Analysis and prediction of daily climate variables in central Punjab using machine learning and geospatial techniques
| dc.contributor.advisor | Kingra, P.K. | |
| dc.contributor.author | Bora, Sony | |
| dc.date.accessioned | 2020-11-02T04:52:18Z | |
| dc.date.available | 2020-11-02T04:52:18Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | The climate science community faces the significant challenge of dealing with a continuously changing observing system. Huge datasets offer a high degree of statistical power coupled with untapped opportunities for data miners. The advent of machine learning has made data mining facile by providing algorithms that autonomously identify patterns with minimal human interference. For the trend analysis, the Sen’s slope for daily maximum air temperature, daily minimum air temperature, daily maximum soil temperature, daily minimum soil temperature, daily morning relative humidity, daily evening relative humidity, daily open pan evaporation and daily sunshine hours of all the months for 50 years, from 1970 to 2019 were calculated and observed highest slope magnitude on 20th March (0.093 oC/year), 18th May (0.127 oC/year), 15th April (0.167 oC/year), 2nd January (0.061 oC/year), 14th May (0.345 percent/year), 18th January (0.789 percent/year), 2nd May (-0.104 mm/year) and 2nd January (-0.159 hr/year) respectively. It concluded that the variability in maximum and minimum air temperature will cause decrease in yield by 7.17% (405 Kg/ha) and 4.86% (262 Kg/ha) respectively by 2050. To calculate LST the archive satellite data of Landsat-5 TM for the years 1994, 1996, 1998, 2008, 2010, Landsat -7 ETM+ for the year 2000, 2002 and Landsat -8 for the year 2014, 2016, 2018 were used. The air and soil temperature can be predicted from LST by using equation Ta = 0.775 Ts and TS = 0.884 Ts respectively (where Ta is air temperature, TS is soil temperature and Ts is land surface temperature). A good correlation was observed between LST and AET for maize (R² = 0.856) and wheat (R² = 0.897). The big climate data was mined using hierarchical clustering. Decadal comparison from 1970 – 2019 was carried out between mean maximum and minimum, soil and air temperature and pan evapotranspiration with soil and air temperature using Regression analysis technique wherein, a linear and an exponential relationship was observed respectively. | en_US |
| dc.identifier.citation | Bora, Sony (2020). Analysis and prediction of daily climate variables in central Punjab using machine learning and geospatial techniques (Unpublished M.Sc. thesis). Punjab Agricultural University, Ludhiana, Punjab, India. | en_US |
| dc.identifier.uri | https://krishikosh.egranth.ac.in/handle/1/5810154079 | |
| dc.keywords | Sen’s slope, LST, big climate data, mined, hierarchical clustering | en_US |
| dc.language.iso | English | en_US |
| dc.pages | 103 | en_US |
| dc.publisher | Punjab Agricultural University, Ludhiana | en_US |
| dc.research.problem | Analysis and prediction of daily climate variables in central Punjab using machine learning and geospatial techniques | en_US |
| dc.sub | Agricultural Meteorology | en_US |
| dc.theme | Analysis and prediction of daily climate variables in central Punjab using machine learning and geospatial techniques | en_US |
| dc.these.type | M.Sc | en_US |
| dc.title | Analysis and prediction of daily climate variables in central Punjab using machine learning and geospatial techniques | en_US |
| dc.type | Thesis | en_US |