## 3 Evaluation Model of Current Situation of River and Lake Shoreline Planning and Utilization
### Combined Weighting Method to Calculate Weights
#### 3.1.2 Calculation of Weights Based on the Improved CRITIC Method  
The CRITIC method is a method proposed by [PERSON] to calculate objective weights, which not only considers the degree of variability of the data but also focuses on the correlation between them compared to the entropy weight method and the standard deviation method. It calculates the indicator weights by the relative strength of the indicators and the magnitude of the information measured by the standard deviation. It does not mean that the greater the variation of the value, the more important it is, but it is a scientific judgment made entirely by using the objective properties of the data. The standard deviation reflects the degree of difference in the values taken by each assessment scheme under the same indicator, and the larger the standard deviation, the greater the fluctuation. If there is a large positive correlation between the indicators, it means that the less conflicting they are, the lower the weights will be. The method is a more comprehensive objective weighting method and has been applied in many fields. The traditional CRITIC method is suitable for data with stability and indicators with certain correlation, but when the mean difference of indicator data is large, the standard deviation will be affected by the mean, so it needs to be improved by introducing the coefficient of variation for calculation, and the larger the value of the coefficient of variation, the more obvious the fluctuation and the more informative the improved of CRITIC method calculates more accurate indicator weights, and the steps are as follows:  
Let the assessment matrix \(V=[v_{kj}]\), and standardize the values \(v_{kj}\) taken for the evaluation indicators to obtain the matrix \(V^{\prime}\):  
\[V^{\prime}=[v^{\prime}_{kj}] \tag{4}\]  
\(v^{\prime}_{kj}=(v_{kj}-\overline{v}_{j})/s_{j}\), \(\overline{v}_{j}\) and \(s_{j}\) denote the mean and standard deviation of the \(j\)th indicator, respectively.  
The coefficient of variation \(u_{j}\) for the \(j\)th indicator is as follows:  
\[u_{j}=\frac{s_{j}}{\overline{v}_{j}} \tag{5}\]The correlation coefficient matrix \(R\) is obtained from the normalized matrix \(V^{\prime}\):  
\[R=\left[r_{lj}\right] \tag{6}\]  
\[r_{lj}=\frac{\sum\limits_{k=1}^{n}\left(v^{\prime}_{kl}-\overline{v^{\prime}_{ l}}\right)\left(v^{\prime}_{kj}-\overline{v^{\prime}_{j}}\right)}{\sqrt{\sum \limits_{k=1}^{n}\left(v^{\prime}_{kl}-\overline{v^{\prime}_{l}}\right)^{2} \left(v^{\prime}_{kj}-\overline{v^{\prime}_{j}}\right)^{2}}}=\frac{\text{cov} (V^{\prime}_{l}-V^{\prime}_{j})}{\sqrt{D(V^{\prime}_{l})}\sqrt{D(V^{\prime}_{ j})}} \tag{7}\]  
In Equation (7), \(r_{lj}\) denotes the correlation coefficient between the \(l\)th indicator and the \(j\)th indicator and \(\text{cov}(V^{\prime}_{l}-V^{\prime}_{j})\) denotes the covariance of \(V^{\prime}_{l}\) and \(V^{\prime}_{j}\).  
The composite factor \(Tj\) for indicator \(j\) is as follows:  
\[T_{j}=u_{j}\sum\limits_{l=1}^{n}\left(1-r_{lj}\right) \tag{8}\]  
The objective weight \(\omega_{kj}\) of the \(j\)th evaluation indicator is as follows:  
\[\omega_{kj}=\frac{T_{j}}{\sum\limits_{j=1}^{n}T_{j}} \tag{9}\]