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@article{ART002516901},
author={wan-sik an},
title={A Study on Machine Learning-based Grass Demand Forecasting},
journal={Journal of Knowledge Information Technology and Systems},
issn={1975-7700},
year={2019},
volume={14},
number={5},
pages={535-542},
doi={10.34163/jkits.2019.14.5.010}

TY - JOUR
AU - wan-sik an
TI - A Study on Machine Learning-based Grass Demand Forecasting
JO - Journal of Knowledge Information Technology and Systems
PY - 2019
VL - 14
IS - 5
PB - Korea Knowledge Information Technology Society
SP - 535
EP - 542
SN - 1975-7700
AB - The origin of the grass varies between regions and countries, but in the West, crops, which have been widely used for feed, have long been adapted to livestock grazing, and are derived from plants and perennials with good land cover capacity. In Korea, the origin of grass is different from that of the West. It has been used to decorate and cover the tomb's ground. Thus, Grass is one of the essential elements in our life. Grass is the major resource of various ecosystems and it also provides a space to relax. Nevertheless, Recently, Korea is recognized as a recession due to the reduction of new golf course construction and the slowdown of construction industry. However, since the 5-day system was implemented due to economic development and national income improvement after the Olympic and World Cup, the demand for grass as a green space for recreation and sports Is increasing. In particular, the use of new towns, the West Coast Saemangeum project, neighborhood parks, school grounds, and general residential gardens is increasing. Grass is expected to increase the value added of social indirect capital such as highways, the increase of golf population, the greening of urban and national lands using grass such as the increase of recreational activities and urban grass parks. In addition, the grass industry is a comprehensive field that includes the development, production, composition and management of garden, slope and sports. However, the grass industry is limited to production. This situation is lacking, and there is also a lack of basic data on the system or industry that can support the grass industry. Accordingly, we are necessary to have a research how we improve to use of grass and suggest newly methods with water demand.
KW - Machine Learning;Water demand;Forecasting;Irrigation Schedule
DO - 10.34163/jkits.2019.14.5.010
ER -

wan-sik an. (2019). A Study on Machine Learning-based Grass Demand Forecasting. Journal of Knowledge Information Technology and Systems, 14(5), 535-542.

wan-sik an. 2019, "A Study on Machine Learning-based Grass Demand Forecasting", Journal of Knowledge Information Technology and Systems, vol.14, no.5 pp.535-542. Available from: doi:10.34163/jkits.2019.14.5.010

wan-sik an "A Study on Machine Learning-based Grass Demand Forecasting" Journal of Knowledge Information Technology and Systems 14.5 pp.535-542 (2019) : 535.

wan-sik an. A Study on Machine Learning-based Grass Demand Forecasting. 2019; 14(5), 535-542. Available from: doi:10.34163/jkits.2019.14.5.010

wan-sik an. "A Study on Machine Learning-based Grass Demand Forecasting" Journal of Knowledge Information Technology and Systems 14, no.5 (2019) : 535-542.doi: 10.34163/jkits.2019.14.5.010

wan-sik an. A Study on Machine Learning-based Grass Demand Forecasting. Journal of Knowledge Information Technology and Systems, 14(5), 535-542. doi: 10.34163/jkits.2019.14.5.010

wan-sik an. A Study on Machine Learning-based Grass Demand Forecasting. Journal of Knowledge Information Technology and Systems. 2019; 14(5) 535-542. doi: 10.34163/jkits.2019.14.5.010

wan-sik an. A Study on Machine Learning-based Grass Demand Forecasting. 2019; 14(5), 535-542. Available from: doi:10.34163/jkits.2019.14.5.010

wan-sik an. "A Study on Machine Learning-based Grass Demand Forecasting" Journal of Knowledge Information Technology and Systems 14, no.5 (2019) : 535-542.doi: 10.34163/jkits.2019.14.5.010