The movements in oil prices are complex and, therefore, seem to be unpredictable. The traditional linear structural models have not been promising when applied to forecasting, particularly in the case of complex series such as oil prices. Although linear and nonlinear time series models have done much better job in forecasting oil prices, there is yet room for an improvement. If the data generating process is nonlinear, applying linear models could result in misleading forecasts. Model specification in nonlinear modeling can also be very case dependent and time-consuming. In this paper, we model and forecast daily futures oil price, listed in NYMEX, applying ARIMA, and GARCH models, for the period April June 1983 – Jan. 2003. Then, we test for chaos using BDS, Lyapunov exponent, Neural Networks, and Embedding Dimension methods. Finally, we will set up a nonlinear and flexible ANN model to forecast the series. Since the tests for chaos indicate that the oil price in futures markets is chaotic, the ANN model should make better forecasts. The forecasts comparison among the models approves that.

Rivers flow forecasting has special importance in surface water management، especially in agricultural planning and risk reduction of floods and droughts. In recent years، studies have shown the superiority of time-series forecasting models based on artificial intelligence، such as artificial neural networks (ANN) and genetic programming (GP). In this paper، continuous and discrete historical flow records are used for Saeed-Abad monthly river flow forecasting in East Azarbaijan province، Iran. Auto regressive moving average with exogenous inputs (ARMAX)، ANN، and GP models are used and compared to each other. Coefficient of determination (R2) and root mean square error (RMSE) are used to evaluate the performance of the aforementioned models. Results show that for the two methods، the GP model is more effective than ARMAX and ANN in terms of accuracy. For continuous time-series forecasting، GP is a more precise model (R2 = 0. 7 and RMSE = 0. 172) than either ANN (R2 = 0. 627 and RMSE = 0. 193) or ARMAX (R2 = 0. 595 and RMSE = 0. 243). For discrete time-series forecasting، the superiority of the GP model is evident in almost all months. Moreover، results indicate that discrete monthly time-series forecasting method is superior to the continuous monthly time-series forecasting method.

Short-term traffic flow forecasting plays a significant role in the Intelligent Transportation Systems (ITS), especially for the traffic signal control and the transportation planning research. Two mainly problems restrict the forecasting of urban freeway traffic parameters. One is the freeway traffic changes non-regularly under the heterogeneous traffic conditions, and the other is the successful predictability decreases sharply in multiple-steps-ahead prediction. In this paper, we present a novel pattern-based short-term traffic forecasting approach based on the integration of multi-phase traffic flow theory and time series analysis methods. For the purpose of prediction, the historical traffic data are classified by the dynamic flow-density relation into three traffic patterns (free flow, synchronized and congested pattern), and then different predict models are built respectively according to the classified traffic patterns. With the current traffic data, the future traffic state can be online predicted by means of pattern matching to identify traffic patterns. Finally, a comparative study in a section of the Third-Loop Freeway, LIULIQIAO, Beijing city, shows that the proposed approach represents more accurately the anticipated traffic flow when compared to the classical time series models that without integration with the traffic flow theory.

Forecasting currency exchange rates is an important financial problem that has received a great deal of attention especially because of its intrinsic difficulty and practical applications. The methods used for time series analyses are conventionally based on the concepts of stationarity and linearity. However, for cases in which the system dynamics are highly nonlinear, the performance of traditional models is very poor. On the other hand, artificial neural networks and wavelet

The exchange of carbon and water in the ecosystem is influenced not only by weather and climatic perturbations but also by vegetation dynamics. The relationship between carbon and water exchange and environment in agro-ecosystem across different temporal scales is not very often been quantified. Spectral analysis of eddy covariance measurements can identify the interactions between environmental and biological factors at multi-temporal scales. Here, we used a new method, ensemble empirical mode decomposition (EEMD), to study the temporal covariance between ecosystem exchange of carbon dioxide (NEE), latent heat flux (LE) and environmental factors in a winter wheat cropping system located at the North China Plain. The results showed that the NEE, LE and environmental factors can be decomposed into 12 significant quasi-period oscillations on various time-scales i.e. hourly, diurnal, weekly and seasonal timescales. Variance of NEE in diurnal, hourly, seasonal, weekly scale was 58.9, 29.6, 4.7, 0.6%, respectively. Variance of LE in diurnal, hourly, seasonal, weekly scale was 55.2, 15.5, 5.1, 1.8%, respectively. The largest of variance contribution is at diurnal time-scale from net radiation (Rn), wind speed (μ) and vapor pressure deficit (VPD) due to daily rhythms in solar radiation. The soil water content varied significantly at a relatively longer time-scale i.e. weekly and seasonal scale. Large variance contribution of ambient temperature (T) (63.4%) and VPD (33.6%) is in trend term due to the significant increasing seasonal trend from winter to summer. The correlation analysis indicated that NEE and LE was correlated highly with net radiation (Rn) at all time-scale, as well as with VPD, ambient temperature (T), and wind speed (μ) in diurnal scale and with soil water in seasonal time-scales. This implied that solar radiation contributed the main variation of carbon and water in short time-scale, i.e. hourly and diurnal. Soil water variation strongly correlated with the seasonal variation of NEE and LE. Furthermore, seasonal signals of NEE and LE synchronized with LAI, which indicated that carbon dioxide and water flux are also regulated by LAI in seasonal time-scale. The quantification of the variation explained by carbon and water fluxes and environmental factors across different temporal scales using EEMD improved the understanding of carbon and water process in a cropping system.

Flood is inherently an uncertain phenomenon and the certainty and credibility of flood forecasting and warning systems will cause errors regardless of the sources of uncertainty. Extreme rainfall events are one of the most important input data to rainfall-runoff models, which always have uncertainty. Considering this issue the uncertainty of the design flood hydrograph can be investigated for different return periods. In this research first to simulate the flood hydrograph the HEC-HMS model was calibrated and validated based on the hourly flood hydrographs recorded at the basin outlet. Historical data were collected on the 24-hour maximum rainfall of Gharesoo Basin stations with 30-year statistics and the affected basins were identified. Then in each station 30 series of 30 years of artificial data with a maximum 24-hour rainfall were produced. For each of these produced stochastic series the best statistical distribution was fitted and in each series extreme values with a return period of 25 50 100 and 1000 years were calculated. Finally in each return period by combining 30 different amounts of rainfall obtained from stochastic series, the uncertainty bandwidth of the flood hydrograph was obtained during this return period. The results indicated that the highest predicted peak discharge for different return periods was between 1.2 and 1.7 times the historically recorded discharge during that return period. Generally the maximum discharge of different return periods was between 1.5 and 3 times the minimum discharge

In this paper, effects of climate changes on the Caspian Sea water and air temperature were investigated. Three stations were selected in Caspian sub-basins for each parameter. Monthly measured data were selected from early 1961 to end 2017. The data downloaded from “Caspian Environment Programme (CEP)” site. For studying climate changes effects on water and air temperature, time series of data were plotted. Trend of water temperature changes were studied using linear regression method. We also, for studying effects of climate changes seasonally and at different months, short and seasonal signals effects were removed from raw data. Results show that climate changes effects were more significant in the middle Caspian sub-basin. Trends of water temperature in southern, middle, and northern Caspian were 0.0036, 0.0216, 0.0156℃⁄year, respectively. Water and air temperature are being in tune completely in northern and middle sub-basins, while for southern basin are not, especially for warm months in the year.

In general, one of the channels that help accelerate the economic growth of countries is the growth of their industry sector. The importance and basic role of the industry sector and its contribution as the most important factor in stimulating economic growth in developed and developing countries is to such an extent that many experts believe that industrial development leads to the growth and development of other sectors. It leads to economy. The present research has been conducted on the asymmetric analysis of monetary shocks on the economic growth rate of the industrial sector in Iran with the SUR model. In this research, using seasonal time series data during the period 1365 to 1398 and using the nonlinear approach of Markov regime change, apparently unrelated regressions (SUR) and linear regression method, the effect of the mentioned shocks on growth Industrial production is reviewed. The results of the SUR technique showed that the reaction of the industries and mines sector and its sub-sectors to predicted and unanticipated monetary shocks is meaningless in normal economic conditions. Therefore, regardless of the fluctuations governing the economy, the transmission channels of monetary policy are weak in the entire sector of industries and mines and its sub-sectors.

In the present paper, ANN is used to predict the tidal level fluctuations, which is an important parameter in maritime areas. A time lagged recurrent network (TLRN) was used to train the ANN model. In this kind of networks, the problem is representation of the information in time instead of the information among the input patterns, as in the regular ANN models. Two sets of data were used to test the proposed model. San Francisco Bay tidal levels were used to test the performance of the model as a predictive tool. The second set of data was collected in Gouatr Bay in southeast of Iran. This data set was used to show the ability of the ANN model in predicting and completing of data in a station, which has a short period of records. Different model structures were used and compared with each other. In addition, an ARMA model was used to simulate time series data to compare the results with the ANN forecasts. Results proved that ANN can be used effectively in this field and satisfactory accuracy was found for the two examples. Based on this study, an operational real time environment could be achieved when using a trained forecasting neural network.

 Wetlands are habitats for vegetation and wildlife and because of this, they have a high environmental value. Also, wetlands reduce soil erosion, restore aquifers, store rainwater in a flood event, and provide water for agriculture or livestock. Wetlands are vulnerable to human interventions and changes such as drainage, urban sprawl, infrastructure development, and over-exploitation of groundwater resources. Prediction of the condition of wetlands in the future requires a correct understanding of the evolution of wetlands and identifying their trend of change. Nowadays, Remote Sensing technology is widely used for mapping wetlands, and its ability to monitor the changes in wetlands regardless of the diversity of wetlands has significantly increased the value of this science in this field. Remote Sensing can be an effective means of simulating and predicting wetland degradation processes by providing images at different times and through dynamic spatial modeling. In this study, the changes in the Bakhtegan wetland have been monitored. This wetland has high environmental and tourism importance and its drying affects negatively the living conditions and health of local people as well as tourism in the region. In addition, predictions of precipitation parameters, groundwater level, and temperature have been conducted. For this purpose, the Google Earth Engine platform was used to capture and process images. Google Earth Engine is a platform that can capture and process images in the shortest time and at high speed. In this regard, using Google Earth Engine, changes in the lake water area along with changes in temperature, groundwater level, and precipitation were extracted and monitored. Moreover, a comparison took place between these parameters to determine the changes that have taken place in the lake over the past two decades. To predict the parameters, the changing pattern was predicted and analyzed using the Prophet model. The most important advantage of the Prophet model is its ability to convert discrete data to continuous data to make the best predictions. This method automatically detects the trend of seasonal data and displays the trend of seasonal changes.

 Satellite images were acquired from the Google Earth Engine platform to monitor the wetland. Landsat 7 and 8 images were used for water body extraction, GRACE Data were used for extraction of groundwater level changes, MODIS product was used for extraction of vegetation and wetland surface temperature, and TRMM image product was used to extract precipitation values. An automated water extraction index was used to extract the wetland body water. The groundwater level was extracted from the GRACE sensor. MODIS sensor product was used to obtain the surface temperature time series for the study area. For the extraction of precipitation time series, the monthly cumulative data of the TRMM (3B43V7) satellite with a spatial resolution of 0.25°C was extracted using Google Earth Engine and the trend of changes was evaluated and analyzed. The Mann-Kendall test is one of the most widely used non-parametric tests for detecting meteorological and environmental data trends, which is used to detect a monotonic trend line since this test is a non-parametric method, it does not need that the data follow a normal distribution. The Prophet predictive model is a predictive library developed by Facebook and is available in R and Python programming languages. This library supports additive modeling methods and can properly predict discrete values continuously. This feature is called "Holiday". Another feature of this library is the automatic detection of daily, weekly, seasonal and annual trends. The mean absolute error (MAE), by default, exists in the Prophet library. This error represents a more natural standard than the mean error and unlike the RMSE error, it is unambiguous.

 In the present study, we monitored the Bakhtegan wetland using the Google Earth Engine platform to observe the trend of water level changes in this wetland from 2000 to 2020. In addition, Parameters were also predicted using the Prophet Prediction method which is developed and published by Facebook. By examining this trend, it can be observed that the water level of the wetland has been significantly reduced during two decades. In this regard, the trend of groundwater level, temperature, and precipitation in the area was investigated. Examining these factors, it was found that along with a 58.3% decrease in the water level of the wetland, there was a 260% decrease in the groundwater level of the region, although the amount of rainfall in the region has been less compared to other factors and has been decreased about 29%. Using Mann-Kendall statistical test, the trend of this decline was proved. To predict the parameters, the Prophet model has been able to make predictions for 1500 days as continuous data using discrete data. The output of the model has shown that for rainfall parameters and groundwater level a downward trend is predictable over the next 1500 days which is low intensity for precipitation but with high intensity for groundwater level. Temperature prediction indicated that it has a seasonal trend, and has a high amount of fluctuation within a year, but its annual trend indicates stability in the coming years. The results of the model for the water level of the wetland also show a relatively low upward trend that has a probability of change of ±12.5 Square kilometers. Also, the error of the parameters at the 95% significant level has acceptable accuracy, which indicates the validity of the prediction. An automated water extraction index was used in this study to extract the time series of the water body of the wetland. Using the mean time series extracted, the maximum and minimum wetland’s water body area belongs to 2006 with 629.23 square kilometers and 2014 with 156.82 square kilometers, respectively. The time series of changes in this wetland indicates that the water volume of the wetland has been declining in the last two decades. According to this study, it can be concluded that the trend of changes in the water level of the wetland has been decreasing. The descending changes in the lake based on the trend of changes in groundwater levels indicates a decrease in water volume in the area. Considering that the trend of precipitation changes has been stable, it can have assumed that improper management and excessive use of groundwater may be a reason for lowering the water level of the wetland. Due to the same decrease in the water level of the lake, the temperature has also decreased by about 3°C.

 According to this study, it can be concluded that groundwater levels and precipitation will have a downward trend in the future, which will lead to a decrease in the water level of the wetland, which itself has the potential to fluctuate in the future, and the downward trend continues. With the current trend, the only solution is to plan properly to preserve the wetland. If this trend continues, we will face the destruction of the wetland. Given the monthly trend of the wetland surface, it is suggested not to over-exploit groundwater resources, especially in the summer. For further research, the Google Earth Engine platform can be used without the need to download the images and spend a lot of time and money, to obtain the time series of images. Regarding the prediction, in future studies, the Prophet model can be applied, since it uses discrete data and at the same time provides the desired accuracy.