Nonparametric Kernel estimation of annual maximum stream flow quantiles.

A nonparametric kernel methods is proposed and evaluated performance for estimating annual maximum stream flow quantiles. The bandwidth of the estimator is estimated by using an optimal technique and a cross-validation technique. Results obtained from a limited amount of real data from Malaysia show that quantiles estimated by nonparametric method using these techniques have small root mean square error and root mean absolute error. Based on correlation coefficient test shown that the nonparametric model approach is accurate, uniform and flexible alternatives to parametric models for flood frequency analysis

Comparisons of the LH Moments and the L Moments

This paper discusses comparisons of the LH moments method with L moments method. LH moments, a generalization of L moments, based on linear combinations of higher-order statistics was introduced for charactering the upper part of distributions and larger events in data by Wang (1997). Analysis of observed data shows that using LH moments estimates of the upper part of distribution events are expected to be more reasonable than the L moments estimates. A comparison of the LH moment diagram and the L moment diagram of the data also shows that the GEV distribution describes the LH moment ratios better than the L moments

Penggunaan gambar rajah nisbah LQ-momen dalam pemilihan taburan terbaik

The method of moments has been one of the simplest and conventional parameter estimation techniques used in statistical hydrology. However, moment estimates are usually inferior in quality and generally not as efficient as the L-moment estimates, especially for distribution with three or more parameters. Of late, many regional frequency studies in hydrology use the L-moment ratio diagram to select the most appropriate distribution of hydrologic and meteorological data. An advantage of L-moment ratio diagrams is that one can compare the fit of several distributions using a single graphical instrument. The purpose of this paper is to revisit the LQ moments and to develop the LQ-moments ratio diagrams based on median estimator. Using flood-flow data at 73 stations in Peninsular Malaysia, we explored the suitability of various flood frequency models using LQ-moments ratio diagrams compared with L-moments ratio diagrams. The result shows that the new diagrams perform as well as L-moments ratio diagrams for selecting a suitable frequency distributions and makes it an attractive option in flood frequency analysis

The distribution of extreme share return in different Malaysian economic circumstances

This paper presents a study on the performance of probability distribution in various financial periods by investigating the effect of economic cycle on extreme stock return activity. Malaysian stock price KLCI data from 1994-2008 were split into three economy periods corresponding to the growth, financial crisis, and recovery. Four prevalent distributions, specifically generalized lambda distribution (GLD), generalized extreme value (GEV), generalized logistic (GLO), and generalized pareto (GPA) had been employed to model weekly and monthly maximum and minimum share returns of Kuala Lumpur Composite Index (KLCI). L-moment approach had been used to estimate the parameter, while k-sample Anderson darling (k-ad) test had been applied to measure the goodness of fit estimation. In conclusion, GLD is the most appropriate distribution to represent weekly maximum and minimum returns for overall three economic scenarios in Malaysia

River flow forecasting: a hybrid model of self organizing maps and least square support vector machine

Successful river flow time series forecasting is a major goal and an essential procedure that is necessary in water resources planning and management. This study introduced a new hybrid model based on a combination of two familiar non-linear method of mathematical modeling: Self Organizing Map (SOM) and Least Square Support Vector Machine (LSSVM) model referred as SOM-LSSVM model. The hybrid model uses the SOM algorithm to cluster the training data into several disjointed clusters and the individual LSSVM is used to forecast the river flow. The feasibility of this proposed model is evaluated to actual river flow data from Bernam River located in Selangor, Malaysia. Their results have been compared to those obtained using LSSVM and artificial neural networks (ANN) models. The experiment results show that the SOM-LSSVM model outperforms other models for forecasting river flow. It also indicates that the proposed model can forecast more precisely and provides a promising alternative technique in river flow forecasting

Wavelet-support vector machine for forecasting palm oil price

This study examines the feasibility of applying Wavelet-Support Vector Machine (W-SVM) model in forecasting palm oil price. The conjunction method wavelet-support vector machine (W-SVM) is obtained by the integration of discrete wavelet transform (DWT) method and support vector machine (SVM). In W-SVM model, wavelet transform is used to decompose data series into two parts; approximation series and details series. This decomposed series were then used as the input to the SVM model to forecast the palm oil price. This study also utilizes the application of partial correlation-based input variable selection as the preprocessing steps in determining the best input to the model. The performance of the W-SVM model was then compared with the classical SVM model and also artificial neural network (ANN) model. The empirical result shows that the addition of wavelet technique in W-SVM model enhances the forecasting performance of classical SVM and performs better than ANN

Tourism forecasting using hybrid modified empirical mode decomposition and neural network

Due to the dynamically increasing importance of the tourism industry worldwide, new approaches for tourism demand forecasting are constantly being explored especially in this Big Data era. Hence, the challenge lies in predicting accurate and timely forecast using tourism arrival data to assist governments and policy makers to cater for upcoming tourists. In this study, a modified Empirical Mode Decomposition (EMD) and Artificial Neural Network (ANN) model is proposed. This new approach utilized intrinsic mode functions (IMF) produced via EMD by reconstructing some IMFs through trial and error method, which is referred to in this research as decomposition. The decomposition and the remaining IMF components are then predicted respectively using ANN model. Lastly, the forecasted results of each component are aggregated to create an ensemble forecast for the tourism time series. The data applied in this experiment are monthly tourist arrivals from Singapore and Indonesia from the year 2000 to 2013 whereby the evaluations of the model’s performance are done using two wellknown measures; RMSE and MAPE. Based on the empirical results, the proposed model outperformed both the individual ANN and EMD-ANN models

Flood estimation at ungauged sites using group method of data handling in Peninsular Malaysia

Group Method of Data Handling (GMDH) have been successful in many fields such as economy, ecology, medical diagnostics, signal processing, and control systems but given a little attention in hydrology field especially for flood estimation at ungauged sites. Ungauged site basically mean the site of interest is no flood peak data available. This paper presented application of GMDH model at ungauged site to predict flood quantile for T=10 year and T=100 year. There five catchment characteristics implement in this study that are catchment area, elevation, longest drainage path, slope of the catchment and mean maximum annual rainfall. The total number of catchment used for this study is 70 catchments in Peninsular Malaysia. Four quantitative standard statistical indices such as mean absolute error (MAE), root mean square error (RMSE) and Nash-Sutcliffe coefficient of efficiency (CE) are employed. Based on these results, it was found that the GMDH model outperforms the prediction ability of the traditional LR mode

Forecasting drought using modified empirical wavelet transform-ARIMA with fuzzy C-means clustering

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

Comparative analysis of river flow modelling by using supervised learning technique

The goal of this research is to investigate the efficiency of three supervised learning algorithms for forecasting monthly river flow of the Indus River in Pakistan, spread over 550 square miles or 1800 square kilometres. The algorithms include the Least Square Support Vector Machine (LSSVM), Artificial Neural Network (ANN) and Wavelet Regression (WR). The forecasting models predict the monthly river flow obtained from the three models individually for river flow data and the accuracy of the all models were then compared against each other. The monthly river flow of the said river has been forecasted using these three models. The obtained results were compared and statistically analysed. Then, the results of this analytical comparison showed that LSSVM model is more precise in the monthly river flow forecasting. It was found that LSSVM has he higher r with the value of 0.934 compared to other models. This indicate that LSSVM is more accurate and efficient as compared to the ANN and WR model