Empirical mode decomposition with least square support vector machine model for river flow forecasting

Accurate information on future river flow is a fundamental key for water resources planning, and management. Traditionally, single models have been introduced to predict the future value of river flow. However, single models may not be suitable to capture the nonlinear and non-stationary nature of the data. In this study, a three-step-prediction method based on Empirical Mode Decomposition (EMD), Kernel Principal Component Analysis (KPCA) and Least Square Support Vector Machine (LSSVM) model, referred to as EMD-KPCA-LSSVM is introduced. EMD is used to decompose the river flow data into several Intrinsic Mode Functions (IMFs) and residue. Then, KPCA is used to reduce the dimensionality of the dataset, which are then input into LSSVM for forecasting purposes. This study also presents comparison between the proposed model of EMD-KPCA-LSSVM with EMD-PCA-LSSVM, EMD-LSSVM, Benchmark EMD-LSSVM model proposed by previous researchers and few other benchmark models such as Single LSSVM and Support Vector Machine (SVM) model, EMD-SVM, PCA-LSSVM, and PCA-SVM. These models are ranked based on five statistical measures namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Correlation Coefficient ( r ), Correlation of Efficiency (CE) and Mean Absolute Percentage Error (MAPE). Then, the best ranked model is measured using Mean of Forecasting Error (MFE) to determine its under and over-predicted forecast rate. The results show that EMD-KPCA-LSSVM ranked first based on five measures for Muda, Selangor and Tualang Rivers. This model also indicates a small percentage of under-predicted values compared to the observed river flow values of 1.36%, 0.66%, 4.8% and 2.32% for Muda, Bernam, Selangor and Tualang Rivers, respectively. The study concludes by recommending the application of an EMD-based combined model particularly with kernel-based dimension reduction approach for river flow forecasting due to better prediction results and stability than those achieved from single models

A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction

This study proposed the new hybrid model of Multiple Linear Regression Clustering (MLRC) combined with Support Vector Machine (SVM) to predict tumor size of colorectal cancer (CRC). Three models: Multiple Linear Regression (MLR), MLRC and hybrid MLRC with SVM model were compared to get the best model in predicting tumor size of colorectal cancer using two measurement statistical errors. The proposed model of hybrid MLRC with SVM have found two significant clusters whereby, each clusters contained 15 and three significant variables for cluster 1 and 2, respectively. The experiments found that the proposed model tend to be the best model with least value of Mean Square Error (MSE) and Root Mean Square Error (RMSE). This finding has shed light to health practitioner in determining the factors that contribute to colorectal cancer

Machine learning approach for flood risks prediction

Flood is one of main natural disaster that happens all around the globe caused law of nature. It has caused vast destruction of huge amount of properties, livestock and even loss of life. Therefore, the needs to develop an accurate and efficient flood risk prediction as an early warning system is highly essential. This study aims to develop a predictive modelling follow Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology by using Bayesian network (BN) and other Machine Learning (ML) techniques such as Decision Tree (DT), k-Nearest Neighbours (kNN) and Support Vector Machine (SVM) for flood risks prediction in Kuala Krai, Kelantan, Malaysia. The data is sourced from 5-year period between 2012 until 2016 consisting 1,827 observations. The performance of each models were compared in terms of accuracy, precision, recall and f-measure. The results showed that DT with SMOTE method performed the best compared to others by achieving 99.92% accuracy. Also, SMOTE method is found highly effective in dealing with imbalance dataset. Thus, it is hoped that the finding of this research may assist the non-government or government organization to take preventive action on flood phenomenon that commonly occurs in Malaysia due to the wet climate

Malaysian Youth, Social Media Following, and Natural Disasters: What Matters Most to Them?

This study attempted to understand youth’s social media following during natural disasters. It investigated Malaysian youth’s perception about the importance of immediacy, trust, and accuracy of social media news and information during natural disasters. This study found that youth prefer to consume social media news and information during natural disasters such as flooding, landslides, and haze. This is mainly contributed by the immediacy of news and information on social media. Besides immediacy, youth also value trust and accuracy of social media news and information during natural disasters. As the findings of this study revealed, youth are more inclined to become followers to social media creators who have a high level of trust and whom they believe can provide accurate natural disasters news and information. Hence, it is crucial that the authority and news organizations to provide natural disasters news on social media that is timely and accurate

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

Comparison of daily rainfall forecasting using multilayer perceptron neural network model

Rainfall is important in predicting weather forecast particularly to the agriculture sector and also in environment which gives great contribution towards the economy of the nation. Thus, it is important for the hydrologists to forecast daily rainfall in order to help the other people in the agriculture sector to proceed with their harvesting schedules accordingly and to make sure the results of their crops would be satisfying. This study is set to forecast the daily rainfall future value using ARIMA model and Artificial Neural Network (ANN) model. Both method is evaluated by using Mean Absolute Error (MAE), Mean Forecast Error (MFE), Root Mean Squared Error (RMSE) and coefficient of determination (R ). The results showed that ANN model has outperformed results than ARIMA model. The results also showed ANN has under-forecast the daily rainfall data by 2.21% compare to ARIMA with over-forecast of -3.34%. From this study, it shows that the ANN (6,4,1) model produces better results of MAE (8.4208), MFE (2.2188), RMSE (34.6740) and R (0.9432) compared to ARIMA model. This has proved that ANN model has outperformed ARIMA model in predicting daily rainfall values

Investigating students’ environmental knowledge, attitude, practice and communication

This study investigated the relationship between students’ knowledge, attitude and practice of the environment and effective communication of environmental messages. For this purpose, a knowledge, attitude and practice (KAP) survey was conducted, involving 895 students from 16 higher learning institutions in Malaysia. The findings revealed that students in general, have a good level of environmental knowledge. However, knowledge does not necessarily lead to practice. There was a weak relationship between students’ level of knowledge and sustainable environment practices. Similarly, there was a weak relationship between students’ attitude and sustainable environment practices. Hence, attitude is not a good predictor for sustainable environment practices. These findings highlight the complexity of the relationship between students’ knowledge, attitude and sustainable environment practice. The findings of this study also suggested that, the internet is regarded as students preferred choice of media which can be utilised to disseminate environmental information. It is important, however, not to disregard the roles of more traditional media such as television and newspapers, as they can also be effectively used to deliver environmental information. Besides media, educational institutions and family also have crucial roles to disseminate environmental information and encourage good practice. Since many of the earlier studies of this nature have been conducted in at other places, particularly in the first world countries, this study is expected to contribute to the knowledge based on Malaysia’s own experience as a developing nation that aspire to champion sustainable environmen

Domain of application in context-aware recommender systems: a review

The purpose of this research is to provide an exhaustive overview of the existing literature on the domain of applications in recommender systems with their incorporated contextual information in order to provide insight and future directions to practitioners and researchers.We reviewed published journals and conference proceedings papers from 2010 to 2016.The review finds that multimedia and e-commerce are the most focused domains of applications and that contextual information can be grouped into static, spatial and temporal contexts

Three-parameter lognormal distribution: parametric estimation using l-moment and tl-moment approach

The three-parameter lognormal (LN3) distribution has been applied to the frequency analysis of flood events. L-moment and TL-moment methods are applied in estimating parameters of the LN3 distribution which are L-moment, η = 0 and TL-moment, η = 1, 2, 3, and 4 to the LN3 distribution. A simulation study is conducted in this paper by fitting this distribution to generate LN3 and non LN3 samples. Relative Root Mean Square Error (RRMSE) and relative bias are evaluated to illustrate the performance of this distribution. The performance of TL-moments approach was compared with L-moments based on the streamflow data from Sg. Trolak and Sg. Slim which are located in Perak, Malaysia. The results showed that TL-moments approach produced a better result at high quantile estimation compared to L-moments

Electricity consumption forecasting using Nonlinear Autoregressive with External (Exogeneous) input neural network

Forecasting is prediction of future values based on historical data. Electricity consumption forecasting is crucial for utility company to plan for future power system generation. Even though there are previous works of electricity consumption forecasting using Artificial Neural Network (ANN), but most of their data is multivariate data. In this study, we have only univariate data of electricity consumption from January 2009 to December 2018 and wish to do a prediction for a year ahead. On top of that, our data consist of autoregressive component, hence Nonlinear Autoregressive with External (Exogeneous) Input (NARX) Neural Network Time Series from Matlab R2018b was used. It gives the mean absolute percentage error (MAPE) between actual and predicted electricity consumption of 1.38%