Ation techniques. The values of 4 error indicators are distinguished in colour degree–light blue indicates a bigger worth, dark blue indicates a smaller sized value. The smaller sized the error indicator, the superior the interpolation method as well as the greater the accuracy in estimating the spatial patterns of precipitation. All round, interpolation models estimate the spatial patterns of precipitation to a affordable degree; on the other hand, outliers seem at some stations. For example, meteorological station 15 has the biggest estimation error, followed by meteorological station 18. The estimation anomaly for a certain spatial place may possibly be attributed to the complicated climate variability [38] triggered by the large elevation variations [45] in Chongqing, which could influence the efficiency of interpolation strategy [33]. 4.4. Complete Ranking by Entropy-Weighted TOPSIS To identify the optimal approach for estimating spatial precipitation patterns in Chongqing, Entropy-Weighted TOPSIS was adopted to quantize and rank the functionality of six interpolation strategies. Determined by the performance evaluation indices (MSE, MAE, MAPE, SMAPE, NSE), the six interpolation procedures are ranked with regards to their efficiency in estimating spatial patterns below distinctive rainfall magnitudes and integrated various rainfall magnitudes. Initial, the indicators are standardized, where MSE, MAE, MAPE, SMAPE are negative indices and NSE is actually a good indicator. Depending on weighting benefits of entropy process, the distance in between good and unfavorable excellent options of each approach is calculated to determine the comparatively proximity (C-value) to the best resolution, and lastly the Fmoc-Ile-OH-15N custom synthesis C-value is ranked to qualitatively evaluate the performance of six methods in estimating the spatial pattern of precipitation in Chongqing under different climatic conditions. The calculation benefits of TOPSIS evaluation are shown in Table two. As outlined by TOPSIS evaluation, KIB may be the optimum interpolation process below the imply annual precipitation pattern, with the comparative proximity (C-value) the highest at 0.964, followed by EBK. RBF is the optimal process within the rainy-season precipitation pattern, together with the C-value the highest at 0.978, followed by KIB. KIB was the optimal strategy in the dry-season precipitation pattern, with the C-value the highest at 1, followed by OK. IDW was the worst process in the all precipitation patterns, with the C-value was the lowest to 0 without the need of exception.Table 2. TOPSIS superiority ranking of six spatial interpolation strategies based on both distinctive rainfall magnitudes and integrated a number of rainfall magnitudes. Methods with superior efficiency are shown in bold.System KIB EBK OK RBF DIB IDW RBF KIB EBK OK DIB IDWPositive Distance (D) 0.016 0.083 0.155 0.18 0.191 0.448 0.01 0.046 0.06 0.104 0.238 0.Negative Distance (D-) 0.441 0.374 0.311 0.269 0.265 0 0.442 0.41 0.401 0.353 0.214Comparatively Proximity (C) 0.964 0.818 0.667 0.six 0.581 0 0.978 0.899 0.87 0.773 0.474Sort Result 1 two three 4 5 6 1 two 3 4 5Mean AnnualRainy SeasonAtmosphere 2021, 12,20 ofTable two. Cont.Method KIB OK EBK DIB RBF IDW KIB EBK OK RBF DIB IDWPositive Distance (D) 0 0.063 0.073 0.189 0.213 0.447 0.024 0.07 0.126 0.127 0.241 0.Damaging Distance (D-) 0.447 0.386 0.375 0.27 0.238 0 0.49 0.44 0.379 0.373 0.265Comparatively Proximity (C) 1 0.86 0.836 0.588 0.528 0 0.954 0.863 0.75 0.746 0.524Sort Outcome 1 two three four 5 six 1 two three four 5Dry SeasonIntegrated ScenarioFinally, determined by the C-value in the six procedures under distinct.
