Truncation errors occur when data is too long for the column it is being inserted into. This can lead to data loss and inaccurate results. To avoid this, you can add a dummy row with data longer than 255 characters in the column that is causing the truncation error. You can also check the data type of the column in both the source and destination, and declare all columns as varchar with a maximum length.
Additionally, you can use the ANSI_WARNINGS option to avoid displaying an error message and automatically truncating the data to the length of the target column. Truncation errors are also defined as errors that result from using an approximation rather than an exact mathematical procedure. This means that a high truncation error is an indicator that the original process LCA model is incomplete. To reduce this error, you can use a hybrid LCA to replace the process LCA when comparing different alternatives.
Additionally, you can use a tolerance as a constraint on the size of the truncation error. The “truncation error T (x, h) associated with the “corrective stage” of the “Milne—Hamming” method is given by. This term originates from the fact that numerical methods can be compared with a truncated Taylor series. The main term of the truncation error represents a fourth-order dissipative process that complements the physical dissipation of the diffusion equation.
For one-step methods, the local truncation error gives us a measure to determine how the solution to the differential equation does not solve the difference equation.
