首页|Models Prediction and Estimation of ENSO and Karachi Rainfall Cycles Through AR-GARCH and GARCH Process
Models Prediction and Estimation of ENSO and Karachi Rainfall Cycles Through AR-GARCH and GARCH Process
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Karachi rainfall and ENSO cycles have influenced on earth climates. The effects of El-Nino Southern Oscillation (ENSO) on the rainfall climate system of the Karachi region are analyzed. The research is working on the ENSO and rainfall Karachi region massive datasets information gathered for the period 1961-2021, which are break down into cycles (1st-10th). The novelty of this study is to analyze the factor of the ENSO effect, which is parallel to the Karachi rainfall region, as well as other factors such as deforestation. ENSO-Rainfall Karachi region cycles are also measured via statistical techniques. The estimate Model and forecasting of volatility through the comparative study of AR(R) - GARCH (P, Q) and GARCH (P, Q) Models. Effect of El Nino-Southern Oscillation (ENSO) on Karachi Rainfall as a case study suitable to research on its behavior for estimating forecast evolution of ENSO-Rainfall Karachi region cycles. The technique of AR(R) - GARCH (P, Q) and GARCH (P, Q) process is feasible for ensuring the appropriateness of the impacts on Karachi region rainfall and ENSO cycle. Different value of AR(R) - GARCH (P, Q) and GARCH (P, Q) Models is used. RMSE, MAE, MAPE and U Test are calculated to describe which technique provide the maximum accuracy of forecasting and Predictions. Most of the forecasting evaluation describe that GARCH (P, Q) has highest accuracy to predicting and forecasting ENSO and Karachi Rainfall Cycles as comparative others three models. This study confirms that, the duration of ENSO years, the tendency of Karachi region rainfall is reduced. The study shows that the relationship between ENSO-Rainfall Karachi region cycles are uncertainty. In the immediate year following the El Nino event, the July and September months, as well as the summer monsoon season, had a statistically significant 90% rainfall deficit.
NETL
NSTL
