000			02183nam a22001937a 4500
003			OSt
005			20220107122852.0
008			190628b xxu||||| |||| 00| 0 eng d
040			_cIIITMK
100			_aAparna T M (92317006) _916006
245			_aSpatial modelling of soil nutrients using random forest algorithm
300			_aMSC GIS 2017-2019
500			_aMachine learning techniques for predictions are very common nowadays. But spatial predictions are not as common as normal prediction. Spatial predictions can be done by using different Machine learning algorithms. The study was proposed to predict spatial distribution of five soil nutrients based on the Random Forest algorithm.Random forest is a machine learning algorithm used here for spatial prediction. Spatial autocorrelation techniques are sometimes biased and this is suboptimal. This study presents a random forest for spatial predictions where buffer distances from observed spatial points that are used as explanatory variables. It incorporates geographical proximity effects into the prediction process. The result shows that random forest can obtain equally accurate and unbiased predictions as different versions of kriging. Advantages of using random forest over kriging are that it needs no rigid statistical assumptions about the distribution of the target variable. It gives more informative maps. It is less biased than kriging. Random forest framework for spatial prediction is more accurate because it trains an amount of data and prediction is done by using that trained data. There are some disadvantages also for random forest,its exponentially growing computational intensity with increase of calibration data and covariates. The high sensitivity of predictions to input data quality is also a disadvantage. Random forest’s success is because of its training data quality, especially its spatial data sampling quality.It minimizes extrapolation problems and any type of bias in data.
502			_bMSC GIS _c2017-2019 _dINT _eRadhakrishnan T
650			_aSPATIAL PREDICTION _916007
650			_aRANDOM FOREST ALGORITHM _916008
650			_aMACHINE LEARNING ALGORITHM _916009
942			_2ddc _cPR
999			_c6532 _d6532