ASSESSING INTELLIGENT MODELS IN FORECASTING MONTHLY RAINFALL BY MEANS OF TELECONNECTION PATTERNS (CASE STUDY: KHORASAN RAZAVI PROVINCE)

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NAZARIEH F.; ANSARI H.

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Journal: Journal of Water and Soil Year:2015 | Volume:29 | Issue:2 Page(s): 274-283

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Title

ASSESSING INTELLIGENT MODELS IN FORECASTING MONTHLY RAINFALL BY MEANS OF TELECONNECTION PATTERNS (CASE STUDY: KHORASAN RAZAVI PROVINCE)

Author(s)

NAZARIEH F. | ANSARI H. | Issue Writer Certificate

Keywords

FUZZY INFERENCE SYSTEMSQ2

NEURAL NETWORKQ2

NEURO-FUZZYQ2

RAINFALL FORECASTINGQ2

TELECONNECTION PATTERNSQ2

Abstract

Introduction: Rainfall is affected by changes in the global sea level change, especially changes in seasurface temperature SST (Sea Surface Temperature) and sea level pressure SLP (Sea level Pressure). Climateanomalies being related to each other at large distance is called teleconnection. As physical relationshipsbetween rainfall and TELECONNECTION PATTERNS are not defined clearly, we used intelligent models for forecastingrainfall. The intelligent models used in this study included FUZZY INFERENCE SYSTEMS, NEURAL NETWORK and Neurofuzzy. 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 forecastingrainfall was presented. The study area for this research is the Khorasan Razavi Province. In order to present amodel 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 identifiedteleconnection indices and rainfall in one, two and three months ahead for all stations, fourteen teleconnectionindices were chosen as inputs for intelligent models. These indices include, SLP Adriatic, SLP northern RedSea, SLP Mediterranean Sea, SLP Aral sea, SST (Sea surface temperature) Labrador sea, SST Oman Sea, SSTCaspian 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 includefourteen teleconnection indices, latitude and altitude of each station and their outputs are the prediction ofrainfall for one, two and three months ahead. For calibration of the models, eighty percent of the data belongedto six stations. Mashhad, Golmakan, Sabzevar, Kashmar, Torbate and Sarakhs were used. Verification of themodel was carried out in two parts. The first part of verification was done with twenty percent of the remainingdata which belonged to the mentioned six stations. The second part of verification was done with data from theNishapur station. Nishapur geographically is located between other stations and did not participate in thecalibration. So, it provides a ondition for assessing models in location except for the calibration stations. Toassess and compare the accuracy of the models, the following statistical criteria have been used: correlationcoefficient (R), normal root mean square error (NRMSE), mean bias error (MBE), Jacovides criteria (t), andratio (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 itcan be concluded that the ranking of the models is NEURO-FUZZY model, FUZZY INFERENCE SYSTEMS, and Neuralnetwork, respectively. R2 /to criteria for prediction of rainfall one, two, and three month earlier in the Neurofuzzy model are 0.91, 0.4, 0.36, in FUZZY INFERENCE SYSTEMS are 0.76, 0.38, 0.31 and in the NEURAL NETWORK modelare 0.43, 0.27, 0.2. The statistical criteria of NEURO-FUZZY model (R, MBE, NRMSE, t, R2/ t) for rainfallforecasting 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 inall classes of SPI indicated that this model has a lower estimate in extremely wet and very wet classes. This isbecause 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 intelligentmodels for rainfall prediction. Computing the best structure of fuzzy, NEURAL NETWORK and NEURO-FUZZY modelsshowed that NEURO-FUZZY can predict rainfall the most accurately. But, the results of these models in very wetand extremely wet condition are not reliable.So, these models should be used with more caution in theseconditions.

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APA: Copy

NAZARIEH, F., & ANSARI, H.. (2015). ASSESSING INTELLIGENT MODELS IN FORECASTING MONTHLY RAINFALL BY MEANS OF TELECONNECTION PATTERNS (CASE STUDY: KHORASAN RAZAVI PROVINCE). JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), 29(2), 274-283. SID. https://sid.ir/paper/141063/en

Vancouver: Copy

NAZARIEH F., ANSARI H.. ASSESSING INTELLIGENT MODELS IN FORECASTING MONTHLY RAINFALL BY MEANS OF TELECONNECTION PATTERNS (CASE STUDY: KHORASAN RAZAVI PROVINCE). JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY)[Internet]. 2015;29(2):274-283. Available from: https://sid.ir/paper/141063/en

IEEE: Copy

F. NAZARIEH, and H. ANSARI, “ASSESSING INTELLIGENT MODELS IN FORECASTING MONTHLY RAINFALL BY MEANS OF TELECONNECTION PATTERNS (CASE STUDY: KHORASAN RAZAVI PROVINCE),” JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), vol. 29, no. 2, pp. 274–283, 2015, [Online]. Available: https://sid.ir/paper/141063/en

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