Statistics & Computer Science
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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 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 Determining and Comparing Multivariate Distributions: An Application to AORD and GSPC with their related financial markets(2016) Chandrasekara, N.V.; Mammadov, M.; Tilakaratne, C.D.Many real world applications are associated with more than one variable and hence, identifying multivariate distributions associated with real world problems portrays great importance today. Many studies can be found in the literature in this aspect and most of them are associated with two variables/dimensions and the maximum dimension of multivariate distribution found in the literature is four. Different optimization techniques have been used by researchers to find multivariate distributions in their studies. Numerical methods can be identified as more preferable than analytical methods when the dimension of the problem is high. The main objective of this study is to identify the multivariate distribution associated with the return series of Australian all ordinary index (AORD) and those of the related financial markets and compare it with the multivariate distribution of return series of the US GSPC index and its related financial markets. No research were found in the literature which were aimed at finding aforesaid multivariate distribution and comparisons. Moreover no evidence found for identifying a multivariate distribution with six dimensions. Five financial markets: Amex oil index, Amex gold index, world cocoa index, exchange rate of Australian dollar to United States dollar and US GSPC index were found to be associated with AORD. Hence the attempt was to derive the multivariate distribution of return series of AORD and these five return series and therefore the optimization problem of the study is a six dimension problem which associated with forty three parameters need to be estimated. A local optimization technique and a global optimization technique were used to estimate the parameters of the multivariate distribution. Results exhibit that the parameter estimates obtained from the global optimization technique are better than the parameter estimates obtained from the local optimization technique. The multivariate distribution of return series of AORD and related financial markets is central, less peaked and have fat tails. A comparison was done with another multivariate distribution of a return series of a leading stock market index: GSPC and return series of its associated financial markets and found that both distributions are alike in shape. Two periods were identified in the AORD series and found that the shape of the multivariate distribution of one period is similar to the shape of the multivariate distribution of full data set while the shape of the multivariate distribution of the other period is dissimilar to that of full data set.Item Estimating parameters of multivariate scaled t distribution of GSPC and its associated financial indices(2015) Chandrasekara, N.V.; Mammadov, M.A.; Thilakaratne, C.D.Item Identifying distributions of selected stock returns(2015) Chandrasekara, N.V.; Thilakaratne, C.D.; Mammadov, M.A.Item Forecasting exchange rates using time series and neural network approaches(Sri Lanka Association for the Advancement of Science, 2013) Nanayakkara, K.A.D.S.A.; Chandrasekara, N.V.; Jayasundara, D.D.M.Item Identifying factors associated with severity of asthma in Kegalle District(Sri Lanka Association for the Advancement of Science, 2013) Peramuna, P.R.N.L.; Chandrasekara, N.V.; Jayasundara, D.D.M.Item The impact of mothers’ migration for work abroad on children’s education(Sri Lanka Association for the Advancement of Science, 2013) Dissanayake, P.L.; Chandrasekara, N.V.; Jayasundara, D.D.M.