Temporal Analysis Of Temperature Variability In Pakistan Using The Method Of Empirical Mode Decomposition (original) (raw)

Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series

Water Resources Management, 2014

ABSTRACT In this study, a nonparametric technique to set up a river stage forecasting model based on empirical mode decomposition (EMD) is presented. The approach is based on the use of the EMD and artificial neural networks (ANN) to forecast next month's monthly streamflows. The proposed approach is applied to a real case study. The data from station on the Kizilirmak River in Turkey was used. The mean square errors (MSE), mean absolute errors (MAE) and correlation coefficient (R) statistics were used for evaluating the accuracy of the EMD-ANN model. The accuracy of the EMD-ANN model was then compared to the artificial neural networks (ANN) model. The results showed that EMD-ANN approach performed better than the ANN in predicting stream flows. The most accurate EMD-ANN model had MSE=0.0132, MAE=0.0883 and R=0.8012 statistics, respectively.

Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule-Walker equation based approach

Meteorological Applications, 2011

The present paper has adopted an autoregressive approach to inspect the time series of monthly maximum temperature (T max ) over northeast India. Through autocorrelation analysis the T max time series of northeast India is identified as non-stationary, with a seasonality of 12 months, and it is also found to show an increasing trend by using both parametric and non-parametric methods. The autoregressive models of the reduced T max time series, which has become stationary on removal of the seasonal and the trend components from the original time series, were generated through Yule-Walker equations. The sixth order autoregressive model (AR ) is identified as a suitable representative of the T max time series based on the Akaike information criteria, and the prediction potential of AR(6) is also established statistically through Willmott's indices. Subsequently, autoregressive neural network models were generated as a multilayer perceptron, a generalized feed forward neural network and a modular neural network. An autoregressive neural network model of order four (AR-NN(4)), in the form of a modular neural network (MNN), has performed comparably well with that of AR(6) based on the high values of Willmott's indices and the low values of the prediction error. Therefore, AR-NN(4)-MNN will be a better option than AR(6) to forecast a time series, i.e. the monthly T max time series of northeast India, because AR-NN(4)-MNN requires fewer predictors for a superior forecast of a time series.

Modelling the Monthly and Annual Temperature Series of Quetta, Pakistan

The monthly average temperature series of Quetta – Pakistan from 1950 – 2000 is examined. A straight line is fitted to the data and seasonal variation and trend in temperature for each month of the year were obtained. An overall model is constructed as large variations in the monthly slopes were observed. In order to describe the seasonal pattern and trend in temperature, corresponding to the different months, both sine/cosine waves and sine/cosine waves multiplied by the time were included in the model as independent variables. The lag-1 autocorrelation was found in the residual of the model and hence another model was fitted to the pre-whiten series that shows a good fit (R2=0.95) and is free from correlated residuals. Both parametric and non-parametric tests applied to each month temperature show significant trend in all months except February and March.

Predicting Climatic Meteorological Parameters by Using the Artificial Dynamics Neural Networks: Case Study, Bushehr City

Journal of Computer Science & Computational Mathematics, 2016

Climate change is found to be one of the main catastrophes to which human encountered. It serves as a threat to the Planet Earth and to predict its components has great deal of importance to planning on irrigation, controlling pests and diseases, drought as well as crisis management among many others. Since both temperature and humidity are the most important meteorological parameters so that other atmospheric changes are function of these two parameters, the present research tries to put forward appropriate prediction on them by using models of nonlinear Autoregressive Neural Network and Autoregressive Network with Exogenous inputs (NARX). For this purpose, metrological data for Bushehr province in south of Iran for the years 2012-2013 and model performance criteria including R 2 , RMSE and NRMSE were used. Different architectures for dynamic artificial neural network models were investigated through comparing the root mean square error. Models of performance validation suggested that Nonlinear Autoregressive Exogenous (NARX) forecasts temperature and humidity more accurate than Nonlinear Autoregressive Neural Network (NAR).

Pattern Recognition Through Empirical Mode Decomposition for Temperature Time Series Between 1986 and 2019 in Mexico City Downtown for Global Warming Assessment

Communications in Computer and Information Science, 2019

Global warming is a real threat for the survival of life on Earth in the following 80 years. The effects of Global Warming are particularly harmful for inhabitants of very saturated urban settlements, which is the case of Mexico City. In this work, we analyse temperature time series from Mexico City Downtown, taken hourly between 1986 and 2019. The gaps in the time series were interpolated through the kriging method. Then, temporal tendencies and main frequencies were obtained through Empirical Mode Decomposition. The first frequency mode reveals a clear increasing tendency driven by Global Warming, which for 2019 was of 0.72 • C above a 30-year baseline period mean between 1986 and 2016. Furthermore, the shorter periods identified in the first intrinsic mode functions are likely driven by the solar activity periods. It remains to find the origin of the smallest identified periods in the time series (<0.36 years).

A Novel WD-SARIMAX Model for Temperature Forecasting Using Daily Delhi Climate Dataset

Sustainability

Forecasting is defined as the process of estimating the change in uncertain situations. One of the most vital aspects of many applications is temperature forecasting. Using the Daily Delhi Climate Dataset, we utilize time series forecasting techniques to examine the predictability of temperature. In this paper, a hybrid forecasting model based on the combination of Wavelet Decomposition (WD) and Seasonal Auto-Regressive Integrated Moving Average with Exogenous Variables (SARIMAX) was created to accomplish accurate forecasting for the temperature in Delhi, India. The range of the dataset is from 2013 to 2017. It consists of 1462 instances and four features, and 80% of the data is used for training and 20% for testing. First, the WD decomposes the non-stationary data time series into multi-dimensional components. That can reduce the original time series’ volatility and increase its predictability and stability. After that, the multi-dimensional components are used as inputs for the SA...

Forecasting of Summer Monsoon Rainfall over Gangetic West Bengal, India Utilising Intrinsic Mode Functions, Linear and Neural Regression

Journal of Modeling and Optimization

The South West Monsoon rainfall data of the meteorological subdivision number 6 of India enclosing Gangetic West Bengal is shown to be decomposable into eight empirical time series, namely Intrinsic Mode Functions. This leads one to identify the first empirical mode as a nonlinear part and the remaining modes as the linear part of the data. The nonlinear part is modeled with the technique Neural Network based Generalized Regression Neural Network model technique whereas the linear part is sensibly modeled through simple regression method. The different Intrinsic modes as verified are well connected with relevant atmospheric features, namely, El Nino, Quasi-biennial Oscillation, Sunspot cycle and others. It is observed that the proposed model explains around 75% of inter annual variability (IAV) of the rainfall series of Gangetic West Bengal. The model is efficient in statistical forecasting of South West Monsoon rainfall in the region as verified from independent part of the real da...

Neural Network and Regression Methods for Estimation of the Average Daily Temperature of Hyderabad for the Years 2018-2020

International Journal of Economic and Environmental Geology

A qualitative study on temperature distribution has been executed in Hyderabad by several researchers. This study, however, is the first attempt to study temperature distribution quantitatively. Two different methods, i.e., Artificial Neural Network (ANN) and Regression Analysis (RA), have been used to determine the average daily temperature distribution for Hyderabad, a city in Pakistan. Both the methods are used to predict the average daily temperature of the years; 2018, 2019, and 2020. In Neural Network (NN) analysis, the network was trained and validated for three years with temperature recorded from 2015-2017. With the help of training and validation parameters of the hidden layer, the average d aily temperature was predicted for 2018-2020. Based on input parameters (dew point, relative humidity, and wind speed), a multiple regression model was developed, and average daily temperature for the years 2018-2020 was predicted again. For validation of the model statistical errors, ...

Neural Network Analysis of Time Series: Applications to Climatic Data

Southern Hemisphere Paleo- and Neoclimates, 2000

Artificial neural networks are parallel computational algorithms which can simulate very efficiently complex dynamical systems. In this work we show, by means of real-world applications, that this technique can be a useful tool in the analysis of time-series data related to climate. We study two time series: the first one characterizes the solar activity as measured by the annual mean value of the sunspot number (Wolf number); the second one is the record of summer monsoon rainfall over India. Both records are often used in the literature as benchmarks for testing new statistical techniques. From these studies we conclude that artificial neural networks can advantageously substitute conventional methods of time series analysis. Moreover, they reveal themselves as a promising way of making predictions on climatic phenomena.

Comparative Study of Wavelet-SARIMA and EMD-SARIMA for Forecasting Daily Temperature Series

International Journal of Analysis and Applications, 2022

This paper aims to find a forecasting model based on the comparative study of wavelet- ARIMA and EMD-ARIMA models and residual-based model selection technique for temperature time series. Time series analysis is essential in studying temperature data for investigating the variation and predicting the future trend, in which we can control the changes and make good decisions. And most important is to understand the trend in the series with time. This study applied hybridized models of wavelet transform and empirical mode decomposition with seasonal autoregressive integrated moving average (SARIMA), which combines two models to get better accuracy, for forecasting daily average temperature time series data in the central region of Eritrea, Asmara. Daily data was collected for 30 years, from January 1, 1991, to December 31, 2020. The study compares WT-SARIMA and EMD-SARIMA models to find a well fit and better forecasting model. Model selection techniques are essential for time series an...