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

In this paper, a study on the use of precipitation prediction techniques with time series data was presented. Time series are an effective tool for understanding the nature of hydrological phenomena that with sufficient knowledge of them, future changes can be modeled and predicted. Various statistical models have been considered with the aim of reducing error and increasing forecast accuracy. Due to its structural and flexibility, state space makes it possible to model each of the components of a variable, including surface, seasonal and random separately. Therefore, by identifying the system in the way of modeling the studied variable, it is possible to control and minimize the estimation error, more intelligently compared to classical models. In the present study, in order to evaluate the modeling capability of state space and compare it with classical models, monthly preciptation modeling was performed in three rain gauge stations in Dez catchment, with four structural models of state space including Kalman filter, ETS exponential smoothing model and Modified exponential smoothing models were BATS and TBATS and the classic model was ARIMA. The results showed that at Sepiddasht Sezar station based on RMSE and MAE criteria of TBATS model and in Tangpanj Bakhtiyari station based on RMSE and MAE criterion of Kalman filter model and in Telezang station according to RMSE and MAE criterion of TBATS model the best models were chosen.

Measuring complexity and ways to reduce it in organizations and decision-making processes has become one of the topics of the day. The chaos theory was proposed in the 1960s, and the most effective and most successful effort was made by Edward Lorenz. Towards this, the flow rate of Sefidrood River as the most important river in Guilan Province and the second-longest river in Iran was studied using Chaos theory.

The study area in this research is a sub-basin of the Sefidrood River Basin. After collecting the monthly and annual discharge data of Sefidrood River, the following items were investigated:1- chaotic dynamic systems, 2- phase space reconstruction, 3- determining the time delay, 4- determining the embedding dimension, 5- determining the correlation dimension, and 6- determining the Lyapunov and Hurst exponents.

In determining the delay time using the autocorrelation function (ACF( curve, the appropriate lag is where the graph reaches a value close to zero or about 0.1 to 0.2. An appropriate embedding dimension is an embedded dimension in which the number of false neighbors has reached to about zero. For a lag of 1-month, the delay vectors are concentrated around the diagonal axis of space. Therefore, X(t) and X(t +1) are very close and continuous. Therefore, they will cause the characteristics of the adsorbent structure to be lost. Also in the state (phase) space for the delay time of 100 months, the density of lag vectors is close to the horizontal and vertical axes of the graph and indicates the incoherence and complexity of successive components in the lag vectors and its inadequacy to achieve system dynamics. However, due to the 5 months delay state space obtained from the average actual information (AMI) method, the delay vectors have a better distribution and the state space is well filled with points. The correlation dimension of the monthly time series is 3.37.

The presence of stochastic behavior in the river flow was determined using the correlation dimension test and Hurst exponent. The correlation exponent was saturated after increasing the embedded dimension in an incorrect value equal to 3.37. In addition, the closest correct value to the correlation dimension indicates the minimum variables required to describe the system, which is a value of 4. The obtained Hurst exponent is opposite to 0.5 and according to Hurst studies, it indicates the non-randomness and the presence of chaos in the river. The Hurst exponent obtained in daily scales is between 0.5 and 1 and indicates the existence of long-term memory in this series.