Browsing by Author "Lakshitha, W. A. D. M."

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The effect of food commodity price fluctuation on inflation in Sri Lanka
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Nadeekantha, H. A. D. D.; Lakshitha, W. A. D. M.; Lakshitha, W. A. D. M.; Chandrasekara, N. V.
In Sri Lanka, the intersection of inflation and food price fluctuations holds profound significance, affecting not only the nation's economic stability but also the daily lives of its citizens. While existing research has extensively focused on the impact of rice prices on inflation, no published studies have been found that specifically investigate the influence of fluctuations in vegetable and fish commodity prices on inflation. Hence, there is a research gap to have a comprehensive understanding about price fluctuation on inflation. Thus, the objectives of this research are to primarily consider the effect of price fluctuations in mostly consuming vegetable and fish commodities on inflation using suitable techniques. The study focuses on key commodities, including beetroot, cabbage, potato, and various fish types (Seer, Mullet, Kelawalla, and Hurulla). Monthly data from January 2014 to June 2022, sourced from the Central Bank of Sri Lanka and the Department of Census and Statistics, were utilized for the analysis, with no missing values. To measure inflation, the National Consumer Price Index (NCPI) was used. Since all the time series of monthly observations of fish and vegetable prices and NCPI were non-stationary, the first differencing of logarithm for all the series was used where it proved the stationary by both graphical and theoretical techniques. After investigating the lag structures for fish and vegetable models, the optimum and the better lags were found. The cointegration test for both models proved that there were correlations between several time series in the long run based on the optimal lag length. Hence, two Vector Error Correction (VEC) models were fitted for two groups of food commodity prices namely, Fish and Vegetables where VEC models are well-suited for examining the relationships between food commodity prices and inflation over time. Strong cointegration relationships were identified inside these two groups. According to the VEC Granger causality test, it was found that beetroot, cabbages and potatoes do Granger-cause in NCPI but cabbages and other selected fishes do not Granger-cause in NCPI. To study the impact on inflation, the impulse response function was used. It was found that price shocks of the Hurulla fish type have a significant positive impact on inflation than other fish types of Seer, Mullet, and Kelawalla. Beetroot price shocks have a significantly more positive impact on inflation than other vegetable types of potatoes, tomatoes, and cabbage. The model, which was fitted for fish prices, the percentage of forecasting errors for NCPI increases over time for each type of fish, according to the forecast error variance decompositions. In the model, which was fitted for vegetable prices, the percentage also increases with time, but it remains smaller compared to the fish. Sri Lanka needs effective strategies and policies to mitigate the challenges of unstable inflation, hence the understanding of price fluctuation on inflation empowers policymakers to craft targeted strategies to mitigate the impact of inflation on daily life.
Identification of factors leading to elephant deaths in human-elephant conflicts
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Lakshitha, W. A. D. M.; Chandrasekara, N. V.; Kavinga, H. W. B.; Withanage, N.
Human-elephant conflicts (HEC) have emerged as one of the main challenges that Sri Lanka faces throughout several decades. According to the official data of the Department of Wildlife Conservation (DWC), the number of elephant deaths is higher than the number of human deaths due to HEC per year. This research focused on the North Central Province, where the highest number of elephant deaths have been recorded. Hence, the objectives of this research are to identify the main factors that have affected the deaths of elephants and to identify suitable models to predict the causes of elephant deaths due to human-elephant conflict. Although there has been much research related to HEC worldwide, no published research studies were found in the literature that utilized advanced statistical techniques such as Multinomial Logistic Regression (MLR), LASSO regression, Decision Tree (DT), Support Vector Machine (SVM), and Probabilistic Neural Network (PNN) for their studies. However, this research will address that research gap by constructing models for classifying the causes of elephant deaths resulting from HEC. Data was collected from various departments, including DWC, the Department of Meteorology, and the crop calendar of the Department of Agriculture. Furthermore, Pearson's Chi-square and Fisher's exact tests were used to identify the association between the cause of death and influencing factors. Five variables, including the elephant age group, grass levels, gender, rainfall season, and place of death, were found to significantly influence the causes of death of an elephant. MLR and Data Mining (DM) techniques were initially utilized, but due to multicollinearity arising in MLR, the LASSO technique was employed as a remedial method. To overcome the class imbalanced problem, 90% of the data were randomly selected for model building while maintaining the class ratio of the response variable, and the remaining 10% of the data were used for testing. Performance measures, overall classification accuracy (OCA), and Misclassification Percentage of Critical Cases (MPCC) were used to evaluate and compare the classification potential of models. Models such as final MLR, LASSO, DT, SVM with Polynomial and Gaussian Kernels, and PNN with spread 0.801 illustrated 42.30%, 50%, 53.84%, 69.23%, 73.07%, and 73.07% of OCA. In addition, the above models showed 34.61%, 30.76%, 7.69%, 11.53%, 19.23%, and 26.92% MPCC respectively. Finally, the SVM model with Gaussian Kernel exhibited high OCA (73.07%) with 19.23% of MPCC as the better model since the PNN showed a high MPCC of about 26.92%. These findings will be helpful for authorities in their future and existing projects.