A method for evaluating and predicting the mixing degree and hypoxic zone of lake and reservoir water bodies based on decision tree analysis

Abstract

The present invention discloses a method for assessing and predicting the mixing degree and hypoxic zone of lake and reservoir water bodies based on decision tree analysis, comprising the following steps: collecting the physical and chemical indicators of the target lake and reservoir water body and basic information on meteorological factors at the lake and reservoir location; calculating the water density gradient and determining the water density at each depth during different thermal stratification periods, and assessing the mixing degree of the lake and reservoir water body during different thermal stratification periods; calculating the water hypoxia index and determining the distribution range of the hypoxic zone during different thermal stratification periods, and assessing the dissolved oxygen distribution state of the lake and reservoir water body during different thermal stratification periods; programming a decision tree code using RStudio software to couple the meteorological factors with the depth of the lake and reservoir mixing layer and the hypoxic zone, analyzing the numerical changes, assessing the probability of occurrence, and making predictions. This method can systematically assess the numerical changes and probability of occurrence of the mixing degree and distribution range of the hypoxic zone of the lake and reservoir water body, and provide reference indicators for improving the hydrodynamics of the lake and reservoir water body and managing the deterioration of water quality caused by thermal stratification.

Description

Technical Field

[0001] The present invention relates to the field of comprehensive water environment management, and in particular to a method for evaluating and predicting the mixing degree and anoxic zones of lake and reservoir water bodies based on decision tree analysis. Background Art

[0002] Thermal stratification of water bodies is a widespread phenomenon in nature, primarily influenced by the combined influence of factors such as lake depth, water mobility, and the meteorological and climatic conditions of the region in which the lakes and reservoirs are located. Thermal stratification is a phenomenon in which temperature variations in water lead to uneven vertical temperature distribution. During thermal stratification, the water body is divided into a mixing layer, a thermocline, and a stagnant layer from the surface to the bottom. This stable temperature stratification causes significant changes in the physical and chemical properties of the water body, as well as the characteristics and distribution of aquatic organisms. Among them, dissolved oxygen, the most important indicator of the health of lake and reservoir aquatic ecosystems, has its concentration distribution significantly affected by thermal stratification. The thermocline inhibits vertical mixing between the surface and bottom waters of lakes and reservoirs, hindering atmospheric reoxygenation and photosynthetic oxygen production in the upper waters from replenishing the lower waters. Dissolved oxygen in the stagnant layer is gradually depleted by the combined effects of organic matter decomposition and oxygen consumption by bottom sediments, ultimately forming a zone of extremely low dissolved oxygen concentration in the lower waters of lakes and reservoirs. During the summer thermal stratification period in deepwater lakes and reservoirs, the emergence of hypoxic zones in the stagnation layer is a common phenomenon. Thermal stratification and the emergence of hypoxic zones in lakes and reservoirs pose a certain threat to the stability of the water environment.

[0003] Therefore, it is necessary to study and analyze the changes in the degree of water mixing and the distribution of dissolved oxygen in lake and reservoir water bodies during different thermal stratification periods, and clearly point out the scope of the hypoxic zone, so as to provide a basis for more scientific and reasonable guidance of lake and reservoir water environment management and water pollution control. Summary of the Invention

[0004] Purpose of the invention: In order to overcome the shortcomings of the background technology, the present invention discloses a method for evaluating and predicting the mixing degree and hypoxic zone of lake and reservoir water bodies based on decision tree analysis; this method conducts on-site monitoring of water samples and meteorological factors of target lakes and reservoirs, analyzes the physical and chemical indicators of water bodies, calculates the mixing layer depth and hypoxia index of target lake and reservoir water bodies, and combines R language decision tree to perform systematic evaluation and prediction of water mixing degree and hypoxic zone.

[0005] Technical solution: The method for evaluating and predicting the mixing degree and hypoxic zone of lake and reservoir water bodies based on decision tree analysis disclosed in the present invention includes the following steps:

[0006] S1. Collect basic information on the physical and chemical indicators of the target lake and reservoir and the meteorological factors of the lake and reservoir location;

[0007] S2. Based on the physical and chemical indicators of the target lakes and reservoirs, calculate the water density gradient and determine the water density at each depth during different thermal stratification periods, and assess the degree of mixing of the lake and reservoir water during different thermal stratification periods;

[0008] S3. Based on the physical and chemical indicators of the target lakes and reservoirs, calculate the water hypoxia index and determine the distribution range of the water hypoxia zone during different thermal stratification periods, and evaluate the dissolved oxygen distribution status of the lake water during different thermal stratification periods;

[0009] S4. Decision tree code was written using RStudio software to couple meteorological factors with the depth of the lake mixing layer and the hypoxic zone, and then analyze the numerical change assessment, occurrence probability, and prediction forecast.

[0010] Among them, S2 is evaluated using the following method:

[0011]

[0012]

[0013]

[0014] Where, ρ i It is the density of lake water at different depths, in kg / m 3 ;T i is the temperature at different depths of the lake water body, in °C; i We can find ρ here i , thus obtaining the density gradient, δ min The unit is kg / m 3 / m;Z i is the water depth corresponding to the i-th layer; Z e is the depth of the mixed layer in m;

[0015] The depth of the water mixing layer is determined by the water density and water density gradient. If there is a density gradient mutation zone inside the water body, there will be thermal stratification in the water body and the degree of water mixing will be poor. The greater the depth of the mixing layer, the better the degree of water mixing, and the smaller the depth of the mixing layer, the worse the degree of water mixing. When the depth of the mixing layer is close to the maximum water depth, the water body is in a completely mixed state.

[0016] Furthermore, S3 uses the following methods for evaluation:

[0017]

[0018] Where H anoxic is the water depth where the dissolved oxygen concentration in a monitoring vertical line is lower than X mg / L, m; H w is the total water depth at the monitoring point, m;

[0019] The hypoxic zone in the water body is measured by the hypoxia index. When AI = 0, there is no hypoxic zone in the water body; when AI>0, there is a hypoxic zone in the water body. At the same time, the degree of hypoxia in the water body can be compared according to the size of the AI ​​index. The higher the AI ​​index, the more serious the degree of hypoxia in the water body.

[0020] Furthermore, in S4, the changes in the mixed layer depth and the hypoxic zone can be coupled with meteorological factors through R language decision tree analysis, thereby obtaining the numerical changes and occurrence probabilities of the mixed layer depth and the hypoxic zone under different meteorological factors, and combining the predicted meteorological factors to predict the mixed layer depth and the hypoxic zone.

[0021] Beneficial effects: Compared with the existing technology: The present invention takes into account the sensitivity of the physical and chemical indicators of lake and reservoir water bodies to hydrodynamic conditions and meteorological factors. Through on-site monitoring and indoor analysis, long-term data on the physical and chemical indicators of lake and reservoir water bodies at different times, spaces and depths are obtained. At the same time, meteorological factor data of the lake and reservoir locations are collected. Based on the actual hydrodynamic conditions and physical and chemical indicator information of the target lake and reservoir, the degree of mixing of lake and reservoir water bodies, the numerical changes in the distribution range of hypoxic zones and the probability of occurrence can be systematically evaluated and predicted, providing a basis for reference indicators for the improvement of the hydrodynamics of lake and reservoir water bodies and the treatment of water quality deterioration caused by thermal stratification of water bodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Schematic diagram of the process of the present invention;

[0023] Figure 2 The distribution of thermal stratification degree of a lake or reservoir;

[0024] Figure 3 The vertical water temperature and dissolved oxygen distribution of a lake or reservoir;

[0025] Figure 4 This is the evaluation result of the mixed layer depth of a lake or reservoir;

[0026] Figure 5 This is the evaluation result of the hypoxia index of a certain lake or reservoir;

[0027] Figure 6 This is the result of the decision tree analysis of the mixed layer depth of a certain lake reservoir;

[0028] Figure 7 Analyze the pruning results of the decision tree for the depth of the mixed layer of a certain lake reservoir;

[0029] Figure 8 This is the prediction result of the decision tree for the mixed layer depth of a lake or reservoir. DETAILED DESCRIPTION

[0030] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0031] like Figure 1 The method for assessing and predicting the mixing degree and hypoxic zone of lake and reservoir water bodies based on decision tree analysis includes the following steps:

[0032] S1. Collect the physical and chemical indicators of the target lake and reservoir and basic information on meteorological factors at the lake and reservoir location. Figure 2 The water temperature distribution shown in the figure shows the basic situation of temperature stratification. In mid-to-late January 2020, the water temperature in the upper and lower layers of the water body was similar, and the water body showed a clear mixing state of autumn and winter.

[0033] S2. Based on the physical and chemical indicators of the target lakes and reservoirs, calculate the water density gradient and determine the water density at each depth during different thermal stratification periods, and assess the degree of mixing of the lake and reservoir water during different thermal stratification periods;

[0034]

[0035]

[0036]

[0037] Where, ρ i It is the density of lake water at different depths, in kg / m 3 ;T i is the temperature at different depths of the lake water body, in °C; i We can find ρ here i , thus obtaining the density gradient, δ min The unit is kg / m 3 / m;Z i is the water depth corresponding to the i-th layer; Z e is the depth of the mixed layer in m;

[0038] The depth of the water mixing layer is determined by the water density and water density gradient. If there is a density gradient mutation zone inside the water body, the water body will have thermal stratification and poor water mixing. The greater the depth of the mixing layer, the better the water mixing, and the smaller the depth of the mixing layer, the worse the water mixing. When the depth of the mixing layer is close to the maximum water depth, the water body is in a completely mixed state. Figure 4 The depth of the mixed layer of the water body shown gradually decreased from 7 meters in early December to about 13 meters in mid-to-late January, and the water body gradually entered a completely mixed state.

[0039] S3. Based on the physical and chemical indicators of the target lakes and reservoirs, calculate the water hypoxia index and determine the distribution range of the water hypoxia zone during different thermal stratification periods, and evaluate the dissolved oxygen distribution status of the lake water during different thermal stratification periods;

[0040]

[0041] Where H anoxic is the water depth where the dissolved oxygen concentration in a monitoring vertical line is lower than X mg / L, m; H w is the total water depth at the monitoring point, m;

[0042] The hypoxic zone of the water body is measured by the hypoxia index. When AI=0, there is no hypoxic zone in the water body; when AI>0, there is a hypoxic zone in the water body. At the same time, the degree of hypoxia in the water body can be compared according to the size of the AI ​​index. The higher the AI ​​index, the more serious the degree of hypoxia in the water body. Figure 3 It can be seen that the water temperature of the lake and reservoir showed a gradual decline on December 5, 2019, and the water temperature was completely close in mid-to-late January 2020, and the water mixing layer increased. Figure 5 As shown in the figure, the dissolved oxygen in lake and reservoir water gradually recovers as the water temperature in the lower layer changes, and the anoxic zone gradually decreases until it disappears.

[0043] S4. Use RStudio software to write decision tree code to couple meteorological factors with the depth of the mixed layer of lakes and reservoirs and the hypoxic zone, and then analyze the numerical change assessment and occurrence probability and forecast; specifically, through R language decision tree analysis, the changes in the mixed layer depth and hypoxic zone can be coupled with meteorological factors to analyze the numerical changes and occurrence probability of the mixed layer depth and hypoxic zone under different meteorological factors, and the predicted meteorological factors can be combined to predict the depth of the mixed layer and the hypoxic zone. Figure 6 、 Figure 7 As shown in the figure, based on the existing measured data and calculation results, the decision tree method is used to combine the water mixing layer degree with the changes in meteorological factors, and the meteorological factors that affect the depth of the mixing layer are analyzed. The results of the preliminary analysis show that the water mixing is mainly affected by radiation, wind speed, and temperature. Therefore, the water mixing degree can be predicted based on meteorological monitoring data and combined with actual monitoring values, and then reasonable arrangements can be made for the management of lakes and reservoirs to prevent the sudden occurrence of water quality problems caused by the formation and destruction of temperature stratification.

Claims

1. A method for evaluating and predicting the mixing degree and hypoxic zone of lake and reservoir water bodies based on decision tree analysis, characterized in that: The following steps are involved: S1. Collect basic information on the physical and chemical indicators of the target lake and reservoir and the meteorological factors of the lake and reservoir location; S2. Based on the physical and chemical indicators of the target lakes and reservoirs, calculate the water density gradient and determine the water density at each depth during different thermal stratification periods, and assess the degree of mixing of the lake and reservoir water during different thermal stratification periods; S3. Based on the physical and chemical indicators of the target lakes and reservoirs, calculate the water hypoxia index and determine the distribution range of the water hypoxia zone during different thermal stratification periods, and evaluate the dissolved oxygen distribution status of the lake water during different thermal stratification periods; S4. Decision tree code was written using RStudio software to couple meteorological factors with the depth of the lake-reservoir mixed layer and the hypoxic zone, and then analyze the numerical change assessment and occurrence probability and forecast; S2 is evaluated using the following method: Where, ρ i It is the density of lake water at different depths, in kg / m 3 ; T i is the temperature at different depths of the lake water body, in °C; i We can find ρ here i , thus obtaining the density gradient, δ min The unit is kg / m 3 / m;Z i is the water depth corresponding to the i-th layer; Z e is the depth of the mixed layer in m; The depth of the water mixing layer is determined by the water density and density gradient. If there is a sudden change in density gradient inside the water body, the water body will be thermally stratified and the water mixing will be poor. The deeper the mixing layer, the better the water mixing, and the smaller the mixing layer, the worse the water mixing. When the mixing layer depth approaches the maximum water depth, the water body is in a completely mixed state. S3 is evaluated using the following methods: Where H anoxic is the water depth where the dissolved oxygen concentration in a monitoring vertical line is lower than X mg / L, m; H w is the total water depth at the monitoring point, m; The hypoxic zone in the water body is measured by the hypoxia index. When AI = 0, there is no hypoxic zone in the water body; when AI>0, there is a hypoxic zone in the water body. At the same time, the degree of hypoxia in the water body can be compared according to the size of the AI ​​index. The higher the AI ​​index, the more serious the degree of hypoxia in the water body.

2. The method for assessing and predicting the mixing degree and hypoxic zone of lake and reservoir water bodies based on decision tree analysis according to claim 1 is characterized by: In S4, the R language decision tree analysis can be used to couple the changes in the mixed layer depth and the hypoxic zone with meteorological factors, thereby obtaining the numerical changes and occurrence probabilities of the mixed layer depth and the hypoxic zone under different meteorological factors, and combining the predicted meteorological factors to predict the mixed layer depth and the hypoxic zone.

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