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Enhancing sustainable development through Spatiotemporal analysis of Ramsar wetland sites in South Asia
(2024-12-01)
Goyal, Manish Kumar
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Rakkasagi, Shivukumar
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Surampalli, Rao Y.
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Zhang, Tian C.
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Erumalla, Saikumar
The ecological significance of wetlands makes it imperative to study changes in their inundation extent and propose necessary conservation measures. Monitoring wetland dynamics and implementing strategies to protect these essential ecosystems is crucial for maintaining the balance of natural systems. This study used pre-processed Landsat imagery (1991–2020) to generate yearly composites and produce inundation maps based on an automated Short-Wave Infrared thresholding technique within the Google Earth Engine platform. The analysis was executed on individual wetlands to describe their typical condition owing to regional climatic and geographical circumstances. The Mann-Kendall test was used to understand the trends in the change of inundation extent. The thresholding method achieved an overall accuracy of 89.0 %, with average dry and wet Producer's accuracies of 90.6 % and 86.6 %, respectively. The accuracy was higher for open water lakes compared to wetlands with complex vegetation dynamics. The trend analysis revealed that 46 sites follow an increasing trend, while the remaining 43 sites were found to be decreasing. Among these 43, 12 sites were found to be significantly decreasing, with the Upper Ganga River showing a maximum decrease of about 59 % in the inundation extent. Factors such as elevation, precipitation, temperature, and climate type were found to influence the trends in wetland inundation. Wetlands at high altitudes (>4000 m) and those receiving less than 500 mm of annual precipitation were more likely to exhibit decreasing trends. Coastal wetlands showed varying trends, with five increasing and three significantly increasing. The findings of this study provide valuable insights into the relationship between sustainable development and wetland conservation, supporting the Ramsar Convention's goals and the UN's Sustainable Development Goals. The individualized analysis of Ramsar sites enables the development of localized management strategies, climate change adaptation, and informed policy-making, ultimately contributing to the sustainable use of these critical ecosystems in South Asia.
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An Optimization Model for the Maximized Profit of Housing Development Project: A Case Study of Bangkok and Perimeter, Thailand
(2023-05-01)
Kakaew, Siripob
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U-tapao, Chalida
The objective of this research was to assess the profitability of the housing development project located in Bangkok, and the perimeter areas are Nonthaburi, Samut Prakan, and Pathum Thani, which are the result of good construction management. Moreover, the researchers’ previous data were used to predict the profit for construction planning, using stepwise multiple regression analysis. Then, we found the profit equation of the housing development project and applied it to the optimization model to clarify the model by the case study. The case study has 427 units, dividing the house into three types, with an optimization performed using the GAMS tool. In the same way, according to this optimization, it can be divided into two scenarios: scenario one found the maximized profit of the project to be 1053.91 MB, and scenario two was the result of making a sensitive analysis of scenario one and found the maximized profit of the project to be 1054.18 MB, which is the best scenario for the maximized profit of the project, and so it is the best choice for construction planning. The research contribution in this model can be applied to other projects that are suitable for large or densely populated cities, such as Beijing, New Delhi, and others. In the Conclusion section, we offer recommendations and detail future work that the researchers have discussed and described for the benefit of future research in housing development projects.
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Optimization-Based Train Timetables Generation with Demand Forecasting for Thailand High Speed Rail System
(2021-09-01)
Khwanpruk, Somkiat
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U-tapao, Chalida
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Khwanpruk, Kankanit
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Laokhongthavorn, Laemthong
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Suwannatrai, Arkom
In this paper, we propose a timetable optimizer (TO) consisting of the following parts: 1) Demand Forecasting Module 2) Train Optimization Module 3) Timetable Generator Module. TO is a specialized system for planning the train timetable which designed to help solve the problem of determining the number of trains that are suitable and scheduling the train suitably according to the number of trains designated by the system. TO integrates the passenger data from the latest round trip or historical information and forecasts a number of passengers of high-speed trains based on historical data in order to find the optimal number of trains by using a mixed integer programing model. Lastly, the TO system can applies the number of trains to calculate the appropriate train schedule so that the planning cycle is complete. A case study of high-speed railway system in Thailand with 36 trains to satisfy demand of 35,000 passengers/ each direction is conducted. The results show that the proposed system can quickly generate a timetable having an optimal number of train with suitable time interval according to a demand forecasting.
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A Study on High-Speed Rail Pricing Strategy for Thailand Based on Dynamic Optimal Pricing Model
(2021-06-30)
Khwanpruk, Somkiat
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U-tapao, Chalida
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Khwanpruk, Kankanit
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Laokhongthavorn, Laemthong
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Moryadee, Seksun
The Bangkok–Nong Khai high-speed railway is the first high-speed line in Thailand and is currently under construction. The project is due to be completed by 2023, with ticket prices starting at 80 baht plus 1.8 baht per km which can be considered as a straight-line fare set with a fixed rate increase. Different from the current price policy, this paper examines the application of dynamic pricing to Thailand’s HSR. Dynamic Pricing Optimizer proposed in this paper is a unique system of dynamic pricing by considering the changes in the amount of service requirements and the time of purchasing tickets designed to solve the problem of pricing that is appropriate. First, The Dynamic Pricing Optimizer (DPO) system will integrate the user data from the latest trip or historical information. After that, there will be a forecast for users of high-speed trains based on historical data in order to create a demand function that is linear. After that, the price will be determined by the optimization method to find the most suitable price in order to maximize the revenue. Lastly, we compare both fare policies and found that The Dynamic Pricing policy can increase revenue up to 6.5%.
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A Multi-Objective Optimization Model for Solid Waste Disposal Under Uncertainty: A Case Study of Bangkok, Thailand
(2017-03-01)
Laokhongthavorn, Laemthong
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U-tapao, Chalida
This paper has applied operation research to solid waste disposal by which two objective functions are optimized to minimize the expected operational costs (maximize revenues) and the expected net carbon dioxide equivalent (CDE) emissions. Types and uncertain amounts of solid wastes as well as costs of electricity were factored into the selection decision of solid waste disposal, i.e., landfill, incineration, composting and recycling. An optimization model was applied to the solid waste disposal of Bangkok, Thailand. In addition, a multi-objective optimization technique was proposed for a trade-off decision-making between minimum operational costs and CDE emissions. Composting and landfill are effective alternatives for Bangkok’s solid waste disposal system. The operational costs and net CDE emissions are highly correlated with the quantity of solid waste. Policy makers and plant operators could adopt the proposed optimization model under uncertainty in the selection of an optimal solid waste disposal.
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A stochastic, two-level optimization model for compressed natural gas infrastructure investments in wastewater management
(2016-01-01)
U-tapao, Chalida
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Moryadee, Seksun
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Gabriel, Steven A.
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Peot, Christopher
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Ramirez, Mark
In this paper, we present a stochastic two-level optimization model whose upper-level problem depicts a wastewater treatment plant deciding on the size of compressed natural gas (CNG) filling stations and their locations. These upper-level decisions are integrated with operational decisions for the plant as well as downstream markets including agriculture, CNG transportation, residential natural gas, and electricity markets at the lower level. The two-level problem, expressed as a stochastic mathematical program with equilibrium constraints (SMPEC), is reformulated as mixed-integer linear program (MILP) using SOS1 transformations and linearizations. As a case study, the SMPEC is used to evaluate the options for CNG investment for a wastewater treatment plant located in the Washington, DC metro area. Our results indicate that the CNG produced from the wastewater treatment plant could meet approximately 20% of the expected total transportation demand in Washington, DC. In addition, CNG produced from the wastewater treatment plant could reduce CO<inf>2</inf> emissions by a significant amount. The CNG benefits are traded off with less on-site wastewater-derived power production.

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