Browsing by Author "Withanage, N."

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Factors affecting intimate partner violence against women in Sri Lanka
(Faculty of Science, University of Kelaniya, Sri Lanka, 2021) Wijekoon, H. M. S. D.; Withanage, N.
Intimate Partner Violence (IPV) against women has become a major health and social drawback in Sri Lanka that causes serious consequences for women’s mental and physical well-being as well as their reproductive health. The Sri Lanka Demographic and Health Survey (SLDHS) 2016 reported that a total of 17 percent of ever-married women age 15-49 in the country suffered domestic violence from their intim te partner. This study aimed to develop the Violence Index which measures the level of IPV experienced by a woman in Sri Lanka and to identify the factors that affect IPV against women in the country. A total of 2494 ever-married women age 15-49 who were found to be victims of IPV were selected from the database of SLDHS 2016 which was carried out by the Department of Census and Statistics in Sri Lanka. Characteristics of women, husbands, and their relationships that may influence IPV were considered as the explanatory variables while the estimated Violence Index was taken as the outcome variable. Multiple Correspondence Analysis and the theory of Composite Indicator were applied to develop the Violence Index. The association between the Violence Index and each explanatory variable was examined using the Kruskal-Wallis H test and the Mann-Whitney U test. Gamma regression analysis was carried out to determine the significant factors that affect IPV against women. Results show that the estimated Violence Index represented the lowest level of IPV experienced by a woman as 0.03 and the highest level of IPV experienced by a woman as 10.53. The values of the Violence Index showed a high right-skewed distribution. Religion, woman’s education level, woman’s married time, woman’s participation in making the household decisions, husband’s employment status, the age gap between husband and wife, enough money for daily expenses in the house, and household alcohol consumption were the most influential factors for IPV against women in Sri Lanka. The Gamma regression model provided a better prediction on the Violence Index with the mean squared error of 0.9447 and the mean absolute error of 0.5306. This study recommends raising public awareness on the risk and protective factors of IPV and the development of the current policies and new strategies to prevent IPV against women in Sri Lanka.
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.
A study on factors associated with child sexual abuse and recognizing the severity: Special reference to Galle district
(Faculty of Science, University of Kelaniya Sri Lanka, 2022) Dilshan, L. H. K.; Withanage, N.; Chandrasekara, N. V.
Child Sexual Abuse (CSA) has been a universal and social crisis with serious life-long consequences. One in four girls and one in six boys worldwide have experienced some form of sexual abuse in their childhood. According to Police statistics, CSA cases have been increasing rapidly in recent years in Sri Lanka. Galle is among the four districts where the reported child abuse cases are high, and the reported CSA complaints are rising drastically. Further, no previous study has been carried out in the Southern part of the island regarding the crisis of CSA. Therefore, the main objective of this study is to determine the key risk factors affecting the CSA cases in Galle Police Division and to develop suitable statistical and machine learning models to recognize the severity of CSA. All the 225 CSA cases reported to the Police Child and Women Bureau of Galle Police Division during the 2017 – 2020 period were considered for this study. The severity of CSA can be categorized into not fatal, child sexual exploitation, and fatal categories. Out of the twenty-one risk factors, which were found from the literature and knowledge of domain experts, sixteen factors showed a significant relationship with the severity of CSA at 10% significance level according to the chi-square test of association. These significant risk factors were area, child’s age, gender, whether mother lives with child, reason, the willingness of child, frequency of abuses, place of incident, relationship to the perpetrator, perpetrator’s age, education level of the perpetrator, perpetrator’s job, marriage status, whether the perpetrator has children, the number of children he has, and drug addiction of perpetrator. The Ordinal Logistic Regression (OLR) model was trained using a backward selection method with different data selection criteria. Next, the machine learning techniques: Decision Tree (DT), Support Vector Machine (SVM), and Probabilistic Neural Network (PNN) were employed to predict the severity of CSA. The random over-sampling technique was used to overcome the class imbalance problem that persists in the dataset. The bagging technique was implemented to preserve the robustness of the models and to improve their performance. The adequacy of the OLR model with the oversampling technique was examined and it was selected as the best model after considering the proportional odds assumption and analysis of deviance. The model classified the severity of CSA with 68.85% accuracy and area, gender, reason, frequency of abuses, place, perpetrator’s job, and whether the perpetrator has children can be identified as the significant predictors for CSA. The DT, SVM and PNN models classified the severity of CSA with an accuracy of 82.15%, 77.68% and 81.25%, respectively for the bagging technique. The PNN model performed better than the other fitted models with higher accuracy. The results obtained from this study can be used to get precautions and to arrange awareness sessions for parents and adults to reduce CSA in Galle Police Division. Similarly, the scope of the study can be extended to the whole island to reduce CSA and to make a better place for children.
The Variations of Spatial Distribution of the Schools in Colombo District: A GIS Based Approach
(In: Proceedings of the International Postgraduate Research Conference 2017 (IPRC – 2017), Faculty of Graduate Studies, University of Kelaniya, Sri Lanka., 2017) Withanage, N.
The education provides the life blood for human being. In Sri Lankan context Education Zone (EZ) is supervised the large numbers of schools in the specific area. Due to lack of application the Information Communication Technology (ICT) in education many problems can be identified in school management process. Especially there is a high potentiality to apply Geographic Information Systems (GIS) for education management and administration. Even most of the schools do not cover the GIS part in their curriculum. Now a day‘s popular schools gained more competition in the process of grade one student selection. Thus the schools used manual distance calculation system using hard copy of the area map, no common computerized system. And also there is a contrastive difference between Colombo urban schools and schools in the outskirt of Colombo. Within the Colombo metropolitan area the number of 1AB schools are higher than the other areas of the country as well as the outer core of Colombo. There are 36 National Schools and totally 405 government schools are situated in the district of Colombo. Moreover 16 out of 36 National schools are located in the Colombo metropolitan area. The study has been devoted to discuss what factors have been contributed towards the variations in spatial distribution of schools in the Colombo District. Identifying the spatial variation of school distribution in the district and determining the school catchment areas were among specific objectives. The school system can be used the GIS for distance calculation within the specific proximity area. In the study different GIS techniques like proximity analysis and Thiessen Polygon tools were being facilitated to identify threshold within a school clusters. This study tried to identify schools clusters by using Spatial Autocorrelation and Multi-Distance Spatial Cluster Analysis (Ripley's K Function) tools. The study had proven the schools in Colombo District are spatially distributed as clusters.According to the analysis main school clusters located in the western coastal area which having high density of schools. Excluded the core schools in clusters are being changed the level of schools. The results will be able to imply in the decision making in bottom level to top level especially in education zone.