• Title/Summary/Keyword: Life Weather Index

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County-Based Vulnerability Evaluation to Agricultural Drought Using Principal Component Analysis - The case of Gyeonggi-do - (주성분 분석법을 이용한 시군단위별 농업가뭄에 대한 취약성 분석에 관한 연구 - 경기도를 중심으로 -)

  • Jang, Min-Won
    • Journal of Korean Society of Rural Planning
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    • v.12 no.1 s.30
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    • pp.37-48
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    • 2006
  • The objectives of this study were to develop an evaluation method of regional vulnerability to agricultural drought and to classify the vulnerability patterns. In order to test the method, 24 city or county areas of Gyeonggi-do were chose. First, statistic data and digital maps referred for agricultural drought were defined, and the input data of 31 items were set up from 5 categories: land use factor, water resource factor, climate factor, topographic and soil factor, and agricultural production foundation factor. Second, for simplification of the factors, principal component analysis was carried out, and eventually 4 principal components which explain about 80.8% of total variance were extracted. Each of the principal components was explained into the vulnerability components of scale factor, geographical factor, weather factor and agricultural production foundation factor. Next, DVIP (Drought Vulnerability Index for Paddy), was calculated using factor scores from principal components. Last, by means of statistical cluster analysis on the DVIP, the study area was classified as 5 patterns from A to E. The cluster A corresponds to the area where the agricultural industry is insignificant and the agricultural foundation is little equipped, and the cluster B includes typical agricultural areas where the cultivation areas are large but irrigation facilities are still insufficient. As for the cluster C, the corresponding areas are vulnerable to the climate change, and the D cluster applies to the area with extensive forests and high elevation farmlands. The last cluster I indicates the areas where the farmlands are small but most of them are irrigated as much.

An Application of Machine Learning in Retail for Demand Forecasting

  • Muhammad Umer Farooq;Mustafa Latif;Waseemullah;Mirza Adnan Baig;Muhammad Ali Akhtar;Nuzhat Sana
    • International Journal of Computer Science & Network Security
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    • v.23 no.9
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    • pp.1-7
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    • 2023
  • Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

An Application of Machine Learning in Retail for Demand Forecasting

  • Muhammad Umer Farooq;Mustafa Latif;Waseem;Mirza Adnan Baig;Muhammad Ali Akhtar;Nuzhat Sana
    • International Journal of Computer Science & Network Security
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    • v.23 no.8
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    • pp.210-216
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    • 2023
  • Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

Structural Shape Estimation Based on 3D LiDAR Scanning Method for On-site Safety Diagnostic of Plastic Greenhouse (비닐 온실의 현장 안전진단을 위한 3차원 LiDAR 스캔 기법 기반 구조 형상 추정)

  • Seo, Byung-hun;Lee, Sangik;Lee, Jonghyuk;Kim, Dongsu;Kim, Dongwoo;Jo, Yerim;Kim, Yuyong;Lee, Jeongmin;Choi, Won
    • Journal of The Korean Society of Agricultural Engineers
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    • v.66 no.5
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    • pp.1-13
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    • 2024
  • In this study, we applied an on-site diagnostic method for estimating the structural safety of a plastic greenhouse. A three-dimensional light detection and ranging (3D LiDAR) sensor was used to scan the greenhouse to extract point cloud data (PCD). Differential thresholds of the color index were applied to the partitions of raw PCD to separate steel frames from plastic films. Additionally, the K-means algorithm was used to convert the steel frame PCD into the nodes of unit members. These nodes were subsequently transformed into structural shape data. To verify greenhouse shape reproducibility, the member lengths of the scan and blueprint models were compared with the measurements along the X-, Y-, and Z-axes. The error of the scan model was accurate at 2%-3%, whereas the error of the blueprint model was 5.4%. At a maximum snow depth of 0.5 m, the scan model revealed asymmetric horizontal deflection and extreme bending stress, which indicated that even minor shape irregularities could result in critical failures in extreme weather. The safety factor for bending stress in the scan model was 18.7% lower than that in the blueprint model. This phenomenon indicated that precise shape estimation is crucial for safety diagnostic. Future studies should focus on the development of an automated process based on supervised learning to ensure the widespread adoption of greenhouse safety diagnostics.

Study on Control of Thermal Environmental Factors for Improvement of Productivity of Laying Hens in Summer (여름철 산란계사 내 열환경인자 중 제어요소에 관한 연구)

  • Kim, Seong-Wan;Lee, Tae-Hoon;Cha, Gwang-Jun;Gutierrez, Winson M.;Chang, Hong-Hee
    • Journal of agriculture & life science
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    • v.53 no.2
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    • pp.121-129
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    • 2019
  • This study carried out to determine control factors for the improvement of productivity of laying hens suffering heat stress during hot weather. A total of 48,451 ISA Brown layers were housed in a farm located in Gyeongsangnam-do, Republic of Korea. Five thermo-hydrometer loggers were installed inside the house to collect data of dry-bulb temperature and relative humidity. The experiment continued for 81 days when the summer season begins from 19th June to 7th September, 2018. This study analyzed the correlations among layers' production index and daily average, highest, and lowest temperature; daily average, highest, and lowest relative humidity; and daily average, minimum, and maximum THI. The result indicated that feed consumption, hen-day egg production, egg weight, and FCR decreased as the daily average, highest and lowest dry-bulb temperature and THI rise (p<0.01). On the other hand, water intake increased as the daily average, highest and lowest dry-bulb temperature and THI rise (p<0.001). The relative humidity was not considered to have direct correlations to the layers' production index (p>0.05). However, it was noticeable that the mortality did not have significant relations with daily average and highest temperature; THI; or daily average, highest and lowest relative humidity while it was relevant to the daily lowest temperature and THI (p<0.05). In conclusion, to enhance the productivity of laying hens in a hot climate, it is recommended that daily average, highest, and lowest dry-bulb temperature and THI are maintained as low as possible. Especially, the daily lowest temperature is needed to lower to 20℃, which is the lowest critical temperature for layers.

Analysis of Industrial Linkage Effects for Farm Land Base Development Project -With respect to the Hwangrak Benefited Area with Reservoir - (농업생산기반 정비사업의 산업연관효과분석 -황락 저수지지구를 중심으로-)

  • Lim, Jae Hwan;Han, Seok Ho
    • Korean Journal of Agricultural Science
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    • v.26 no.2
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    • pp.77-93
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    • 1999
  • This study is aiming at identifying the foreward and backward lingkage effects of the farm land base development project. Korean Government has continuously carried out farmland base development projets including the integrated agricultural development projects. large and medium scale irrigation projects and the comprehensive development of the four big river basin including tidal land reclamation and estuary dam construction for the all weather farming since 1962. the starting year of the five year economic development plans. Consequently the irrigation rate of paddy fields in Korea reached to 75% in 1998 and to escalate the irrigation rate, the Government had procured heavy investment fund from IBRD. IMF and OECF etc. To cope with the agricultural problems like trade liberalization in accordance with WTO policy, the government has tried to solve such problems as new farmland base development policy, preservation of the farmland and expansion of farmland to meet self-sufficiency of foods in the future. Especially, farmland base development projects have been challanged to environmental and ecological problems in evaluating economic benefits and costs where the value of non-market goods have not been included in those. Up to data, in evaluating benefits and costs of the projects, farmland base development projects have been confined to direct incremental value of farm products and it's related costs. Therefore the projects'efficiency as a decision making criteria has shown the low level of economic efficiencies. In estimating economic efficiencies including Leontiefs input-output analysis of the projects could not be founded in Korea at present. Accordingly this study is aimed at achieving and identifying the following objectives. (1) To identify the problems related to the financial supports of the Government in implementing the proposed projects. (2) To estimated backward and foreward linkage effects of the proposed project from the view point of national economy as a whole. To achieve the objectives, Hwangrak benefited area with reservoir which is located in Seosan-haemi Disticts, Chungnam Province were selected as a case study. The main results of the study are summarized as follows : a. The present value of investment and O & M cost were amounted to 3,510million won and the present value of the value added in related industries was estimated at 5.913million won for the period of economic life of 70 years. b. The total discounted value of farm products in the concerned industries derived by the project was estimated at 10,495million won and the foreward and backward linkage effects of the project were amounted to 6,760 and 5,126million won respectively. c. The total number of employment opportunities derived from the related industries for the period of project life were 3,136 man/year. d. Farmland base development projects were showed that the backward linkage effects estimated by index of the sensitivity dispersion were larger than the forward linkage effect estimated by index of the power of dispersion. On the other hand, the forward linkage effect of rice production value during project life was larger than the backward linkage effect e. The rate of creation of new job opportunity by means of implementing civil engineering works were shown high in itself rather than any other fields. and the linkage effects of production of the project investment were mainly derived from the metal and non-metal fields. f. According to the industrial linkage effect analysis, farmland base development projects were identified economically feasible from the view point of national economy as a whole even though the economic efficiencies of the project was outstandingly decreased owing to delaying construction period and increasing project costs.

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