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Item Application of fuzzy goal programming model to assess optimal multi crop cultivation planning(Agriculture and Natural Resources, 2022) Hakmanage, N.M.; Chandrasekara, N.V.; Jayasundara, M.Importance of the work: Planning for the optimal use of resources in agricultural systems considering uncertainty, with the objective of maximizing profit and production, will improve the social and economic conditions of farmers. Objectives: A rural farming area in Sri Lanka was used as a study site to apply the fuzzy goal programming (FGP) approach to identify the optimal cultivation plan and land resource allocation under uncertainty to optimize profit, production, labor, water use, fertilizer costs and land allocation. Materials & Methods: A tolerance-based FGP technique was used to quantify the fuzziness of different goals for the model. This study was carried out using 24 crops on a total land area under cultivation of 47.4 ha. These crops were categorized into three varieties: vegetable, fruit and other. Furthermore, the crops were classified into seven groups according to the required period of cultivation. Results: The proposed model suggested statistically significant increments of 11% and 10.6% for the net return and harvest amount, respectively, for the 24 crops compared to existing cultivation techniques.Main finding: The FGP multi-crop cultivation planning approach is a new application for the Sri Lankan rural farming community and it should be useful for agricultural planners, by allowing them to make more informed recommendations to the farming community. Crops that provide higher levels of production and profit than those currently being cultivated should be developed to extend cultivation under the supervision of agricultural experts or officers to obtain sustainable development of cultivation.Item 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.Item Effect of Common Culinary Methods Practiced in Sri Lanka on the Nutrient Composition of Commonly Consumed Vegetables and Other Foods(International Journal of Food Science, 2021) Dewangani, H.G.N.; Jayawardena, B.M.; Chandrasekara, N.V.; Wijayagunaratne, H.D.S.P.In Sri Lankan traditional cooking, coconut and spices are incorporated to enhance the taste, flavor, and aroma. However, little attention has been given to assess the effect of these ingredients on the nutritional and chemical composition of the consumed food. The objective of this study was to ascertain the effect of traditional cooking methods on the chemical composition of vegetables, cereals and cereal-based foods, legumes, and selected nonvegetarian food varieties consumed in the daily diet. The results indicate that the addition of coconut milk (CM), coconut scraps, and coconut oil (CO) had a significant impact on the fat content of the prepared foods (p < 0.05). Cooking facilitated the incorporation of fat into food. According to the results, more percentage increases of fat content were observed in tempered string beans (97.51%) and cauliflower milk curry (96.6%). Data revealed that boiling helped to reduce the fat content in cereals and legumes. The cooked foods prepared using traditional recipes with CM, CO, or scraps have higher nutritional content than raw foods and have a significant nourishing potential that meets the daily energy requirements (p < 0.05). It can be concluded that the chemical composition of cooked food serves as a more realistic guideline in recommending dietary interventions in disease and weight management.Item 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.Item Linear programming approach to assess an optimal cultivation plan: A case study(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Hakmanage, N.M.; Chandrasekara, N.V.; Jayasundara, D.D.M.An optimal cultivation plan refers to the procedure or action of making the best or most effective use of resources for cultivation in a sustainable manner while maximizing net return. Reaching an efficient cultivation plan and utilization of resources and requirements is often a challenging problem. To utilize resources and requirements such as water, land, manpower, fertilizers and seeds, optimization techniques are used. The objective of this research is to maximize the net return of the cultivation using linear programming technique and allocate the arable land optimally. Linear programming is the most convenient and effective tool to handle the objective function with many constraints. This study was carried out in a rural village located in Dompe divisional secretariat in Gampaha district using 150 farming lands, to determine the land resource allocation for twelve selected crops: bitter gourd, lady's fingers, manioc, potatoes, rambutan, banana, pineapple, beetle, rice, coconut, tea and pepper. The linear programming model is formulated for the optimal land resource allocation of 4477.2 perches. The maximum net return projected by the proposed model is Rs 6,370,512.00 for cultivation seasons. The proposed solution is a 34.96% increase in profit as compared to the actual profit obtained from the cultivations. Crops like rambutan, rice, manioc and pineapple which provides a higher return should be developed and cultivation extended under the supervision of the agricultural expertise or officers. The model suggests that some crops such as lady’s fingers, potatoes, banana and coconut may not be providing comparable returns versus the other selected crops. The results reveal that linear programming approach will significantly improve the net benefits with optimal crop area allocation. The limitation of this study is that it is considered the soil condition is the same for all crops in the study area. Advanced operations research techniques like multi objective nonlinear programming models will be employed for this study in future work.Item Tourist volume forecasting: An approach with supervised machine learning algorithms(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Basnayake, B.R.P.M .; Chandrasekara, N.V.The tourism industry generates almost US$4 billion of income and provides direct and indirect employment to a large number of people in the country. Expert knowledge on the travel behaviour of tourists is an important part of planning and aids decision making for all stakeholders including the government and private business organisations. There was a severe drop in tourist arrivals during the civil war and was also apparent after the more recent Easter Sunday bomb attack. The study compared the predictions of different forecasting models on tourist arrivals in Sri Lanka, in an effort to identify the most appropriate model. The supervised machine learning algorithms (MLA) applied were Time Delay Neural Network (TDNN) and Feedforward Neural Network (FFNN) with two different algorithms namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG). Recently, MLA has started playing a vital role as an effective forecasting tool. A better model in forecasting was identified using the performance criteria of the Normalized Mean Squared Error (NMSE). As an initial step, monthly data from December 2019 to January 2000 were standardized to maintain the consistency of data. The aforementioned models were trained for 100, 200 and 500 epochs separately, with different numbers of hidden layers and hidden neurons, and detected the model with minimum NMSE for further training. For the selected model from TDNN, subsequently, the transfer functions and time delays were modified. A better model was identified in 500 epochs for the network with 2 hidden layers of 4 and 3 hidden neurons with tansig transfer functions from time delay of 3 (NMSE 0.3537). For the FFNN model, input combinations were recognized using the Pearson correlation coefficient and Spearman's rank correlation coefficient. Among the trained models with the different input combinations, the model with MA3, MA6, MA9, MA12, and MA15, lag 1, lag 2, lag 3, lag 11 and lag 12 indicated the lower NMSE of 0.5244 where Moving Average (MA) indicates current and past values and depends linearly on the output variable and lags being predetermined fixed quantity of passing time. For the FFNN, a better model was identified with the adjustment of parameters. A better model was identified in 100 epochs for the network with 3 hidden layers of 3 hidden neurons in each layer with tansig transfer functions, a learning rate (ɳ) of 0.01, a combination coefficient (μ) of 0.001 and a decreasing factor as 0.1 and increasing factor as 10 of μ (NMSE 0.2234). For the SCG algorithm, the lowest performance measurement value, NMSE was 0.3193. The model had 500 epochs with 3 hidden layers of 3, 2 and 2 hidden neurons respectively, transfer functions with tansig in all hidden layers, a sigma parameter value of 5e (- 5) and a lambda of 5e (-7). The main conclusion is that all the discussed network models capture the actual behaviour of the testing set while the minimum NMSE was identified in the FFNN with the LM algorithm. The findings of the analysis are beneficial, as tourism is a global service industry and a source of foreign exchange earnings and a key employment generation sector for the country.Item 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.Item HOURLY SOLAR RADIATION FORECASTING USING ARTIFICIAL NEURAL NETWORK MODEL FOR COLOMBO, SRI LANKA(Advances and Applications in Statistics, 2019) Saumyamala, M.G.A.; Chandrasekara, N.V.Sri Lanka is a tropical country located close to the equator with abundant sunlight throughout the year. For efficient utilization of this solar resource for power generation in photovoltaic (PV) systems and agricultural modelling, prior knowledge of global solar radiation (GSR) in the future is important. Limited availability of onsite GSR data and the high cost are the main barriers in forecasting GSR for Sri Lanka. As a solution this study suggests an artificial neural network (ANN) model to forecast hourly solar radiation using weather data and solar angles to forecast GSR in Colombo, specifically using feedforward neural network (FFNN) trained with Levenberg- Marquardt (LM) back propagation algorithm. Hourly weather data for 6 weather variables and two solar angles from 1st of March 2017 to 14th of February 2018 were used for training, validation and testing the network. Input parameters and training parameters were adjusted to identify the most accurate network configuration and the performance of the network was measured using normalized mean squared error (NMSE). Coefficient of determination (R2) measured to identify the appropriateness of using weather variables and solar angles to forecast solar radiation. The final hourly FFNN model consists of 2 hidden layers and there are 5 neurons and 3 neurons in each layer respectively. This model was able to forecast hourly solar radiation with 0.0961 NMSE and the R2 was 90.39%. This implies the capability of this model for prediction of global solar radiation when unseen weather data input supply to the model and ensure the accuracy of the result.Item An Ensemble Technique For Multi Class Imbalanced Problem Using Probabilistic Neural Networks(Advances and Applications in Statistics, 2018) Chandrasekara, N.V.; Tilakaratne, C.D.; Mammadov, M.A.The class imbalanced problem is one of the major difficulties encountered by many researchers when using classification tools. Multi class problems are especially severe in this regard. The main objective of this study is to propose a suitable technique to handle multi class imbalanced problem. Probabilistic neural network (PNN) is used as the classification tool and the directional prediction of Australian, United States and Srilankan stock market indices is considered as the application. We propose an ensemble technique to handle multi class imbalanced problem that is called multi class undersampling based bagging (MCUB) technique. This is a new initiative that has not been considered in the literature to handle multi class imbalanced problem by employing PNN. The results obtained demonstrate that the proposed MCUB technique is capable of handling multi class imbalanced problem. Therefore, the PNN with the proposed ensemble technique can be used effectively in data classification. As a further study, other classification tools can be used to investigate the performance of the proposed MCUB technique in solving class imbalanced problems.Item Prediction of daily gold prices in Sri Lanka: A comparison of time series and artificial neural network models.(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Shashikala, M. A. G.; Chandrasekara, N.V.; Jayasundara, D. D. M.Gold is an ancient and one of the most precious and popular commodities in the world. Investors at all levels are attracted to gold in all times as gold is a solid and tangible long term store of value. Gold can be used in portfolios to reduce volatility, to protect global purchasing power and minimize losses during times of market shock. Therefore, a more accurate forecast of gold prices can help the investors in their decision making. The main objective of this study is to develop a more accurate and efficient model to forecast daily gold prices in Sri Lanka. For this study, the daily gold prices (LKR/Troy Ounce), published by the Central Bank of Sri Lanka from 10th June 2014 to 30th November 2016 were used. During the past decades, Traditional Time Series Modelling was used in forecasting financial data but recently, Artificial Neural Networks are used in many researches of forecasting. Hence, both traditional time series modelling and artificial neural network approaches were considered in developing a more accurate and efficient model in the study. Autoregressive Integrated Moving Average model (ARIMA); a traditional time series model and Feed Forward Neural Network model (FFNN); an artificial neural network model, were compared. The model evaluation was carried out using performance measures; Normalized Mean Squared Error (NMSE) and Directional Symmetry (DS). ARIMA (2,1,2) model with NMSE and DS values, 0.1358 and 71% respectively, was selected as the best model among the fitted traditional time series models. FFNN model containing two hidden layers, with 4 and 5 neurons respectively in each layer with model parameters; mu of 0.00061 and minimum gradient of 0.7e-7 was selected as the best model among the trained FFNN models. The NMSE is 0.000139 and DS is 78% of the final ANN model. The deviation between the actual and forecast values (NMSE) is very low in the fitted FFNN model and the accuracy of the predicted direction (DS) is more than that of ARIMA (2,1,2) model. The above results prove that the ANN outperforms traditional time series modelling techniques in forecasting highly volatile financial data such as daily gold prices.
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