Table 1.

List of previous studies that utilised different interpolation techniques for environmental studies, and the findings from each study are also provided. Comparing methods as done here cannot be generalized, as this is limited to the empirical exercises, and no rigorous theoretical demonstration is provided (the table is sorted based on the similarity of interpolation techniques adopted and compared in each study).

Process	Techniques	Findings	Ref.
Spatial rainfall mapping using IDW in the middle of Taiwan.	IDW	- IDW can be improved by the adjustment of the distance-decay parameter and the search radius.	[4]
		- Does not have significant interpolating ability for extreme values.
Compared to the developed CAR method with IDW for climate datasets in China.	IDW, CAR	- High-elevation data are an important factor for meteorological studies.	[5]
		- CAR performs slightly better than GIDW in terms of objective comparisons, especially in estimating local neighbouring patterns.
Comparing different techniques for precipitation and elevation.	IDW, AIDW, Kriging	- Varying the distance-decay parameter of IDW based on the spatial pattern can improve overall performance (AIDW).	[6]
		- AIDW can perform better than kriging in some cases.
Proposing regression-based IDW and comparing it with IDW and kriging.	IDW, RIDW, Kriging	- Integrating linear regression in IDW provides comparable objective evaluation to kriging and is computationally less demanding.	[7]
		- The confidence interval (CI) of RIDW also surpasses kriging CI.
Evaluating different interpolation techniques for air temperature data.	Spline, IDW, Kriging	- Kriging performs better overall, followed by IDW and spline.	[8]
Comparing different techniques for rainfall mapping in Sri Lanka.	IDW, TPS, OK, BK	- Interpolation results are very much dependent on the settings.	[9]
		- Different methods with different settings must be tested to define the suitable technique.
		- Bayesian kriging and splines performed best overall.
Assessing different interpolation methods to define the most suitable technique for the McArthur Forest Fire Danger Index (FFDI).	IDW, OK, RF, RFOK, RFIDW	- Combination of methods: RFOK and RFIDW shows the most promising results (least error).	[1]
		- Fire danger index is highly related to the behaviour of climate change, and should be considered carefully.
Investigated several techniques for depth to underground water in northwest China.	IDW, RBF, OK, SK, UK	- Simple kriging is the optimal method in terms of result consistency and the smallest prediction interval of 95%.	[10]
		- Depth of underground water increases significantly over the year because of excessive exploitation.
Ranking spatial interpolation techniques using GIS-based DSS.	Spline, IDW, Kriging, TP, TS	- No optimal technique that can accurately predict the rainfall.	[11]
		- Performance of each technique depends on the scale of the input data.
		- Kriging is the recommended technique, as it produces the most consistent results.
Using an interpolation method to construct a comprehensive archive of Australian climate data.	TPS, OK	- Different climate variables can be more accurately interpolated using different techniques due to the characteristic variability.	[3]