A method for constructing a fishway water replenishment fish luring effect evaluation model

Abstract

The application belongs to the technical field of ecological hydraulics and fish protection, and provides a construction method of a fishway water supplement fish luring effect evaluation model: 1, dam downstream marker fish release test; 2, fishway water supplement fish luring effect evaluation index is established, and main events and competition risk events are defined; 3, fishway water supplement fish luring effect data file variable and fishway water supplement fish luring effect evaluation model are established; 4, key factors influencing the water supplement fish luring effect are identified, and the optimal model for evaluating the fishway water supplement fish luring effect is screened out; 5, the optimal model is relied on to carry out evaluation on the water supplement fish luring effect. The application aims at the problems of dam discharge covering fishway water supplement fish luring flow signal and competition interference, studies the key factors influencing the water supplement fish luring effect, and constructs a Fine-Gray competition risk model by taking in biological and environmental covariates and removing multiple collinearity variables after VIF test, so that the optimal model is screened out through AIC criterion and the key influencing factors are identified, and finally the quantitative evaluation of the water supplement fish luring effect is realized.

Description

Technical Field

[0001] This invention belongs to the field of ecohydraulics and fish conservation technology, specifically relating to a method for constructing an evaluation model of the effect of water replenishment and fish attraction in water conservancy facilities by applying competitive risk statistics. Background Technology

[0002] With the increasing integration of water conservancy projects and ecological environmental protection, the restoration of river connectivity has become a core indicator for watershed ecological governance and aquatic biodiversity conservation. While damming has provided a tremendous material foundation for human civilization in terms of flood control, power generation, irrigation, and navigation, its physical disruption of aquatic ecosystems cannot be ignored. Fishway facilities, as key engineering measures for restoring river connectivity and ensuring the integrity of the life cycle of migratory fish, are beneficial to maintaining biodiversity.

[0003] Although fishway infrastructure has a long history, many early projects did not achieve ideal results in terms of fish passage. The core problem was that fish struggled to accurately locate the fishway entrance in the downstream flow field. The main channel's flood discharge or power generation flow far exceeded the fishway's own flow, making the fish-attracting flow signal at the fishway exit easily masked by the dam's discharge signal. Water replenishment for fish attraction is designed to address this issue of fishway entrance detection probability. Currently, water replenishment technology has evolved from simple bypass pipe replenishment to more refined parameters, biomimetic structures, and intelligent control. Domestic and international scholars and engineers have conducted extensive physical model experiments and in-situ monitoring studies on key parameters such as replenishment flow rate, inlet layout, flow pattern, and velocity gradient, resulting in a series of engineering application cases that provide fundamental support for the design of fishway replenishment systems. However, in practical engineering applications and theoretical research, existing water replenishment and fish attraction technologies still have obvious shortcomings and deficiencies. There is a lack of scientific and accurate quantitative evaluation methods and systems for the effects of water replenishment and fish attraction. Existing evaluation methods mostly rely on indicators such as water replenishment attraction rate and total number of fish passing through, completely ignoring the competitive risk effect brought about by dam discharge.

[0004] This invention provides a method for constructing an evaluation model for the effectiveness of fish attraction through water replenishment in fish passages. By introducing the Fine-Gray competition risk model, it corrects the systematic bias of traditional evaluation methods in handling competition events. Combined with a multi-dimensional evaluation index system and multi-factor covariate screening, it achieves accurate quantitative evaluation of the effectiveness of water replenishment for attracting fish under the interference of outflow. At the same time, it identifies key factors affecting the effectiveness of water replenishment for attracting fish, and finally provides a scientific and reliable quantitative basis for the design optimization and operation management of fish passage water replenishment systems. Summary of the Invention

[0005] This invention provides a method for constructing a model for evaluating the effect of water replenishment and fish attraction in fish passages. The method utilizes mathematical modeling to construct an evaluation model for the effect of water replenishment and fish attraction coupled with multiple factors under the condition of leakage interference. The model provides suggested reference values ​​for the effect of water replenishment and fish attraction.

[0006] A method for constructing a model for evaluating the effectiveness of fish attraction through water replenishment in fish passages includes the following steps:

[0007] Step 1: Marking and releasing fish downstream of the dam; Step 2: Construct evaluation indicators for the effectiveness of fishway water replenishment and fish attraction, and define key events and competitive risk events; Step 3: Establish data file variables for the effect of fishway water replenishment and fish attraction, and establish an evaluation model for the effect of fishway water replenishment and fish attraction; Step 4: Identify the key factors affecting the effectiveness of water replenishment for attracting fish, and select the optimal model for evaluating the effectiveness of water replenishment for attracting fish in fish passages; Step 5: Evaluate the effect of water replenishment on attracting fish based on the optimal model.

[0008] Preferably, the experimental process in step 1 is as follows: PIT induction coils are deployed in the fishway inlet area and the discharge area below the dam, and then PIT tags are implanted into the abdominal cavity of the experimental fish. The fish are then released to conduct the experiment. Monitoring data is automatically collected by the induction coils to obtain the attraction probability of fish in different functional areas in real time.

[0009] Preferably, in step 2, the calculation method for the evaluation index of the fishway water replenishment and fish attraction effect is as follows: Based on the tagged fish data monitored by the radio frequency identification system, corresponding indicators are obtained to evaluate the effectiveness of fish attraction through water replenishment in fish passages. These indicators include: water replenishment attraction rate, water replenishment attraction time, misattraction rate due to leakage, misattraction time due to leakage, and cumulative occurrence rate of attraction. The definitions of each evaluation indicator are as follows: (1) Water replenishment attraction rate, defined as the ratio of the number of tagged fish successfully attracted to the entrance of the fishway by the replenishment water flow to the total number of tagged fish released: ; in, For water absorption rate; To determine the number of tagged fish to release in the experiment; The number of tagged fish detected by the induction coil at the entrance area of ​​the fishway; (2) Water replenishment and attraction time is defined as the time interval between the start of release and the first detection of the tagged fish at the entrance of the fishway: ; in, To attract time; The moment when the induction coil at the entrance area of ​​the fishway first identifies a marked fish; To test the timing of releasing the tagged fish; (3) Misattraction rate, defined as the ratio of the number of tagged fish attracted to the discharge area by the dam discharge to the total number of released tagged fish: ; in, For the leakage misdirection rate; To determine the number of tagged fish to release in the experiment; The number of tagged fish detected by induction coils in the dam spillway area; (4) Misleading time of discharge is defined as the time interval between the start of the release of the tagged fish and the first detection of the fish in the discharge area of ​​the dam: ; in, For the time of accidental leakage; This is the moment when the induction coil in the dam's spillway area first detected the marked fish; To test the timing of releasing the tagged fish; (5) Cumulative incidence rate of induced fish populations, a dynamic indicator calculated using the Fine-Gray risk competition model, is defined as the rate at which fish populations in a given environment, considering the risk of competition from spillway flow, are affected. The probability of entering the water replenishment or discharge zone before the specified time: ; in, To induce cumulative incidence, it means "up to the 1st" Up to the hour, because of the "Cumulative probability of a class of events occurring"; For "the time when the event occurred" Before"; The event type that ultimately occurs for each fish.

[0010] Preferably, in step 2, the event type that ultimately occurs for each fish is defined as follows: (1) Key event: The tagged fish first reach the fishway entrance area after release ( Attract success); (2) Competition risk event: After release, the tagged fish are attracted by the high velocity or strong turbulence signal of the dam discharge and enter the discharge interference area downstream of the dam. (Misdirection of leakage); (3) Censorship: During the experimental observation period, the tagged fish neither entered the fishway entrance area nor the dam discharge area, or the tagged fish signal was not detected due to reasons such as tag detachment. (Deleted).

[0011] Preferably, in step 3, the variables in the fishway water replenishment and fish attraction effect data file include biological factors and environmental factors. The biological factors include body length, total length, wet weight, and condition factor, while the environmental factors include water temperature, diurnal rhythm, water replenishment flow rate, outflow flow rate, water pH, and water transparency.

[0012] Preferably, in step 3, to address the potential multicollinearity problem among covariates and improve the model's fitting accuracy, the variance inflation factor (VIF) is used to test all candidate covariates and eliminate those with strong multicollinearity. The formula for calculating the VIF value is as follows:

[0013] in, For the first Variance inflation factor for each variable; With the first The coefficient of determination when performing linear regression with one variable as the dependent variable and all other variables as independent variables; The covariates are body length, total length, wet weight, condition factor, water temperature, diurnal rhythm, water inflow, outflow, water pH, and water transparency.

[0014] Preferably, in step 3, the evaluation model for the effectiveness of fishway water replenishment and fish attraction adopts the Fine-Gray competitive risk model based on the sub-distribution risk function, as shown in the following expression:

[0015] in, For a given covariate Below, mark the fish in The instantaneous risk rate when entering the fishway entrance area; The baseline subdistribution risk rate represents the baseline attraction strength when all covariates are 0; The set of covariates includes body length, total length, wet weight, condition factor, water temperature, diurnal rhythm, water inflow, outflow, water pH, and water transparency. These are the regression coefficients of each covariate.

[0016] Preferably, in step 4, the Akaike Information Criterion (AIC) is used to select the optimal model and identify the key factors affecting the effect of water replenishment and fish attraction. The smaller the AIC value, the better the model is in balancing complexity and fitting effect. The formula for the Akaike Information Criterion is: ; in, The number of model parameters; This represents the log-likelihood function value of the model.

[0017] Preferably, in step 5, the results of the Fine-Gray competitive risk model based on the sub-distribution risk function are used to evaluate the effect of water replenishment on attracting fish, providing a quantitative basis for formulating management measures and optimization schemes for the fishway water replenishment system.

[0018] The present invention also provides an evaluation model for the effect of water replenishment and fish attraction in fish passages, characterized in that it is constructed using the above-mentioned construction method for evaluating the effect of water replenishment and fish attraction in fish passages.

[0019] The beneficial effects of this invention are as follows: 1. This invention aims to improve the fish attraction effect of water replenishment and fish attraction facilities. It introduces the Fine-Gray competitive risk model for the first time to solve the problem of evaluation bias caused by simply censoring fish that are misled by the discharge in traditional survival analysis. It more realistically restores the competitive attraction mechanism of the complex flow field below the dam.

[0020] 2. This invention eliminates multicollinearity of covariates through the VIF test and selects the optimal model using the AIC criterion, ensuring that the evaluation results accurately reflect the contribution of biological and environmental factors to the effect of water replenishment and fish attraction.

[0021] 3. This invention relies on radio frequency identification technology to individually mark experimental fish using PIT tags, and analyzes multiple dynamic indicators such as the fish's water replenishment attraction rate, water replenishment attraction time, discharge mis-attraction rate, discharge mis-attraction time, and cumulative attraction occurrence rate, thereby achieving a comprehensive evaluation of the water replenishment and fish attraction effect.

[0022] 4. This invention uses the Fine-Gray competitive risk model to identify the key factors affecting the effectiveness of water replenishment for fish attraction under the interference of dam discharge, and uses this model to provide reference and technical support for the evaluation of the effectiveness of water replenishment for fish attraction. Attached Figure Description

[0023] Figure 1 This is a flowchart of the steps of the present invention; Figure 2 A schematic diagram of the test area layout; Figure 3 Covariate correlation analysis diagram for a study on the effect of water replenishment on fish passages to attract fish; Figure 4 This is a schematic diagram of the overall cumulative incidence rate curve. Figure 5 A schematic diagram showing the cumulative incidence rate curves of fish passage area induced at different times; Figure 6 A schematic diagram showing the cumulative incidence rate curves of fishway areas with different flow rates. Detailed Implementation

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. A method for constructing a model for evaluating the effectiveness of fish attraction through water replenishment in fish passages includes the following steps: Step 1: Marking and releasing fish downstream of the dam; Step 2: Construct evaluation indicators for the effectiveness of fishway water replenishment and fish attraction, and define key events and competitive risk events; Step 3: Establish data file variables for the effect of fishway water replenishment and fish attraction, and establish an evaluation model for the effect of fishway water replenishment and fish attraction; Step 4: Identify the key factors affecting the effectiveness of water replenishment for attracting fish, and select the optimal model for evaluating the effectiveness of water replenishment for attracting fish in fish passages; Step 5: Evaluate the effect of water replenishment on attracting fish based on the optimal model.

[0025] In step 1, radio frequency identification (RFI) devices (Passive Integrated Transponder, PIT) and induction coils (such as...) are deployed at the fishway entrance and spillway area downstream of the dam. Figure 2 (As shown by the red dashed line), induction coils were deployed around the fishway entrance area, covering all routes that fish could take to enter the area. Induction coils were also deployed before the main spillway area. PIT tags were then implanted into the abdominal cavities of the test fish. At the start of the formal experiment, the test fish were released in the area below the dam. When the fish were attracted by the water flow and reached the induction coils, the RFID monitoring system automatically marked the fish's number and arrival time, obtaining real-time data on the probability of fish being attracted to different areas. In step 2, the calculation method and event type definition for the evaluation index of the fishway water replenishment and fish attraction effect are as follows: Based on the tagged fish data monitored by the radio frequency identification system, corresponding indicators are obtained to evaluate the effectiveness of fish attraction through water replenishment in fish passages. These indicators include: water replenishment attraction rate, water replenishment attraction time, misattraction rate due to leakage, misattraction time due to leakage, and cumulative occurrence rate of attraction. The definitions of each evaluation indicator are as follows: (1) Water replenishment attraction rate, defined as the ratio of the number of tagged fish successfully attracted to the entrance of the fishway by the replenishment water flow to the total number of tagged fish released: ; in, For water absorption rate; To determine the number of tagged fish to release in the experiment; The number of tagged fish detected by the induction coil at the entrance area of ​​the fishway; (2) Water replenishment and attraction time is defined as the time interval between the start of release and the first detection of the tagged fish at the entrance of the fishway: ; in, To attract time; The moment when the induction coil at the entrance area of ​​the fishway first identifies a marked fish; To test the timing of releasing the tagged fish; (3) Misattraction rate, defined as the ratio of the number of tagged fish attracted to the discharge area by the dam discharge to the total number of released tagged fish: ; in, For the leakage misdirection rate; To determine the number of tagged fish to release in the experiment; The number of tagged fish detected by induction coils in the dam spillway area; (4) Misleading time of discharge is defined as the time interval between the start of the release of the tagged fish and the first detection of the fish in the discharge area of ​​the dam:

[0026] in, For the time of accidental leakage; This is the moment when the induction coil in the dam's spillway area first detected the marked fish; To test the timing of releasing the tagged fish; (5) Cumulative Induced Incidence Rate (CIF), a dynamic indicator calculated using the Fine-Gray risk competition model, is defined as the rate at which fish in the event of a fish population increase after considering the risk of competition from the spillway. The probability of entering the water replenishment or discharge zone before the specified time: ; in, To induce cumulative incidence, it means "up to the 1st" Up to the hour, because of the "Cumulative probability of a class of events occurring"; For "the time when the event occurred" Before"; The event type that ultimately occurs for each fish.

[0027] The event type that ultimately occurs for each fish is defined as follows: (1) Key event: The tagged fish first reach the fishway entrance area after release ( Attract success); (2) Competition risk event: After release, the tagged fish are attracted by the high velocity or strong turbulence signal of the dam discharge and enter the discharge interference area downstream of the dam. (Misdirection of leakage); (3) Censorship: During the experimental observation period, the tagged fish neither entered the fishway entrance area nor the dam discharge area, or the tagged fish signal was not detected due to reasons such as tag detachment. (Deleted).

[0028] In step 3, the data file variables for the fishway water replenishment and fish attraction effect include biological factors and environmental factors. The biological factors include body length, total length, wet weight and condition factor, while the environmental factors include water temperature, diurnal rhythm, water replenishment flow rate, outflow flow rate, water pH and water transparency, as detailed in Table 1.

[0029] Table 1. Data file variables for the fishway water replenishment and fish attraction effect evaluation model.

[0030] Furthermore, in step 3, to address the potential multicollinearity issue among the covariates (i.e., the file variables mentioned above) and improve the model's fitting accuracy, the VIF test is used to examine all candidate covariates. The formula for calculating the VIF value of each covariate is as follows:

[0031] in For the first Variance inflation factor for each variable; With the first When performing linear regression with one variable as the dependent variable and all other variables as independent variables, the coefficients of determination are: VIF < 5 (no collinearity), 5 ≤ VIF < 10 (moderate collinearity), and VIF ≥ 10 (severe collinearity).

[0032] Finally, in step 3, the evaluation model for the effectiveness of fishway water replenishment and fish attraction adopts the Fine-Gray competitive risk model based on the sub-distribution risk function, as shown in the following expression:

[0033] in, For a given covariate Below, mark the fish in The instantaneous risk rate when entering the fishway entrance area; The baseline subdistribution risk rate represents the baseline attraction strength when all covariates are 0; The set of covariates includes body length, total length, wet weight, condition factor, water temperature, diurnal rhythm, water inflow, outflow, water pH, and water transparency. These are the regression coefficients of each covariate.

[0034] In step 4, the Akaike Information Criterion (AIC) is used to select the optimal model and identify the key factors affecting the effectiveness of water replenishment for fish attraction. A smaller AIC value indicates a better model in balancing complexity and fitting performance. The Akaike Information Criterion formula is: ; in, The number of model parameters; This represents the log-likelihood function value of the model.

[0035] In step 5, the results of the Fine-Gray competitive risk model based on the sub-distribution risk function are used to evaluate the effect of water replenishment on attracting fish, providing a quantitative basis for formulating management measures and optimization schemes for the fishway water replenishment system.

[0036] Example 1 A survey of existing fishway projects revealed that the effectiveness of water replenishment for attracting fish is significantly hampered by dam discharge, with some fish being misled by the discharge to areas downstream of the dam that are not at the fishway entrance. A calculation example is provided to illustrate the calculation method and process involved in this invention. The specific implementation process is as follows: Using a water conservancy hub equipped with a water replenishment and fish attraction system, healthy fish were selected, and PIT tags were implanted into their abdominal cavities. After being temporarily held for 48 hours, the experimental fish were released into the downstream area of ​​the dam. Radio frequency identification (RFID) technology was used in the experimental area to obtain data on the water replenishment attraction rate, water replenishment attraction time, discharge misattraction rate, discharge misattraction time, and cumulative incidence of attraction. Figure 4 The behavioral outcomes of the experimental fish were statistically analyzed, as shown in Table 2.

[0037] Table 2. Classification and statistics of behavioral outcomes of experimental fish

[0038] The data file recording the fish-attracting effect of fishway water replenishment includes variables (Table 3) that comprise biological and environmental factors. Biological factors include body length, total length, wet weight, and condition factor, while environmental factors include water temperature, diurnal rhythm, water inflow rate, outflow rate, water pH, and water transparency. Before building the model, the correlation between variables was analyzed. Figure 3 The VIF test was used on all candidate covariates to remove variables with high collinearity. All 10 candidate variables were included in the first round of VIF testing, and the results are shown in Table 4. Based on the severe collinearity issues found in the VIF test for body length, total length, and wet weight, these variables were removed. The replenishment flow rate and discharge flow rate showed a moderate correlation and were combined into the flow ratio QS / QR (FR), which directly reflects the strength of the replenishment signal's interference with the discharge flow, and has a more significant biological meaning. A second round of VIF testing was conducted on the remaining variables (fertility, water temperature, diurnal rhythm, flow ratio, water pH, and water transparency), and the results are shown in Table 5. Finally, fertility, water temperature, diurnal rhythm, flow ratio, water pH, and water transparency can be included in subsequent model analyses.

[0039] Table 3 Experimental data file variables

[0040] Table 4 Results of the first round of VIF testing

[0041] Table 5 Results of the second round of VIF testing

[0042] Multiple models were screened using the Akaike Information Criterion (AIC). The model with the two covariates of circadian rhythm (CR) and flow ratio (FR) was found to have the lowest AIC value and the best fit (see Table 6). This model was used as the optimal model for fish attraction through water replenishment to identify key factors affecting the effectiveness of fish attraction through water replenishment.

[0043] Based on the regression coefficients and significance test results of the optimal model (see Table 7), it can be seen that the diurnal rhythm has a significant impact on the water replenishment and fish attraction effect model (P<0.05), and the flow rate ratio has a highly significant impact on the water replenishment and fish attraction effect model (P<0.01). Specifically, the analysis results show that the released fish are 4.26 times more likely to reach the fishway entrance area during the day than at night (see Table 7). Figure 5 For every 0.1 increase in the flow rate ratio, the probability of the released fish reaching the fishway entrance area increases by 20%. The high flow rate ratio group exhibits a faster rate of replenishment and fish attraction events, with a higher cumulative incidence (see...). Figure 6 ).

[0044] Table 6 Model Selection Based on Akaike Information Criterion (AIC)

[0045] Note: , For model weights, These are the optimal model weights.

[0046] Table 7

[0047] Note: β is the regression coefficient of the Fine-Gray competitive risk model; SE is the standard error of the regression coefficient β; Z is the test statistic, calculated as Z=β / SE; P is the significance probability; SHR is the subdistribution hazard ratio, calculated as SHR=e^β; 95% CI is the 95% confidence interval corresponding to SHR.

Claims

1. A method for constructing a model for evaluating the effectiveness of fish attraction through water replenishment in fish passages, characterized in that, Includes the following steps: Step 1: Marking and releasing fish downstream of the dam; Step 2: Construct evaluation indicators for the effectiveness of fishway water replenishment and fish attraction, and define key events and competitive risk events; Step 3: Establish data file variables for the effect of fishway water replenishment and fish attraction, and establish an evaluation model for the effect of fishway water replenishment and fish attraction; Step 4: Identify the key factors affecting the effectiveness of water replenishment for attracting fish, and select the optimal model for evaluating the effectiveness of water replenishment for attracting fish in fish passages; Step 5: Evaluate the effect of water replenishment on attracting fish based on the optimal model.

2. The method for constructing a model for evaluating the effect of fish attraction through water replenishment in fish passages, as described in claim 1, is characterized in that... Step 1 Experimental Procedure: PIT induction coils were deployed in the fishway inlet area and the discharge area below the dam. Then, PIT tags were implanted into the abdominal cavity of the test fish, and the fish were released to conduct the experiment. Monitoring data was automatically collected by the induction coils.

3. The method for constructing a model for evaluating the effect of fish attraction through water replenishment in fish passages according to claim 1, characterized in that, In step 2, the calculation method for the evaluation index of the fishway water replenishment and fish attraction effect is as follows: Based on the tagged fish data monitored by the radio frequency identification system, corresponding indicators are obtained to evaluate the effectiveness of fish attraction through water replenishment in fish passages. These indicators include: water replenishment attraction rate, water replenishment attraction time, misattraction rate due to leakage, misattraction time due to leakage, and cumulative occurrence rate of attraction. The definitions of each evaluation indicator are as follows: (1) Water replenishment attraction rate, defined as the ratio of the number of tagged fish successfully attracted to the entrance of the fishway by the replenishment water flow to the total number of tagged fish released: ; in, For water absorption rate; To determine the number of tagged fish to release in the experiment; The number of tagged fish detected by the induction coil at the entrance area of ​​the fishway; (2) Water replenishment and attraction time is defined as the time interval between the start of release and the first detection of the tagged fish at the entrance of the fishway: ; in, To attract time; The moment when the induction coil at the entrance area of ​​the fishway first identifies a marked fish; To test the timing of releasing the tagged fish; (3) Misattraction rate, defined as the ratio of the number of tagged fish attracted to the discharge area by the dam discharge to the total number of released tagged fish: ; in, For the leakage misdirection rate; To determine the number of tagged fish to release in the experiment; The number of tagged fish detected by induction coils in the dam spillway area; (4) Misleading time of discharge is defined as the time interval between the start of the release of the tagged fish and the first detection of the fish in the discharge area of ​​the dam: ; in, For the time of accidental leakage; This is the moment when the induction coil in the dam's spillway area first detected the marked fish; To test the timing of releasing the tagged fish; (5) Cumulative incidence rate of induced fish populations, a dynamic indicator calculated using the Fine-Gray risk competition model, is defined as the rate at which fish populations in a given environment, considering the risk of competition from spillway flow, are affected. The probability of entering the water replenishment or discharge zone before the specified time: ; in, To induce cumulative incidence, it means "up to the 1st" Up to the hour, because of the "Cumulative probability of a class of events occurring"; The time when the event occurred Before"; The event type that ultimately occurs for each fish.

4. The method for constructing a model for evaluating the effect of fish attraction through water replenishment in fish passages according to claim 1, characterized in that, In step 2, the event type that ultimately occurs for each fish is defined as follows: (1) Key event: The tagged fish first reach the fishway entrance area after release ( Attract success); (2) Competition risk event: After release, the tagged fish are attracted by the high velocity or strong turbulence signal of the dam discharge and enter the discharge interference area downstream of the dam. (Misdirection of leakage); (3) Censorship: During the experimental observation period, the tagged fish neither entered the fishway entrance area nor the dam discharge area, or the tagged fish signal was not detected due to reasons such as tag detachment. (Deleted).

5. The method for constructing a model for evaluating the effect of fish attraction through water replenishment in fish passages according to claim 1, characterized in that, In step 3, the data file variables for the fishway water replenishment and fish attraction effect include biological factors and environmental factors. The biological factors include body length, total length, wet weight, and condition factor, while the environmental factors include water temperature, diurnal rhythm, water replenishment flow rate, outflow flow rate, water pH, and water transparency.

6. The method for constructing a model for evaluating the effect of fish attraction through water replenishment in fish passages according to claim 1, characterized in that, In step 3, to address the potential multicollinearity issue among covariates and improve model fitting accuracy, the variance inflation factor (VIF) is used to test all candidate covariates and eliminate those with strong multicollinearity. The formula for calculating the VIF value is as follows: in, For the first Variance inflation factor for each variable; With the first The coefficient of determination when performing linear regression with one variable as the dependent variable and all other variables as independent variables; The covariates are body length, total length, wet weight, condition factor, water temperature, diurnal rhythm, water inflow, outflow, water pH, and water transparency.

7. The method for constructing a model for evaluating the effect of fish attraction through water replenishment in fish passages according to claim 1, characterized in that, In step 3, the evaluation model for the effectiveness of fishway water replenishment and fish attraction adopts the Fine-Gray competitive risk model based on the sub-distribution risk function, as shown in the following expression: in, For a given covariate Below, mark the fish in The instantaneous risk rate when entering the fishway entrance area; The baseline subdistribution risk rate represents the baseline attraction strength when all covariates are 0; The set of covariates includes body length, total length, wet weight, condition factor, water temperature, diurnal rhythm, water inflow, outflow, water pH, and water transparency. These are the regression coefficients of each covariate.

8. A method for constructing a model for evaluating the effectiveness of fish attraction through water replenishment in fish passages, as described in claim 1, characterized in that... In step 4, the Akaike Information Criterion (AIC) is used to select the optimal model and identify the key factors affecting the effectiveness of water replenishment for fish attraction. A smaller AIC value indicates a better model in balancing complexity and fit. The Akaike Information Criterion formula is: ； in, The number of model parameters; This represents the log-likelihood function value of the model.

9. A method for constructing a model for evaluating the effectiveness of fish attraction through water replenishment in fish passages, as described in claim 7, characterized in that... In step 5, the results of the Fine-Gray competitive risk model based on the sub-distribution risk function are used to evaluate the effect of water replenishment on attracting fish, providing a quantitative basis for formulating management measures and optimization schemes for the fishway water replenishment system.

10. A model for evaluating the effectiveness of water replenishment and fish attraction in fish passages, characterized in that, It was constructed using the method described in any one of claims 1-9 for evaluating the effect of fish attraction through water replenishment in fish passages.

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