# Quantitative Analysis of Influencing Factors on Changzhou Ship Lock Capacity
## 5 Conclusions  
Insufficient water level, ship lock maintenance, abnormal weather, and power station discharge seriously affect ship passage and even cause ship navigation delays. They play a dominant role in the influencing factors of ship lock capacity. The ship passing data that do not meet the navigability conditions cannot accurately reflect the navigation situation of ships in saturated operating conditions. To quantitatively analyze the factors constraining the Changzhou ship lock capacity, an analysis method based on PCA was proposed based on the hydrological observation data and ship passing data from the Changzhou ship lock in 2020. The influencing factors of Changzhou ship lock capacity under saturated operation were analyzed, and the contribution rates of each influencing factor were obtained. The main conclusions are as follows:  
1. The confidence interval estimation method, based on lognormal distribution, and the navigability data redundancy elimination method, based on hydrological analysis, can extract ship lock operation data that meets navigability conditions, which is a prerequisite for analyzing the factors affecting the ship lock capacity under saturated operating conditions. 2. Based on PCA, we obtained the contribution rates and cumulative contribution rates of each influencing factor, and took the influencing factors with cumulative contribution rates exceeding 90% as the primary influencing factors. By comparing the contribution rates of influencing factors, it was determined that the primary factors affecting the Changzhou ship lock capacity are lock sill, operation direction, ship dimensions, draft, loading capacity, and actual load. 3. The results obtained by the PCA method are superior to those obtained by the AHP method in analysis of influencing factors of ship lock capacity, which can provide a theoretical basis for improving the ship lock capacity. In qualitative analysis, the data used in the AHP method mostly comes from expert survey questionnaires, which inherently introduces a degree of subjectivity. In contrast, the PCA method, based on enormous real data, leads to more reliable analysis results. In quantitative analysis, the AHP calculates comprehensive weights of influencing factors, reduces the biases arising from data subjectivity through mathematical calculations, and provides a comparative assessment of importance only. However, the PCA method is based on the analysis of enormous real data and provides the contribution rates of each influencing factor. As the amount of data increases, the accuracy of the analysis results is higher. Based on the above analysis, we propose a set of measures to enhance the ship lock capacity, including hydrological monitoring and forecasting, optimization of lock scheduling, automation upgrades, and structural modifications. Additionally, apply predictive maintenance strategies to reduce unexpected downtime. The next research focus is to verify the applicability of the method proposed in this paper on other ship locks and extend it to investigate the influencing factors on the capacity of multi-level and multi-lane ship locks. Conceptualization, Q.X. ; methodology, Q.X. and Y.W. ([PERSON]); software, Q.X. and R.W. ; validation, Q.X. and M.Z. ; formal analysis, Q.X. ; investigation, Q.X. and M.Z. ; resources, Y.W. ([PERSON]) and M.Z. ; data curation, Q.X. and R.W. ; writing--original draft preparation, Q.X. ; writing--review and editing, Q.X. and Y.W. ([PERSON]); visualization, Q.X. ; supervision, [PERSON] ([PERSON]); project administration, [PERSON] ([PERSON]); funding acquisition, M.Z. and Y.W. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key R&D Program of China (Grant No. 2023 YFB2603800), the Guangxi Key Technologies R&D Program (Grant No. GK-AB22080106), the Key Scientific Research Program of Transportation Industry (Grant No. 2021-MS6-156), and the Fundamental Research Funds for the Central Public Welfare Research Institutes (Grant No. TKS20210305).