were identified. After calculating the correlation between the identified teleconnection indices and rainfall in one, two and three months ahead for all stations, fourteen teleconnection indices were chosen as inputs for intelligent models. These indices include, SLP Adriatic , SLP northern Red Sea, SLP Mediterranean Sea, SLP Aral sea, SST Sea surface temperature Labrador sea, SST Oman Sea, SST Caspian Sea, SST Persian Gulf, North Pacific pattern, SST Tropical Pacific in NINO12 and NINO3 regions, North Pacific Oscillation, Trans-Nino Index, Multivariable Enso Index. Inputs of the intelligent models include fourteen teleconnection indices, latitude and altitude of each station and their outputs are the prediction of rainfall for one, two and three months ahead. For calibration of the models, eighty percent of the data belonged to six stations. Mashhad, Golmakan, Sabzevar, Kashmar, Torbate and Sarakhs were used. Verification of the model was carried out in two parts. The first part of verification was done with twenty percent of the remaining data which belonged to the mentioned six stations. The second part of verification was done with data from the Nishapur station. Nishapur geographically is located between other stations and did not participate in the calibration. So, it provides a ondition for assessing models in location except for the calibration stations. To assess and compare the accuracy of the models, the following statistical criteria have been used: correlation coefficient (R), normal root mean square error (NRMSE), mean bias error (MBE), Jacovides criteria (t), and ratio (R2/t). To evaluate models in different rainfall depths, rainfall data based on standard precipitation index (SPI) was divided into seven classes, and the accuracy of each class was calculated separately.
Results and Discussion: By comparing the models' ability to predict rainfall according to the R2 /t criteria it can be concluded that the ranking of the models is Neuro-fuzzy model, Fuzzy Inference Systems, and Neural network, respectively. R2 /to criteria for prediction of rainfall one, two, and three month earlier in the Neuro-fuzzy model are 0.91, 0.4, 0.36, in Fuzzy Inference Systems are 0.76, 0.38, 0.31 and in the neural network model are 0.43,0.27, 0.2. The statistical criteria of Neuro-fuzzy model (R, MBE, NRMSE, t, R2/ t) for rainfall forecasting one month earlier are 0.8, -0.55,0.43, 0.7 , 0.91; two months earlier are 0.79, -1.32, 0.48, 1.56, 0.4; and three months earlier are 0.73,-1.37, 0.54, 1.47, 0.36 . Calculation of MBE criteria for Neuro-fuzzy models in all classes of SPI indicated that this model has a lower estimate in extremely wet and very wet classes. This is because of lack of data belonging to these classes for model training.
Conclusion: The results of this research showed that teleconnection indices are suitable inputs for intelligent models for rainfall prediction. Computing the best structure of fuzzy, neural network and Neuro-fuzzy models showed that Neuro-fuzzy can predict rainfall the most accurately. But, the results of these models in very wet and extremely wet condition are not reliable .So, these models should be used with more caution in these conditions.

Keywords

Fuzzy Inference Systems; Neural Network; Neuro-fuzzy; Rainfall forecasting; Teleconnection patterns

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