Browsing by Author "Kushan, M.A.K."

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An Approach for Prediction of Weekly Prices of Green Chili in Sri Lanka: Application of Artificial Neural Network Techniques
(Journal of Agricultural Sciences – Sri Lanka, 2022) Basnayake, B.R.P.M.; Kaushalya, K.D.; Wickaramarathne, R.H.M.; Kushan, M.A.K.; Chandrasekara, N.V.
Purpose: Predicting the prices of crops is a principal task for producers, suppliers, governments and international businesses. The purpose of the study is to forecast the prices of green chili, which is a cash crop in Sri Lanka. Artificial neural networks were applied as they help to extract important insights from the bulk of data with a scientific approach. Research Method: The Time Delay Neural Network (TDNN), Feedforward Neural Network (FFNN) with Levenberg-Marquardt (LM) algorithm and FFNN with Scaled Conjugate Gradient (SCG) algorithm were employed on weekly average retail prices of green chili in Sri Lanka from the 1st week of January 2011 to the 4th week of December 2018. The performance of models was evaluated through the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Normalized Mean Squared Error (NMSE). Findings: Among the three methods implemented, the FFNN model using the LM algorithm exhibited the highest accuracy with a minimum MSE of 0.0033, MAE of 0.0437 and NMSE of 0.2542. The model built using the SCG algorithm fitted data with a minimum MSE of 0.0033, MAE of 0.0458 and NMSE of 0.2549. Among the fitted TDNN models, the model with 8 input delays were a better model with an MSE of 0.0036, MAE of 0.0470 and NMSE of 0.3221. FFNNs outperformed TDNN in forecasting green chili prices of Sri Lanka. Originality/ Value: The neural network approach in forecasting the prices of green chili provides more accurate results to make decisions based on the trends and to identify future opportunities.
Identification of factors and classifying the accident severity in Colombo - Katunayake expressway, Sri Lanka
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Kushan, M.A.K.; Chandrasekara, N.V.
Sri Lanka’s expressway system was launched in 2011 and now owns three major expressways. Many peoples choose expressways rather than normal ways due to the reasons of time, traffic, easy of driving, etc. According to police reports of highway main traffic police branch, in recent years the number of accidents occurring in expressways is increasing drastically. Nowadays, the rate of accident occurrence in Colombo-Katunayake Expressway is high compared to the other two expressways and there was no previous research has been done in Sri Lanka regarding accidents on ColomboKatunayake expressway. Therefore, the objective of the study was to identify the factors contributing to accidents on the Colombo-Katunayake Expressway and to develop appropriate machine learning models to classify the severity of the accidents. In this study, 704 total accident cases were considered during the period 2013-2019. Chi-square test, logistic regression, and Kruskal–Wallis tests were used to identify the association between the accident severity and other influential variables found from the literature. Finally, seven variables: time category, driver’s age category, vehicle type, the reason for the accident, number of vehicles involved, cause for accident and rainfall were identified as influencing variables to accident severity under 5% level of significance. Naïve Bayes classification algorithm and probabilistic neural network (PNN) were used in the study to forecast accident severity. A random under-sampling technique was used to overcome the class imbalanced problem persists in the data set considered in the study. The final models developed using the Naïve Bayes algorithm and PNN exhibit 72.14% and 74.29% overall classification accuracy respectively. Both aforementioned models can be considered as suitable models to forecast accident severity in the Colombo-Katunayake expressway where the PNN model exhibits slightly higher accuracy. The final models developed by this study can be used to implement safety improvements against traffic accidents in expressways of Sri Lanka.
Identification of factors and classifying the accident severity in Colombo Katunayake expressway, Sri Lanka using multinomial logistic regression
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Kushan, M.A.K.; Chandrasekara, N.V.
Accidents are one of the main social problems in the World, which cause damages or injuries unintentionally and unexpectedly. This is a major issue affecting not only in developing countries like Sri Lanka but also in developed countries. Sri Lanka's expressway system was launched in 2011 and currently has three major expressways: Southern Expressway, Colombo-Katunayake Expressway and Outer-Circle Expressway. After the construction of expressways, many people opted for expressways based on time, traffic, ease of driving, etc., rather than ordinary roads. The number of accidents on expressways has been on the rise in recent years compared to the past. At present, the accident rate on the Colombo-Katunayake Expressway, which connects the Sri Lankan capital, Colombo with Bandaranaike International Airport, Katunayake and Negombo, is high compared to the other two expressways, but no research has been done to date regarding this. Therefore, the objective of the study was to identify the factors contributing to accidents on the Colombo-Katunayake Expressway and to develop an appropriate regression model to classify the severity of the accidents. In this study, 704 total accident cases of Colombo-Katunayake expressway were considered during the period from 2013 to 2019. Initially, Pearson Chi-square, Logistic regression and Kruskal–Wallis H tests were used to identify the association between the multinomial response variable (accident severity) and eleven predictor variables identified based on the literature. Finally, from selected predictor variables, seven variables: time category, driver’s age category, vehicle type, reason for the accident, number of vehicles involved, cause for accident and rainfall were identified as influencing variables to accident severity under 5% level of significance. Since this is not a time series data, 80% of the data were selected in various ways for model building and the remaining 20% were used to test the performance of the built models. Considering significant variables identified above, Multinomial Logistic Regression (MLR) was trained using the stepwise enter method with different data selections criteria. The Random under-sampling technique was used to overcome the class imbalance problem that persists in the data set considered in the study and after selecting the best model, the adequacy of the model was examined and classified the severity of accidents in Colombo-Katunayake Expressway. The final MLR model predicts accident severity with an overall accuracy of 64.3% and rainfall, cause for accident and time category (it is a categorical variable that divides 24 hours into four equal parts) have been identified as the most influential factors affecting accidents on the Colombo-Katunayake Expressway. Furthermore, the final model depicts, with rainy weather, high speed, sleepiness, technical faults and reckless driving increased the likelihood of an accident on the Colombo-Katunayake Expressway, and [0-6] and [12-18] hours were identified as dangerous time categories. The final model developed by this study can be used to implement safety improvements against traffic accidents in expressways of Sri Lanka. As a future study, machine learning techniques can be employed to identify better models with higher classification accuracy.