scholarly journals Forecasting Drought Using Modified Empirical Wavelet Transform-ARIMA with Fuzzy C-Means Clustering

2018 ◽  
Vol 11 (3) ◽  
pp. 1152
Author(s):  
Muhammad Akram Shaari ◽  
Ruhaidah Samsudin ◽  
Ani Shabri Ilman
Keyword(s):  
Wavelet Transform ◽  
Moving Average ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Intrinsic Mode Functions ◽  
Drought Forecasting ◽  
Fuzzy C Means ◽  
Empirical Wavelet Transform ◽  
Fuzzy C Means Clustering ◽  
Auto Regressive

Drought forecasting is important in preparing for drought and its mitigation plan. This study focuses on the investigating the performance of Auto Regressive Integrated Moving Average (ARIMA) and Empirical Wavelet Transform (EWT)-ARIMA based on clustering analysis in forecasting drought using Standard Precipitation Index (SPI). Daily rainfall data from Arau, Perlis from 1956 to 2008 was used in this study. SPI data of 3, 6, 9, 12 and 24 months were then calculated using the rainfall data. EWT is employed to decompose the time series into several finite modes. The EWT is used to create Intrinsic Mode Functions (IMF) which are used to create ARIMA models. Fuzzy c-means clustering is used on the instantaneous frequency given by Hilbert Transform of the IMF to create several clusters. The objective of this study is to compare the effectiveness of the methods in accurately forecasting drought in Arau, Malaysia. It was found that the proposed model performed better compared to ARIMA and EWT-ARIMA.

scholarly journals Future Trend Forecast by Empirical Wavelet Transform and Autoregressive Moving Average

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2621 ◽  
Author(s):  
Qiusheng Wang ◽  
Haipeng Li ◽  
Jinyong Lin ◽  
Chunxia Zhang
Keyword(s):  
Complex System ◽  
Wavelet Transform ◽  
Moving Average ◽  
Parametric Model ◽  
Future Trend ◽  
Autoregressive Moving Average ◽  
System A ◽  
Empirical Wavelet Transform ◽  
Novel Method ◽  
System States

In engineering and technical fields, a large number of sensors are applied to monitor a complex system. A special class of signals are often captured by those sensors. Although they often have indirect or indistinct relationships among them, they simultaneously reflect the operating states of the whole system. Using these signals, the field engineers can evaluate the operational states, even predict future behaviors of the monitored system. A novel method of future operational trend forecast of a complex system is proposed in this paper. It is based on empirical wavelet transform (EWT) and autoregressive moving average (ARMA) techniques. Firstly, empirical wavelet transform is used to extract the significant mode from each recorded signal, which reflects one aspect of the operating system. Secondly, the system states are represented by the indicator function which are obtained from those normalized and weighted significant modes. Finally, the future trend is forecast by the parametric model of ARMA. The effectiveness and practicality of the proposed method are verified by a set of numerical experiments.

scholarly journals Fuzzy C-Means Based Clustering and Rule Formation Approach for Classification of Bearing Faults Using Discrete Wavelet Transform

Computation ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 54 ◽  
Author(s):  
Anbu ◽  
Thangavelu ◽  
Ashok
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Rule ◽  
Discrete Wavelet ◽  
Clustering Method ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.

scholarly journals Smoothing of 70-Year Daily Rainfall Data Based on Moving Average and Weighted average Technique

2019 ◽  
Vol 8 (6) ◽  
pp. 1407-1410
Keyword(s):  
Moving Average ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Extreme Value Analysis ◽  
Cyclic Pattern ◽  
Simple Method ◽  
Value Analysis ◽  
Rainfall Analysis ◽  
Daily Rainfall Data ◽  
South West Monsoon

An extreme value analysis of rainfall for Thanjavur town in Tamil Nadu was carried out using 70 years of daily rainfall data. Moving average method is a simple method to understand rainfall trend of the selected station. The analysis has been carried out for monthly, seasonal and annual rainfalls. No other graphical methods such as Ordinate graph, Bar diagram, Chronological chart will describe about the trend or cyclic pattern. By smoothening out the extreme variations and indicating the trend or cyclic pattern is known as moving average curve. Through this moving average curve, it is possible to understand the trend which can be used in the future years. From the results of annual wise rainfall analysis, based on 3-year, 5-year, 10-year moving average, it is found that there is no persistent regular cycle is visible and where as in 30-year, 40-year, 50-year moving average a horizontal linear trend has been observed. In Winter season, Summer season, North-East monsoon, South- West monsoon wise rainfall analysis, for 3-year, 5-year, 10-year moving average denote no apparent trend or cyclicity where as in 30-year, 40-year, 50- year moving average a horizontal linear cycle has been noticed. It is clear from the study that there is no large variation of rainfall that had been occurred in the Thanjavur city based on selected years of analysis.

scholarly journals Characterization of Monsoonal Rainfall in the Sudano-Sahelian Zone of Northwestern Nigeria

2021 ◽  
Vol 1 (2) ◽  
pp. 123-135
Author(s):  
Abdullahi Umar ◽  
Saadu Umar Wali ◽  
Ibrahim Mustapha Dankani
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Daily Rainfall ◽  
Kendall Test ◽  
Morlet Wavelet ◽  
Rainfall Data ◽  
Seasonal Rainfall ◽  
Rainfall Characteristics ◽  
Sahelian Zone

Wavelet transform has been underutilized in characterization of rainfall (Real Onset Dates and Real Cessation Dates) in the study area. This study aims at the characterization of monsoonal rainfall. Daily rainfall data of four stations for the period 1981-2018 were collected from Nigerian Meteorological Agency. The Intra-seasonal Rainfall Monitoring Index (IRMI) was generated and used in determining the RODs and RCDs. The Mann–Kendall test was used to detect trends of the rainfall characteristics. Wavelet transform was used in modelling RODs and RCDs. Findings revealed that RODs vary between stations. There is low (0.3 Spearman’s Rank r) correlation between latitudes and Early Cessations (ECs) of rains. The Morlet wavelet analysis revealed that from 1999 to 2018, there were more of EOs and NOs especially in Kano station. We conclude that from 1981 to 2018 there has been a minimal increase in the retreat dates of rainfall in the study area.

scholarly journals FORECASTING DROUGHT WITH ARIMA MODEL AND STANDARDIZED PRECIPITATION INDEX (SPI)

2019 ◽  
Vol 1 (2) ◽  
pp. 32-34
Author(s):  
ALFA MOHAMMED SALISU
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Data Series ◽  
Drought Forecasting ◽  
Data Set ◽  
Forecasting Accuracy ◽  
Diagnostic Checking ◽  
Auto Regressive

Drought forecasting is an important forecasting procedure for preparing and managing water resources for all creatures. Natural disasters across the regions such as flooding, earthquakes, droughts etc. have caused damages to life as a result of which numerous researches have been conducted to assist in reducing the phenomenon. Consequently, therefore, this study considered using Auto-Regressive Integrated Moving Average (ARIMA) model in forecasting drought using Standardized Precipitation Index (SPI) as a forecasting tool which was used to measure and classify drought. The models are developed to forecast the SPI series. Results indicated the forecasting ability of the ARIMA models which increases as the timescales. The study is aimed at using ARIMA method for modeling SPI data series. The studies used data set made up of 624 months, obtained from 1954 to 2008. In the analysis only SPI3 series was non-seasonal while others have seasonality and Seasonal ARIMA was carried out, SPI12 was significant compared with the forecasting accuracy alongside the diagnostic checking having a minimum error of RMSE and MAE in both testing and training phases. The research contributes to the discovering of feasible forecasting of drought and demonstrates that the established model is good and appropriate for forecasting drought.

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