Inverse distance weighted interpolation method is a kind of spatial interpolation geometry method [12], which belongs to distance weight coefficient method series, the principle is to grant closer point greater weight than distant point.
 υe indicates variable value of test point, there e  = n j j j 1     Among them, j  (= 1, 2 ... n) is the variable value of point j (x j , y j ), j  is corresponding weight coefficient. Weight coefficient j  is calculated: j  = j 1 ej ej f d f d n   ( ) ( ) Among them, n is known point, f(d ej )is weight coefficient of distance d ej between interpolation 1 d ,
 Among them, b is appropriate constant, when b is 1 or 2, corresponding to distance reciprocal interpolation and distance reciprocal square interpolation. The method is simple and easy to operate, and it can provide rational interpolation results for **data** when its variable value varies widely, meaningless interpolation results won't appear as well. Shortages are: first of all, this method is very sensitive to the choice of weight function; Second, this method is greatly influenced by inhomogeneous distributed **data** points, when two or more sample points are adjacent, there is no processing to existing redundant information; Finally, inverse distance square has few forecast characteristics, global maximum and minimum variable values are scattered in the data.