ORCID Profile
0000-0002-6037-4974
Current Organisation
Chiang Mai University
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Publisher: MDPI AG
Date: 31-01-2023
DOI: 10.3390/SU15032566
Abstract: Trees and shrubs, as a part of the green infrastructure, are important for the well-being of urban dwellers. This research aims to study the eco-efficiency of trees on outdoor thermal comfort, defined by the ratio of the change in the physiological equivalent temperature (PET) and the trees’ cost. Field data were collected and compared with station data to calibrate and create a base case model. After the base case model was created, the researchers created case scenarios with shrubs and trees occupying 25, 50, 75, and 100% of the space. The cost estimate was calculated by the price quotes from local providers. The results suggested that (1) trees and shrubs were confirmed to positively improve thermal comfort, especially in the late afternoon when it is the most uncomfortable, (2) adding more trees to the study site could increase the eco-efficiency values more than shrubs in all cases, and (3) adding trees at 50% coverage gave the highest eco-efficiency compared to the other options. The results of this exploratory study will provide an alternative design approach that helps in decision-making for outdoor spaces. Future studies should address plant selections and other ecosystem benefits that may affect the eco-efficiency calculation.
Publisher: Penerbit UTM Press
Date: 27-08-2020
Abstract: The purpose of this paper is to determine whether the property price is caused by the sub ision neighbourhood designs in the Bangkok Metropolitan Region (BMR), Thailand. A total price model is developed during the analysis process. The model provides a greater understanding of the significance of the sub ision neighbourhood designs that are related to property pricing. This paper is based on data collection from 50 sub isions across the BMR area. The hedonic pricing approach is used to develop the models. The semi-log models are developed on 1,182 s les of property sales located in eight zones of the BMR. The independent variables include general bundles of property characteristics and the sub ision neighbourhood design items. There are two major findings in this study. First, this study provides a suitable property price model for sub ision development in the BMR. The model presents the high level of R2 at 0.948. The model confirms that all classical hedonic variables are statistically significant to the property price. Furthermore, the additional alternative variables for the sub ision neighbourhood design items can improve the level of variation explained by the model. Second, this study finds that the average property price attributable to the sub ision neighbourhood design is about 20.24 % of the total property price. The components of the sub ision neighbourhood design items consist of project characteristics, recreation features, social facilities, and transportation system design. The model should support knowledge of the design’s impact on the property price for the Government or policy makers on making appropriate policies for urban and environmental management. The model provides a guideline for developers on appropriate property selling-prices for sub ision development in the BMR. The new understanding of the property price attributable to the sub ision neighbourhood designs support suitable decision making on new sub ision development in the BMR
Publisher: MDPI AG
Date: 26-01-2022
DOI: 10.3390/INFRASTRUCTURES7020017
Abstract: Landslide incidents frequently occur in the upper northern region of Thailand due to its topography, which is mostly mountainous with high slopes. In the past, when landslides happened in this area, they affected traffic accessibility for rescue and evacuation. For this reason, if the risk of landslides could be evaluated, it would help in the planning of preventive measures to mitigate the damage. This study was carried out to create and develop a risk estimation model using the artificial neural network (ANN) technique for landslides at the edge of the roadside, by collecting field data on past landslides in the study areas in Chiang Rai and Chiang Mai Provinces. A total of 9602 data points were collected. The variables for forecasting were: (1) land cover, (2) physiographic features, (3) slope angle, and (4) five-day cumulative rainfall. Two hidden layers were used to create the model. The number of nodes in the first and second hidden layers were five and one, respectively, which were derived from a total of 25 trials, and the highest accuracy achieved was 96.74%. When applying the model, a graph demonstrating the relationship between the landslide risk, rainfall, and the slopes of the road areas was obtained. The results show that high slopes result in more landslides than low slopes, and that rainfall is a major trigger for landslides on roads. The outcomes of the study could be used to create risk maps and provide information for developing warnings for high-slope mountain roads in the upper northern region of Thailand.
Publisher: MDPI AG
Date: 28-09-2022
DOI: 10.3390/IJGI11100507
Abstract: Diversity in economic activity can be found at different spatial scales in cities’ urban morphology. Spatial capital is defined as the area’s physical appearance, which is important for enhancing economic activities in urban areas. It addresses how urban form, as a result of urban design, influences urban life—that is, how it supports and creates the potential for variations of urbanity and spatial ersity. The aims of this study are (i) to measure the economic ersity based on Simpson’s ersity index by using points of interest (POI) data, which can reflect economic activity functions in the tourist city of Surat Thani, which is mainly used as a jumping off point for land travel to other islands off the east coast of Thailand (ii) to explore the space syntax to measure the values of urban morphology by integrations with DepthMapX Software and (iii) to investigate the relationship between measures of the degree of spatial morphology configuration and patterns of spatial ersity of economic activities using the Pearson’s correlation coefficient. The study found that measuring the values of urban morphology can generate variations in spatial accessibility that are positively related to the variety of economic ersity, especially in terms of the availability of convenience stores, shops, and bank branches. This research is beneficial to planners in identifying important economic areas of the city, whose complex spatial interactions between commerce and urban morphology influence the current demand for economic space.
Publisher: Univ. of Malaya
Date: 31-08-2021
Abstract: A lower external temperature increases comfort and reduces the chance of heat stress it can be impacted by the density of the urban area, and this is an important issue for the residents in housing estate developments. Therefore, to sustainably reduce this issue, the external temperature is important to manage for urban public spaces’ development. This article reports the results of studies on increasing thermal comfort in public areas by adding different types of shading into computer programs, Rhinoceros and Grasshopper, to calculate the Universal Thermal Comfort Index (UTCI). Increasing the outdoor comfort can be done by adding shaded areas via large trees that can result in thermal reduction and humidity increase, but they do not obstruct air circulation. The result can be used as a guideline for the design of public spaces in housing estates to meet the outdoor comfort efficiently and support the users’ expectations.
Publisher: International Community of Spatial Planning and Sustainable Development
Date: 15-07-2021
Publisher: MDPI AG
Date: 10-07-2022
DOI: 10.3390/S22145161
Abstract: Underwater fish monitoring is the one of the most challenging problems for efficiently feeding and harvesting fish, while still being environmentally friendly. The proposed 2D computer vision method is aimed at non-intrusively estimating the weight of Tilapia fish in turbid water environments. Additionally, the proposed method avoids the issue of using high-cost stereo cameras and instead uses only a low-cost video camera to observe the underwater life through a single channel recording. An in-house curated Tilapia-image dataset and Tilapia-file dataset with various ages of Tilapia are used. The proposed method consists of a Tilapia detection step and Tilapia weight-estimation step. A Mask Recurrent-Convolutional Neural Network model is first trained for detecting and extracting the image dimensions (i.e., in terms of image pixels) of the fish. Secondly, is the Tilapia weight-estimation step, wherein the proposed method estimates the depth of the fish in the tanks and then converts the Tilapia's extracted image dimensions from pixels to centimeters. Subsequently, the Tilapia's weight is estimated by a trained model based on regression learning. Linear regression, random forest regression, and support vector regression have been developed to determine the best models for weight estimation. The achieved experimental results have demonstrated that the proposed method yields a Mean Absolute Error of 42.54 g, R2 of 0.70, and an average weight error of 30.30 (±23.09) grams in a turbid water environment, respectively, which show the practicality of the proposed framework.
Publisher: Elsevier BV
Date: 11-2022
Publisher: MDPI AG
Date: 05-07-2021
DOI: 10.3390/S21134620
Abstract: Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk in Chiang Rai, Thailand. The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). The new bidirectional long short-term memory (Bi-LSTM) algorithm is presented to forecast landslides. The proposed algorithm consists of 3 major steps, the first of which is the construction of a landslide dataset by using Quantum GIS (QGIS). The second step is to generate the landslide-risk model based on machine learning approaches. Finally, the automated landslide-risk visualization illustrates the likelihood of landslide via Google Maps on the website. Four static factors are considered for landslide-risk prediction, namely, land cover, soil properties, elevation and slope, and a single dynamic factor i.e., precipitation. Data are collected to construct a geospatial landslide database which comprises three historical landslide locations—Phu Chifa at Thoeng District, Ban Pha Duea at Mae Salong Nai, and Mai Salong Nok in Mae Fa Luang District, Chiang Rai, Thailand. Data collection is achieved using QGIS software to interpolate contour, elevation, slope degree and land cover from the Google satellite images, aerial and site survey photographs while the physiographic and rock type are on-site surveyed by experts. The state-of-the-art machine learning models have been trained i.e., linear regression (LR), artificial neural network (ANN), LSTM, and Bi-LSTM. Ablation studies have been conducted to determine the optimal parameters setting for each model. An enhancement method based on two-stage classifications has been presented to improve the landslide prediction of LSTM and Bi-LSTM models. The landslide-risk prediction performances of these models are subsequently evaluated using real-time dataset and it is shown that Bi-LSTM with Random Forest (Bi-LSTM-RF) yields the best prediction performance. Bi-LSTM-RF model has improved the landslide-risk predicting performance over LR, ANNs, LSTM, and Bi-LSTM in terms of the area under the receiver characteristic operator (AUC) scores by 0.42, 0.27, 0.46, and 0.47, respectively. Finally, an automated web GIS has been developed and it consists of software components including the trained models, rainfall API, Google API, and geodatabase. All components have been interfaced together via JavaScript and Node.js tool.
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier BV
Date: 09-2023
Publisher: Trans Tech Publications, Ltd.
Date: 05-2020
DOI: 10.4028/WWW.SCIENTIFIC.NET/KEM.841.171
Abstract: To be more sustainable in the road construction industry, the rock-based geopolymer concept should be applied with an aim to create a geopolymer-based road structural layer. The research program studied on the geopolymer for road construction was newly established at Chiang Mai University, Thailand. This study concentrated in a preliminarily evaluation of strength performance and compaction characteristics of crushed rock-based geopolymer (CR-GP) to partially or totally replace the usage of ordinary Portland cement (OPC) as a road stabilizing agent. The standard crushed rock (CR), complying with the standard of road base materials, was obtained from a real construction field. The experiment on CR gradation, compaction and compressive strength were carried out. The results showed that CR of a finer grading curve with higher surface areas tended to better react with alkaline activators, resulting in relatively high compressive strength. The mechanical modification with compaction is one of the simplest methods for strength improvement. It found that higher compactive efforts (the modified compaction), higher densification than that of the standard compaction, corresponding to the compaction theory of soil mechanics. CR-GP having an ideal (reconstituted) grading curve achieved higher compressive strength than that of the standard grading one of a well-graded pattern. Overall, it could be concluded CR-GP has intrinsic compaction characteristics of which at its optimum point of compaction, CR-GP could address the minimum requirement for road standard in terms of compressive strength, by which it could be used as an alternative material in replacing the consumption of OPC.
Publisher: Trans Tech Publications, Ltd.
Date: 05-2020
DOI: 10.4028/WWW.SCIENTIFIC.NET/KEM.841.161
Abstract: At present, our world is facing environmental issues, which is an increasing amount of carbon dioxide (CO 2 ) generated by the Portland cement (OPC) production. To reduce that carbon dioxide emissions, some researchers have studied the alternative cementitious materials to replace the consumption of OPC, and geopolymer is one of the choices. Geopolymer cement (GP), a green technique for construction material, was applied for the road constructions by using Crushed rock (CR-the typical pavement material) as a starting material of geopolymer synthesis. The results showed that the optimum mixture to achieve both properties and economic aspect was 5M of NaOH concentration, 1.0 SS/SH ratio, 0.5 L/B ratio cured at room temperature. With that mixture, it passed the target compressive strength of Cement-treated base (CTB) for pavement (2.1-5.5 MPa) as well as achieving the CO 2 reduction of 45.23% when compared to ordinary Portland cement.
Publisher: Elsevier BV
Date: 11-2022
Publisher: Elsevier BV
Date: 10-2023
No related grants have been discovered for Damrongsak Rinchumphu.