TY - JOUR ID - 76549 TI - Performance Comparison of Statistical, Fuzzy and Perceptron Neural Network Models in Forecasting Dust Storms in Critical Regions in Iran JO - Iranian Journal of Soil and Water Research JA - IJSWR LA - en SN - 2008-479X AU - Ansari ghojghar, Mohammad AU - Pourgholam-Amiji, Masoud AU - Bazrafshan, Javad AU - Liaghat, Abdolmajid AU - Araghinejad, Shahab AD - Phd Candidate of Water Resources Engineering, Department of Irrigation and Reclamation Engineering, University of Tehran, Karaj, Iran. AD - Department of Irrigation and Reclamation Engineering, Faculty of Agriculture Engineering & Technology, Campus of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. AD - Department of Irrigation & Reclamation Engineering, Campus of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. Y1 - 2020 PY - 2020 VL - 51 IS - 8 SP - 2051 EP - 2063 KW - forecasting KW - Radial basis functions KW - Dust KW - Adaptive Neural-Fuzzy Inference System KW - Artificial Intelligence DO - 10.22059/ijswr.2020.302529.668607 N2 - Different regions have different potentials in dust release, and the increase in dust storms indicates the dominance of the desert ecosystem in each region. Prediction of the occurrence of dust storms in critical regions allow desion-makers to efficiently manage and to mitigate its probable damages to landscape. This study aims to predict the frequency of dust storm days (FDSD) over two critical regions (west and southeast) in Iran on a seasonal  scale. To this end, the hourly dust data and World Meteorological Organization codes were gathered in six synoptic stations of Zabol and Zahedan (southeast Iran), Abadan, Ahvaz, Bostan, and Masjed Soleiman (west Iran) covering the statistical period of 25 years (1990-2014). After calculating the frequency of dust storm days, using four artificial intelligence methods including multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), radial basis function (RBF), and general regression neural network (GRNN), the frequency of dust storm days for the next season were predicted. The results showed an increase in the accuracy of the predictions with increasing the number of dust storm days in such a way that based on the results obtained from the MLP model, the correlation coefficient between the observed and predicted values ​​of the frequency of dust storm days for Masjed Soleiman and Zabol stations were 0.8 and 0.97, respectively; explaining that Zabol have the highest frequency among these stations. Also, according to the results of ANFIS, RBF, and GRNN models, the correlation coefficient calculated for prediction in Masjed Soleiman and Zabol stations varied from 0.41 to 0.95, 0.35 to 0.92 and 0.22 to 0.98, respectively. Overall, by comparing the results of the proposed models, ANFIS had the best performance which was followed by GRNN. The results of this study can be useful in managing the issues caused by dust storms and in the combating plans to desertification in the study regions. UR - https://ijswr.ut.ac.ir/article_76549.html L1 - https://ijswr.ut.ac.ir/article_76549_a5772b5194c0eccf3708bb6cf2e6af22.pdf ER -