جستجوی مقالات مرتبط با کلیدواژه « بابلسر » در نشریات گروه « آب و خاک »

In this study, the long-term rainfall in Babolsar city was simulated using an optimized hybrid artificial intelligence (AI) model over a 68 years period during 1951 to 2019. The ANFIS network and the wavelet transform combined to develop the hybrid model. Firstly, the effective lags of time series data were identified by using the autocorrelation function (ACF). Six ANFIS models were defined through these lags, Subsequently. The best ANFIS model was detected by conducting a sensitivity analysis. For testing the best ANFIS model, the value of correlation coefficient (R), variance accounted for (VAF), and scatter index (SI) were respectively computed to be 0.612, 37.029, and 0.761. In Addition, results of the models analysis showed that the (t-1), (t-2), (t-12), and (t-36) were identified as the most significant lags. Finally, the superior hybrid model was examined in three decomposition level (DL), revealing that the best results were obtained from the second decomposition level (DL2). In testing mode of the model, the R, VAF, and SI were calculated to be 0.972, 94.455, and 0.266, respectively. Therefore, the simulation results showed that the wavelet transform enhanced the performance of the ANFIS network significantly.

In this study, the long-term rainfall in Babolsar city was simulated by means of an optimized AI model. To do this, the extreme learning machine (ELM) and the wavelet transform (WT) were combined. It should be stated that the monthly rainfall values from 1951 to 2019 were applied, meaning that 70% of the observed values were employed to training the AI models and 30% of rest were utilized to testing these models. Firstly, the activation functions of the ELM models were evaluated; as a result, the sigmoid was chosen as the best activation function. Moreover, the lags of time series were introduced using the autocorrelation function (ACF) that four ELM models were defined through those identified lags. By performing a sensitivity analysis, the superior ELM model was introduced. The values of correlation coefficient (R), variance accounted for (VAF), and scatter index (SI) for the ELM model were respectively computed to be 0.524, 27.064, and 0.819. Furthermore, different mother wavelets were examined and the “dmey” was opted as the best mother wavelet. The wavelet transform enhanced the accuracy of the simulations significantly. For instance, the VAF index for the hybrid WELM model equaled to 86.461. It is noteworthy that the hybrid model was evaluated for different decomposition levels (DL) and then the best one was detected. Also, the (t-1) and (t-12) lags were identified as the most effective input lags.

Climatic events such as floods, storms and droughts are often caused by extreme weather. Therefore, it is important to study the trend of different ranges of climatic data rather than just average. Most trend detection studies are based on the analysis of changes in mean data and do not provide information on how changes occur in different ranges of the used data range. Therefore, to investigate the trend of changes in different ranges of the time series of climatic data, quantile regression method was proposed. The quantile regression has the ability to examine the changes trend in different quantiles of the data series. Therefore, in this study, we was analyzed the changes trend in the different quantiles of temperature minimum and maximum, precipitation and maximum daily wind speed data time series in Babolsar synoptic station for a period of 62 years (1959- 2020) seasonally and annually. The results showed that the minimum and maximum temperature and wind speed increased significantly in all seasons. The intensity of these increasing trend was higher in the extreme lower quantiles of the daily minimum temperature and extreme upper quantiles of daily maximum temperature in winter. But the wind speed data, in the upper quantiles especially extreme upper quantiles have increased to a much greater intensity than the lower quantiles, especially in autumn. The precipitation did not change significantly in the 62-year period. On an annual scale, the maximum increase for the minimum and maximum temperatures was in the extreme lower quantiles 4.28 and 2.85 degrees centigrade, respectively, and for the wind speed, it was in the extreme upper quantiles 8.24 meters per second.

Climate change is effective on the amount of precipitation in all areas and consequently it is effective on IDF curves. The aim of this study is the evaluation of effects of climate change on IDF curves in Babolsar station in future period since 2011 to 2030 using atmospheric general circulation model and under emission scenarios. In this respect، two appropriate statistical distributions، the Gumbel and Log - Pearson Type III، were fitted with hourly rainfall values between 1998-1966. Then IDF curves have extracted For intervals of 10، 20، 30 min and 1، 2، 6، 12 and 24 hours and return periods of 2، 5، 10، 25، 50 and 100 years. Next، data in daily time scale were estimated for intervals of 2013-2030 using atmospheric general circulation model of HadCm3 under emission scenarios A1B، A2، B1 and down scaling model of LARS-WG. The derivative curves of assumption scenarios have extracted by modified relationships of Bell. The results indicate that the values of correlation coefficient in Gumbel distribution were in the range of 0. 996-0. 998 and for Log - Pearson Type III، they were in the range of 0. 969-0. 974 that shows more significant correlation for Gumbel distribution. Comparison between rainfall predicted by the scenario mentioned and rainfall calculated by Gumbel distribution for data from 1966-1998 is showed an increase in precipitation intensity for BABOLSAR station in future periods (2 - 2011). For example، according to three scenarios A1B، A2، B1 the rainfall value for interval of one hour and return period of 2 year، respectively، 28%، 17% and 25% have increased than the values of basic rainfall.