Geological disaster early warning method based on information amount

Abstract

The invention discloses a geological disaster early warning method based on information amount. The geological disaster early warning method comprises the following specific steps: determining a reference rainfall threshold value of a research area; dividing the research area based on ArcGIS to obtain a plurality of drainage basin units; selecting a plurality of index factors, and calculating the information amount of each index factor based on an information amount model to obtain the information amount of each drainage basin unit; constructing a relationship between the information amount and a threshold adjustment coefficient, and obtaining the threshold adjustment coefficient of each drainage basin unit based on the information amount of each drainage basin unit; obtaining the rainfall threshold value of each drainage basin unit based on the reference rainfall threshold value of the research area and the threshold value adjustment coefficient of each drainage basin unit; obtaining forecast rainfall data, and dividing rainfall grades based on the rainfall threshold value of each drainage basin unit; and determining a geological disaster early warning result based on the rainfall level. According to the method, the rainfall threshold value of each drainage basin unit is obtained based on the reference rainfall threshold value, the information amount of each drainage basin unit and the threshold value adjustment coefficient, so that one unit has one threshold value, and the geological disaster precision and the coverage rate are improved.

Description

Technical Field

[0001] The present invention relates to the field of geological disaster early warning, and more specifically, to a geological disaster early warning method based on information quantity. Background Art

[0002] Geological disasters are an important manifestation of landform evolution, which can significantly change the local landform. In China, geological disasters such as landslides cause serious casualties, economic losses and environmental damage every year. In recent years, many catastrophic landslide events have occurred outside the known potential hazard points, forcing China's geological disaster prevention and control ideas to refocus on the prediction and forecasting of regional disasters. Numerous studies at home and abroad have shown that establishing an early warning model by analyzing meteorological conditions such as extreme rainfall, snow and ice melting, and water level fluctuations in reservoirs and geological background conditions is a feasible way to achieve spatial prediction and time forecasting of landslides.

[0003] Geological disaster public meteorological early warning information services have been applied in many countries such as the United States, Italy, Japan, and Canada, and good results have been achieved in geological disaster prevention and control. However, there is a lack of high-precision regional geological disaster early warning experience in China. Currently, there are still problems such as large prediction granularity, rough accuracy, high false alarm rate and missed alarm rate, and the inability to apply early warning products. The reasons for these problems are various, including the accuracy and timeliness of rainfall data, the completeness and accuracy of disaster records, the accuracy of spatial prediction, the rationality of early warning unit division, and the correctness of various assumptions in the process of establishing an early warning model.

[0004] Therefore, it is necessary to propose a geological disaster early warning method based on information quantity to solve the above problems. Summary of the Invention

[0005] The present invention provides a geological disaster early warning method based on information quantity. By calculating the information quantity to determine the corresponding relationship between the threshold adjustment coefficient and the information quantity, the threshold adjustment coefficient of each watershed unit in the whole region is obtained, and then the rainfall threshold of each unit is obtained, realizing "one unit, one threshold", further realizing geological disaster meteorological early warning, and improving the accuracy and coverage rate of geological disasters.

[0006] The technical solution adopted by the present invention is as follows:

[0007] A geological disaster early warning method based on information quantity, comprising the following steps:

[0008] S1. Determine the reference rainfall threshold of the study area;

[0009] S2. Divide the study area based on ArcGIS to obtain a number of watershed units;

[0010] S3. Select multiple index factors, and calculate the information quantity of each index factor based on the information quantity model to obtain the information quantity of each watershed unit;

[0011] S4. Establish the relationship between the information volume and the threshold adjustment coefficient, and obtain the threshold adjustment coefficient of each watershed unit based on the information volume of each watershed unit;

[0012] S5. Obtain the rainfall threshold of each watershed unit based on the benchmark rainfall threshold of the study area and the threshold adjustment coefficient of each watershed unit;

[0013] S6. Obtain the forecast rainfall data, and divide the rainfall grades based on the rainfall threshold of each watershed unit;

[0014] S7. Determine the geological disaster warning result based on the rainfall grade.

[0015] Further, the S1 includes the following specific steps:

[0016] S11. Obtain the historical geological disaster data of the study area and the daily rainfall data when the historical geological disasters occurred;

[0017] S12. Determine the lower rainfall threshold value of the geological disaster points in the study area;

[0018] S13. Determine the upper rainfall threshold value of the geological disaster points in the study area;

[0019] S14. Calculate the effective rainfall;

[0020] S15. Determine the benchmark rainfall threshold of the study area based on the effective rainfall.

[0021] Further, the S12 includes the following specific steps:

[0022] 1) Obtain the probability distribution of geological disasters occurring at the geological disaster points in the study area;

[0023] 2) Based on the rainfall at the geological disaster points and the probability distribution of geological disasters occurring, obtain a curve in which the relationship between rainfall and the probability of geological disasters occurring is approximately exponential;

[0024] 3) Determine the inflection point value of the curve and use the inflection point value of the curve as the lower rainfall threshold value of the geological disaster points;

[0025] 4) Eliminate the geological disaster points below the inflection point value of the curve to obtain the first target geological disaster points.

[0026] Further, the S13 includes the following specific steps:

[0027] 1) Estimate the parameters μ and α based on the probability density function of the Gumbel distribution, and the calculation formula is as follows:

[0028]

[0029] Among them,

[0030]

[0031] In formulas (1) to (5), μ is the density parameter of the distribution, is the sample mean, is the mean of the standardized values, α is the scale parameter of the distribution, and δ N is 's standard deviation, and δ x is 's standard deviation, and n is the number of samples;

[0032] 2) Calculate the probability of a random variable ξ greater than x based on the parameters μ and α. The calculation formula is as follows:

[0033]

[0034] 3) When a certain probability value P or recurrence value T is given, find the intensity R of a certain extreme rainfall that occurs with probability P P or the intensity R of a certain extreme rainfall that occurs with a certain recurrence period T T , and the calculation formula is as follows:

[0035]

[0036] 4) Select the larger value of the intensity R of a certain extreme rainfall that occurs with probability P P or the intensity R of a certain extreme rainfall that occurs with a certain recurrence period T T as the upper limit rainfall value of the target geological hazard point;

[0037] 5) Eliminate the geological hazard points greater than the upper limit rainfall value to obtain the second target geological hazard points.

[0038] Furthermore, the S14 includes the following specific steps:

[0039] 1) Divide the rainfall pattern of the pre-disaster rainfall process, analyze the distribution characteristics of the daily rainfall before the disaster and its relationship with geological disasters, and determine the number of effective rainfall days:

[0040] 2) Analyze the correlation between the daily rainfall data and historical geological disaster data when historical geological disasters occurred, and determine the effective rainfall coefficient:

[0041] 3) Calculate the effective rainfall based on the number of effective rainfall days and the effective rainfall coefficient. The calculation formula is as follows:

[0042] R e = R0 + αR1 + α 2 R2 +... + α n R n (9)

[0043] In formula (9), Re is the effective rainfall; R0 is the rainfall of the current day; R n is the rainfall of the previous n days; α is the effective rainfall coefficient.

[0044] Further, the S15 includes the following specific steps:

[0045] 1) Calculate the rainfall intensity of the rainfall event inducing geological disasters based on the effective rainfall;

[0046] 2) Construct a rainfall intensity - duration days model based on the effective rainfall days and the rainfall intensity of the rainfall event inducing geological disasters;

[0047] 3) Plot the rainfall event information inducing geological disasters in a coordinate system and fit different percentile lines of the second target geological disaster point;

[0048] 4) The points where the different percentile lines intersect the vertical coordinate of the rainfall intensity - duration days model are used as rainfall thresholds;

[0049] 5) Divide the study area into intervals based on multiple rainfall thresholds and then determine the benchmark rainfall threshold of the study area.

[0050] Further, the information quantity of each watershed unit in the S3, its calculation formula is specifically as follows:

[0051]

[0052] In formula (10), I is the information quantity of a single watershed unit; Ln is the logarithmic function; F j is the number of units where the j index factor appears geological disasters in a single watershed unit; F is the total number of units with geological disasters in the study area; C j is the unit area of the j factor in a single watershed unit; C is the total unit area in the study area.

[0053] Further, the S4 includes the following specific steps:

[0054] S41. Sort the information quantities of each watershed unit in ascending order to obtain the minimum information quantity and the maximum information quantity;

[0055] S42. Set the threshold adjustment range, and determine the threshold adjustment coefficients of the minimum information quantity and the maximum information quantity, where the threshold adjustment coefficient corresponding to the minimum information quantity is increased by one threshold adjustment range, and the threshold adjustment coefficient corresponding to the maximum information quantity is decreased by one threshold adjustment range;

[0056] S43. Based on the minimum information quantity and its threshold adjustment coefficient, and the maximum information quantity and its threshold adjustment coefficient, establish the relationship between the information quantity and the threshold adjustment coefficient;

[0057] S44. Obtain the threshold adjustment coefficient of each watershed unit based on the information content of each watershed unit.

[0058] Further, the S5 includes the following specific steps:

[0059] S51. Obtain the reference rainfall threshold, and multiply the threshold adjustment coefficient of each watershed unit by the reference rainfall threshold respectively to obtain the rainfall threshold change amount of each watershed unit;

[0060] S52. Add the rainfall threshold change amount of each watershed unit to the reference threshold respectively and round up to obtain the rainfall threshold of each watershed unit.

[0061] Further, for the rainfall threshold of each watershed unit in S5, the calculation formula is as follows:

[0062] E i = E × λ i , i = 1, 2,..., k (11)

[0063] In formula (11), E i is the threshold value corrected for the i-th unit, E is the reference rainfall threshold for the entire area of the i-th unit, and λ i is the threshold adjustment coefficient of the i-th unit.

[0064] The present invention has the following beneficial effects compared with the prior art:

[0065] 1) Extreme rainfall events are excluded during the process of determining the reference rainfall threshold of the study area, reducing the false alarm rate and missed alarm rate, being applicable to the daily management of high-incidence areas of geological disasters, and laying a foundation for subsequent adjustment of the rainfall threshold;

[0066] 2) The study area is divided based on ArcGIS to achieve automated processing, improving the division efficiency of the study area and reducing labor costs;

[0067] 3) The information content model is adopted to effectively explore the potential relationship between multiple index factors, enhancing the scientific nature of the model;

[0068] 4) Based on the reference rainfall threshold of the study area, the information content and threshold adjustment coefficient of each watershed unit, the rainfall threshold of each watershed unit is obtained, realizing "one threshold for one unit", with strong pertinence and improving the accuracy and coverage rate of geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The present invention will be further described below in conjunction with the drawings and specific embodiments:

[0070] Figure 1 is the process schematic diagram of the present invention;

[0071] Figure 2It is the distribution map of the target geological disaster points after screening in the present invention;

[0072] Figure 3 It is the distribution map of the division of the watershed unit in the present invention;

[0073] Figure 4 It is the maximum slope map among the index factors of the present invention;

[0074] Figure 5 It is the maximum flow map among the index factors of the present invention;

[0075] Figure 6 It is the engineering geological rock group map among the index factors of the present invention;

[0076] Figure 7 It is the potential slope density grid map among the index factors of the present invention;

[0077] Figure 8 It is the four - adjacent area density map among the index factors of the present invention;

[0078] Figure 9 It is the mountain flood and debris flow hazard density map among the index factors of the present invention;

[0079] Figure 10 It is the geological disaster hazard density among the index factors of the present invention;

[0080] Figure 11 It is the flow chart of obtaining the threshold adjustment coefficient of the study area based on the information content in the present invention;

[0081] Figure 12 It is the relationship diagram between the information content and the threshold adjustment coefficient in the present invention;

[0082] Figure 13 It is the rainfall threshold distribution map of each watershed unit in the present invention. Detailed implementation mode

[0083] Embodiment

[0084] As Figures 1 to 13 shown, a geological disaster early - warning method based on information content includes the following steps:

[0085] S1. Determine the reference rainfall threshold of the study area;

[0086] Specifically, the said S1 includes the following steps:

[0087] S11. Obtain the historical geological disaster data of the study area and the daily rainfall data when the historical geological disasters occurred;

[0088] S12. Determine the lower limit rainfall value of the geological disaster points in the study area;

[0089] S13. Determine the upper limit rainfall value of the geological disaster points in the study area;

[0090] S14. Calculate the effective rainfall;

[0091] S15. Determine the benchmark rainfall threshold of the study area based on the effective rainfall.

[0092] Among them, the said S12 includes the following specific steps:

[0093] 1) Obtain the probability distribution of geological disasters occurring at the geological disaster points in the study area;

[0094] 2) Based on the rainfall at the geological disaster points and the probability distribution of geological disasters occurring, obtain a curve in which the rainfall and the probability of geological disasters occurring are approximately exponentially related;

[0095] 3) Determine the inflection point value of the curve and use the inflection point value of the curve as the lower limit rainfall value of the geological disaster points;

[0096] 4) Eliminate the geological disaster points below the inflection point value of the curve to obtain the first target geological disaster points;

[0097] The said S13 includes the following specific steps:

[0098] 1) Estimate the parameters μ and α based on the density function of the Gumbel distribution, and the calculation formula is as follows:

[0099]

[0100] Among them,

[0101]

[0102] In formulas (1) to (5), μ is the density parameter of the distribution, is the sample mean, is the mean of the standardized values, α is the scale parameter of the distribution, δ N is the standard deviation of, δ x is the standard deviation of, and n is the number of samples;

[0103] 2) Obtain the probability of a random variable ξ greater than x based on the parameters μ and α, and the calculation formula is as follows:

[0104]

[0105] 3) When a certain probability value P or recurrence value T is given, obtain the intensity R of a certain extreme rainfall that occurs with probability P P or the intensity R of a certain extreme rainfall that occurs with a certain recurrence period T T , and the calculation formula is as follows:

[0106]

[0107] 4) Select the intensity R of a certain extreme rainfall that appears with a probability P P or the intensity R of a certain extreme rainfall that appears with a certain recurrence period T T and take the larger value as the upper limit rainfall value of the target geological disaster point;

[0108] 5) Eliminate the geological disaster points greater than the upper limit rainfall value to obtain the second target geological disaster points;

[0109] The S14 includes the following specific steps:

[0110] 1) Divide the rainfall pattern of the pre-disaster rainfall process, analyze the daily rainfall distribution characteristics in the pre-disaster period and its relationship with geological disasters, and determine the effective rainfall days:

[0111] 2) Analyze the correlation between the daily rainfall data and historical geological disaster data when historical geological disasters occur, and determine the effective rainfall coefficient:

[0112] 3) Calculate the effective rainfall based on the effective rainfall days and the effective rainfall coefficient. The calculation formula is as follows:

[0113] R e = R0 + αR1 + α 2 R2 +... + α n R n (9)

[0114] In formula (9), R e is the effective rainfall; R0 is the daily rainfall; R n is the rainfall in the previous n days; α is the effective rainfall coefficient;

[0115] The S15 includes the following specific steps:

[0116] 1) Calculate the rainfall intensity of the rainfall event that induces geological disasters based on the effective rainfall;

[0117] 2) Construct a rainfall intensity-duration days model based on the effective rainfall days and the rainfall intensity of the rainfall event that induces geological disasters;

[0118] 3) Plot the rainfall event information that induces geological disasters in the coordinate system and fit different percentile lines of the second target geological disaster points;

[0119] 4) The points where different percentile lines intersect the vertical coordinate of the rainfall intensity-duration days model are used as rainfall thresholds;

[0120] 5) Divide the intervals of the study area based on multiple rainfall thresholds to determine the benchmark rainfall threshold of the study area;

[0121] In this embodiment, the percentile lines are four percentile lines with the occurrence probabilities of geological disaster rainfall being 30%, 50%, 70%, and 90%.

[0122] S2. Divide the study area based on ArcGIS to obtain several watershed units;

[0123] Among them, the specific division process includes basic DEM data, filling depressions in elevation data, calculating flow direction, calculating cumulative catchment area, setting thresholds, river linking, generating watersheds, and generating raster to polygon;

[0124] S3. Select multiple index factors, and calculate the information content of each index factor based on the information model to obtain the information content of each watershed unit;

[0125] Among them, the multiple index factors include maximum slope, maximum flow, engineering geological rock group, potential slope density, density of four adjacent regions, density of hidden dangers of mountain torrents and debris flows, and density of hidden dangers of geological disasters;

[0126] Specifically, for the information content of each watershed unit in S3, its calculation formula is as follows:

[0127]

[0128] In formula (10), I is the information content of a single watershed unit; Ln is the logarithmic function; F j is the number of units where geological disasters occur in a single watershed unit for the j index factor; F is the total number of units with geological disasters in the study area; C j is the unit area of the j factor in a single watershed unit; C is the total unit area in the study area;

[0129] S4. Construct the relationship between the information content and the threshold adjustment coefficient, and obtain the threshold adjustment coefficient of each watershed unit based on the information content of each watershed unit;

[0130] Specifically, S4 includes the following steps:

[0131] S41. Sort the information content of each watershed unit in ascending order to obtain the minimum information content and the maximum information content;

[0132] S42. Set the threshold adjustment range, and determine the threshold adjustment coefficients of the minimum information content and the maximum information content, where the threshold adjustment coefficient corresponding to the minimum information content is increased by one threshold adjustment range, and the threshold adjustment coefficient corresponding to the maximum information content is decreased by one threshold adjustment range;

[0133] S43. Based on the minimum information content and the threshold adjustment coefficient of the minimum information content, as well as the maximum information content and the threshold adjustment coefficient of the maximum information content, establish the relationship between the information content and the threshold adjustment coefficient;

[0134] S44. Obtain the threshold adjustment coefficient for each watershed unit based on the information amount of each watershed unit;

[0135] In this embodiment, the threshold adjustment range is set to 20%, and the threshold adjustment coefficients for the minimum information amount and the maximum information amount are 0.8 and 1.2 respectively;

[0136] S5. Obtain the rainfall threshold for each watershed unit based on the benchmark rainfall threshold of the study area and the threshold adjustment coefficient of each watershed unit;

[0137] Specifically, S5 includes the following specific steps:

[0138] S51. Obtain the benchmark rainfall threshold, and multiply the threshold adjustment coefficient of each watershed unit by the benchmark rainfall threshold respectively to obtain the rainfall threshold change amount of each watershed unit;

[0139] S52. Add the rainfall threshold change amount of each watershed unit to the benchmark threshold respectively and round up to obtain the rainfall threshold of each watershed unit;

[0140] Among them, the rainfall threshold of each watershed unit in S5 is calculated as follows:

[0141] E i = E × λ i , i = 1, 2,..., k (11)

[0142] In formula (11), E i is the threshold after correction for the i-th unit, E is the benchmark rainfall threshold for the whole region of the i-th unit, and λ i is the threshold adjustment coefficient for the i-th unit;

[0143] S6. Obtain the forecast rainfall data, and divide the rainfall grades based on the rainfall threshold of each watershed unit;

[0144] S7. Determine the geological disaster warning result based on the rainfall grade;

[0145] Among them, the rainfall grade is set to 5 grades, including no danger, low danger, medium danger, high danger and extremely high danger. The warning results set for the corresponding rainfall grades are no warning, blue warning, yellow warning, orange warning and red warning, so as to realize the meteorological warning of geological disasters.

[0146] In the process of determining the benchmark rainfall threshold of the study area, the present invention eliminates extreme rainfall events, reduces the false alarm rate and the missed alarm rate, is applicable to the daily management of high-incidence areas of geological disasters, and also lays a foundation for subsequent adjustment of the rainfall threshold; divides the study area based on ArcGIS to achieve automated processing, improves the division efficiency of the study area, and reduces labor costs; adopts the information amount model to effectively explore the potential relationships among multiple index factors and enhance the scientific nature of the model; obtains the rainfall threshold of each watershed unit based on the benchmark rainfall threshold of the study area, the information amount of each watershed unit, and the threshold adjustment coefficient, realizes "one threshold for one unit", has strong pertinence, and improves the accuracy and coverage rate of geological disasters.

[0147] The embodiments described above are only used to describe the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the principle and essence of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A geological disaster early warning method based on information volume, characterized in that: The specific steps include: S1. Determine the baseline rainfall threshold in the study area; S2, divide the study area into several watershed units based on ArcGIS; S3, selecting multiple indicator factors, calculating the information amount of each indicator factor based on the information amount model to obtain the information amount of each watershed unit; S4, constructing a relationship between the amount of information and the threshold adjustment coefficient, and obtaining the threshold adjustment coefficient of each watershed unit based on the amount of information of each watershed unit; S5, obtaining the rainfall threshold of each watershed unit based on the baseline rainfall threshold of the study area and the threshold adjustment coefficient of each watershed unit; S6. Obtaining forecast rainfall data, and classifying rainfall levels based on rainfall thresholds of each watershed unit; S7. Determine geological disaster warning results based on rainfall levels.

2. The method for early warning of geological disasters based on information volume according to claim 1, characterized in that: The S1 comprises the following specific steps: S11. Obtain historical geological disaster data of the study area and daily rainfall data when historical geological disasters occurred; S12. Determine the lower limit rainfall value of the geological disaster points in the study area; S13. Determine the upper limit rainfall value of the geological disaster points in the study area; S14, calculating effective rainfall; S15. Determine the baseline rainfall threshold of the study area based on the effective rainfall.

3. The method for early warning of geological disasters based on information volume according to claim 2 is characterized in that: The S12 includes the following specific steps: 1) Obtain the probability distribution of geological disasters at the geological disaster points in the study area; 2) Based on the distribution of rainfall at geological disaster points and the probability of geological disaster occurrence, a curve showing an approximately exponential relationship between rainfall and the probability of geological disaster occurrence is obtained; 3) Determine the inflection point value of the curve and use the inflection point value of the curve as the lower limit rainfall value of the geological disaster point; 4) Eliminate the geological disaster points below the inflection point value of the curve to obtain the first target geological disaster point.

4. The method for early warning of geological disasters based on information volume according to claim 2, characterized in that: The S13 comprises the following specific steps: 1) Estimate the parameters μ and α based on the density function of the Gumbel distribution. The calculation formula is as follows: in, In formulas (1) to (5), μ is the density parameter of the distribution, is the sample mean, is the mean of the standardized values, α is the scale parameter of the distribution, and δ N for The standard deviation of x for The standard deviation of , n is the number of samples; 2) Based on the parameters μ and α, the probability of a random variable ξ greater than x is calculated as follows: 3) When a probability value P or recurrence value T is given, find the intensity R of a certain extreme rainfall that occurs with probability P P Or the intensity R of a certain extreme rainfall occurring with a certain return period T T , the calculation formula is as follows: 4) Select the intensity R of a certain extreme rainfall with probability P P Or the intensity R of a certain extreme rainfall occurring with a certain return period T T The larger value of is taken as the upper limit rainfall value of the target geological disaster point; 5) Eliminate the geological disaster points with rainfall values ​​greater than the upper limit to obtain the second target geological disaster points.

5. The geological disaster early warning method based on information volume according to claim 2 is characterized in that: The S14 includes the following specific steps: 1) Classify the rainfall types of the early stage of geological disasters, analyze the distribution characteristics of daily rainfall in the early stage of geological disasters and its relationship with geological disasters, and determine the number of effective rainfall days: 2) Analyze the correlation between daily rainfall data and historical geological disaster data when historical geological disasters occurred, and determine the effective rainfall coefficient: 3) The effective rainfall is calculated based on the effective rainfall days and the effective rainfall coefficient. The calculation formula is as follows: R e =R0+αR1+α 2 R2+...+a n R n (9) In formula (9), R e is the effective rainfall; R0 is the rainfall on that day; R n is the rainfall in the previous n days; α is the effective rainfall coefficient.

6. The geological disaster early warning method based on information volume according to claim 2 is characterized in that: The S15 comprises the following specific steps: 1) Calculate the rainfall intensity of rainfall events that induce geological disasters based on effective rainfall; 2) Construct a rainfall intensity-duration model based on the effective rainfall days and the rainfall intensity of rainfall events that induce geological disasters; 3) Plotting the information of the rainfall event that induces geological disasters into the coordinate system, and fitting different percentile lines of the second target geological disaster point; 4) The points where different percentile lines intersect the ordinate of the rainfall intensity-duration days model are taken as rainfall thresholds; 5) Based on multiple rainfall thresholds, the study area is divided into intervals and the benchmark rainfall threshold of the study area is determined.

7. The geological disaster early warning method based on information volume according to claim 1 is characterized in that: The information volume of each watershed unit in S3 is calculated by the following formula: In formula (10), I is the information content of a single watershed unit; Ln is the logarithmic function; F j is the number of units with geological disasters in a single basin unit with index factor j; F is the total number of units with all geological disasters in the study area; C j is the unit area of ​​the j factor in a single watershed unit; C is the total unit area in the study area.

8. The geological disaster early warning method based on information volume according to claim 1 is characterized in that: The S4 comprises the following specific steps: S41, sorting the information amount of each watershed unit from small to large to obtain the minimum information amount and the maximum information amount; S42, setting a threshold adjustment range, determining the threshold adjustment coefficients of the minimum amount of information and the maximum amount of information, wherein the threshold adjustment coefficient corresponding to the minimum amount of information is increased by one threshold adjustment range, and the threshold adjustment coefficient corresponding to the maximum amount of information is decreased by one threshold adjustment range; S43, establishing a relationship between the amount of information and the threshold adjustment coefficient based on the minimum amount of information and the threshold adjustment coefficient of the minimum amount of information and the maximum amount of information and the threshold adjustment coefficient of the maximum amount of information; S44. Obtaining a threshold adjustment coefficient of each watershed unit based on the amount of information of each watershed unit.

9. The geological disaster early warning method based on information volume according to claim 1 is characterized in that: The S5 comprises the following specific steps: S51, obtaining a reference rainfall threshold, and multiplying the threshold adjustment coefficient of each watershed unit by the reference rainfall threshold to obtain a rainfall threshold change of each watershed unit; S52, adding the rainfall threshold change of each watershed unit to the reference threshold and rounding them up to obtain the rainfall threshold of each watershed unit.

10. The method for early warning of geological disasters based on information volume according to claim 7, characterized in that: The rainfall threshold of each watershed unit in S5 is calculated as follows: E i =E×λ i ,i=1,2,...,k(11) In formula (11), E i is the corrected threshold of the ith unit, E is the benchmark rainfall threshold of the ith unit, λ i is the threshold adjustment coefficient of the i-th unit.

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