IPRC - 2021

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/24887

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Optimal assignment of unusable/ waste lands effectively using improved fuzzy assignment technique
(Faculty of Graduate Studies - University of Kelaniya, Sri Lanka, 2021) Hakmanage, N.M.; Jayasundara, D.D.M.; Chandrasekara, N.V.
Land resources are valuable for humans not only live but also conduct all of their economic activities on it. Allocation of land uses in a critical and optimal manner will pave the way for determining policies for the optimal utilization of land in a sustainable manner for the future, focusing on the uncertain conditions of each allocation. The objective of this study is to identify and propose effective allocations to abandoned lands such as unusable, waste and uncultivable lands using optimal land assignment plan. Fuzzy assignment technique accesses to explore how uncertainty in suitability index and the condition of the land will affect to optimal land allocation with the minimum allocation cost in this study. A major land-use classification system in Sri Lanka contains multiple levels of classification. Among them, land use categories regarded to the study area (farming village which has six unusable lands in Dompe divisional secretariat, Gampaha District) are selected as follows: Agriculture, Habitable or settled lands (Urban or rural areas), Forests, Wildlife, Reserves & Catchments areas, Underutilized Lands, Reservations (Reservoirs, Streams, & Irrigation Channels) and Barren lands. Major properties of the land were identified as land area: vaguely defined categories measured in square meters; Ownership: three possible sectors according to the ownership of the land as Private, Public and Other; Condition: discretional parameter that is vaguely defined with three possible values: bad (0), average (0.5) and good (1) and the Facilities: four different categories (power (P), water (W), communication (C), transportation (T)). Subsequently, the properties of each land and all possible demands were identified and a suitability index was developed using those vague parameters for each assignment of lands. With the aid of the Center of Gravity (COG) method, fuzzy values were converted to their crisp equivalents. Then the cost of assignment of each land for the aforementioned purposes, were considered using with linear, triangular, and trapezoidal fuzzy membership intervals. Thereafter, Robust ranking technique was applied to calculate the numerical values for the interval and obtain the product of suitability index and cost of allocation. Finally, using the Hungarian assignment algorithm, each land was assigned optimally for its effective purposes. The linear, triangular, and trapezoidal membership degrees, the minimum cost was obtained from the trapezoidal membership degree, that is 15% lower than the linear membership degree. Therefore, study proceeds with the trapezoidal membership degree. Using hypothetical assignment costs, six lands in the study area were assigned optimally for agriculture, habitable or settled lands, forests, wildlife, reserves and catchments areas, underutilized lands, reservations, and barren lands. This will be a great social and environmental service as it will involve the re-usage of the lands that are currently abandoned. Furthermore, the findings of this study can be extended nationally to save and maintain the land resource in an optimal manner.
Probability distributions in modelling the financial data: A literature review
(Faculty of Graduate Studies - University of Kelaniya, Sri Lanka, 2021) Basnayake, B.R.P.M.; Chandrasekara, N.V.
Many researchers analyze quantitative financial data such as stock prices, income, currency exchange rates, interest rates and many other financial data in their studies with the main aim of modelling and forecasting. It is a difficult process to identify the true behavior of financial data due to its chaotic nature. However, it is a mandatory requirement to examine the nature of these data as they have a direct impact on the lives of individuals, organizations and countries’ economic health conditions. A basic task in analyzing the financial data is to recognize a suitable statistical distribution of the data. For modelling financial data, one of the most common distributions applied in the literature is normal distribution. However, in the real world, most of these data are not normally distributed. Hence, the main purpose of this study is to demonstrate the selection of appropriate probability distributions for modelling financial data in a practical overview rather than relying on classic distributions. Overall, this literature review will convey a general idea to business practitioners and academic researchers in identifying suitable distributions in modelling the data. Several traditional financial models assume that the original data, returns or log-returns of the data follow normal, log-normal, exponential or beta distributions and the acceptance of this theory is widespread in practice. The main reason for this approach is the favorable properties of the distributions such as the existence of closed forms of probability density functions, easy and simple to estimate parameters for the data. Nevertheless, these distributions represent a limited number of distributional shapes and as a result, fail to identify the underlying characteristics of the data. Further, many studies evidenced that the financial data deviate from these classical distributions due to the skewness and heavy-tails (majority of the data in the tail) or fat-tails (more extreme values in the data) present in the data. There are flexible distributions such as the generalized lambda, normal inverse gaussian, Johnson translation system, the generalized beta family of distributions which were introduced to describe the diverse shapes of distributions. Another advantageous property of these distributions is that they can approximate some of the well-known distributions. Additionally, they can capture the uncertain movements of the financial data precisely. Other alternative distributions applied in the literature are stable, Tukey, power law, hyperbolic, skewed t and student t distributions and they are considered to have realistic and almost perfect fits for the data. Importantly, past studies provided more attention to the mixtures of normal distributions or compound normal distributions in fitting financial data as they have the ability to accommodate asymmetric and non-normal characteristics of empirical finance. Overall, there are several flexible distributions that can capture the true behavior of financial data. This study guides the researchers in selecting appropriate statistical distributions for the financial data rather than lying on classical standard distributions. Therefore, incorporating the accurate distribution in the financial models will provide more precise results and based on these results, government regulators, investors and businesses will be able to implement wise decisions.