CN110738378B - Prediction method and prediction system for debris flow disasters in mountain area - Google Patents
Prediction method and prediction system for debris flow disasters in mountain area Download PDFInfo
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Abstract
The invention provides a prediction method and a prediction system for a mountain debris flow disaster, wherein the prediction method comprises the following steps: acquiring historical data of occurrence of the debris flow disaster in the area to be predicted, and acquiring a debris flow disaster prediction model according to the historical data of occurrence of the debris flow disaster; the debris flow disaster prediction model comprises debris flow disaster prediction sub-models in all seasons, and the debris flow disaster prediction sub-models comprise the relationship between the intensity of debris flow and the coverage proportion of surface vegetation, the gradient, the thickness of ground soil and the rainfall; detecting the surface vegetation coverage proportion, gradient and ground soil thickness of the area to be predicted, and acquiring rainfall information of the area to be predicted; and substituting the earth surface vegetation coverage proportion, gradient, ground soil thickness and rainfall information of the area to be predicted into a mud-rock flow disaster prediction sub-model of the corresponding season to obtain the mud-rock flow intensity of the area to be predicted. The technical scheme provided by the invention can accurately calculate the intensity of the debris flow in the area to be predicted.
Description
Technical Field
The invention belongs to the technical field of geological disaster prediction, and particularly relates to a mountain debris flow disaster prediction method and a mountain debris flow disaster prediction system.
Background
The debris flow is a flood formed by saturated dilution of a soft soil mountain containing sand and stone by flood water, the area, the volume and the flow rate of the debris flow are large, the landslide is a small area of the diluted soil mountain, and the typical debris flow consists of viscous slurry which is suspended with coarse solid scraps and is rich in silt and clay. Under proper topography conditions, a large amount of water body soaks the solid accumulation substances in hillsides or ditch beds, so that the stability of the solid accumulation substances is reduced, and the solid accumulation substances saturated with water move under the action of self gravity, so that debris flow is formed. Debris flow is a disastrous geological phenomenon. Usually, the debris flow burst suddenly and fiercely can carry huge stones. It is extremely destructive because of its high speed of travel, having a strong energy.
The whole process of the debris flow is generally only a few hours, and short is only a few minutes, so that the debris flow is a natural disaster widely distributed in areas with special topography and landform conditions around the world. The water-sand-bearing water-gas mixed flow is a soil, water and gas mixed flow which is excited by water sources such as storm, ice and snow melting and contains a large amount of silt stones and is between sand-carrying water flow and landslide on mountainous valleys or mountainous slope surfaces. Debris flow mostly occurs with mountain floods. The flood is different from general flood in that the flood contains a sufficient amount of solid scraps such as mud, sand and stone, and the volume content of the solid scraps is at least 15 percent and can reach about 80 percent, so the flood has more destructive power than the flood. When debris flow occurs, traffic facilities such as highways, railways and the like are often destroyed, and villages and towns are even destroyed, so that huge economic losses are caused. Therefore, in order to reduce the economic loss, it is necessary to detect and judge the area where the debris flow may occur, and predict the probability of occurrence of the debris flow and the intensity of the debris flow.
In the prior art, the method for predicting the debris flow is judged according to experience, and if the debris flow is caused by heavy rain in summer in the last year, the debris flow is also caused by heavy rain in summer in the present year. It can be seen that the reliability of debris flow prediction in the prior art is poor.
Disclosure of Invention
The invention provides a method and a system for predicting debris flow disasters in mountain areas, which are used for solving the problem of poor reliability in debris flow prediction in the prior art.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a prediction method for mountain debris flow disasters comprises the following steps:
(1) Acquiring historical data of occurrence of the debris flow disaster in the area to be predicted, and acquiring a debris flow disaster prediction model according to the historical data of occurrence of the debris flow disaster;
the mud-rock flow disaster prediction model comprises mud-rock flow disaster prediction sub-models in all seasons, and the mud-rock flow disaster prediction sub-models comprise relations between mud-rock flow intensity and surface vegetation coverage proportion, gradient, ground soil thickness and rainfall;
(2) Detecting the surface vegetation coverage proportion, gradient and ground soil thickness of the area to be predicted, and acquiring rainfall information of the area to be predicted;
(3) And substituting the earth surface vegetation coverage proportion, gradient, ground soil thickness and rainfall information of the area to be predicted into a mud-rock flow disaster prediction sub-model of the corresponding season to obtain the mud-rock flow intensity of the area to be predicted.
According to the technical scheme provided by the invention, a debris flow prediction model is established according to the relationship between the surface vegetation coverage proportion, the gradient, the ground soil thickness and the rainfall information and the debris flow intensity, and the intensity of the debris flow in the area to be predicted is calculated by combining the surface vegetation coverage proportion, the gradient, the ground soil thickness and the rainfall of the area to be predicted. According to the technical scheme provided by the invention, the influence factors of the debris flow are fully considered, and the intensity of the debris flow is predicted in a quantitative manner, so that the obtained prediction result is more accurate.
Further, the debris flow predictor model is as follows:
T＝Ax+By-Cz-Dh
wherein T is the mud-rock flow intensity, x is the gradient value, y is the rainfall, z is the vegetation coverage proportion, h is the ground soil thickness, and A, B, C, D is the gradient value, the rainfall, the vegetation coverage proportion and the weight of the ground soil thickness respectively.
By adopting the debris flow predictor model, not only can the influence of various factors be fully considered, but also the calculated amount can be reduced.
Further, the gradient value, the rainfall, the vegetation coverage ratio and the weight of the ground soil thickness are calculated by adopting a linear regression method according to historical data.
And according to the historical data, a linear regression method is adopted, and the calculated gradient value, rainfall, vegetation coverage proportion and weight of the ground soil thickness are more in line with the actual situation.
Further, when the information of the coverage proportion, gradient, ground soil thickness and rainfall of the surface vegetation of the area to be predicted is substituted into the mud-rock flow disaster prediction sub-model of the corresponding season, normalization processing is carried out on the mud-rock flow disaster prediction sub-model.
The normalization processing can ensure the consistency of the data and reduce the calculated amount.
Further, the method for obtaining the coverage proportion of the vegetation on the surface of the area to be predicted comprises the following steps: firstly, obtaining an overall top view of an area to be predicted, then identifying a part covered by vegetation, and finally calculating the vegetation coverage proportion of the surface of the area to be predicted.
The method for calculating the vegetation coverage proportion of the ground surface of the area to be predicted by adopting the image method is simple in calculation process, and the obtained result is more accurate.
A system for predicting a mountain debris flow disaster, comprising a processor and a memory, said memory having stored thereon a computer program for execution on said processor; the processor, when executing the computer program, performs the steps of:
(1) Acquiring historical data of occurrence of the debris flow disaster in the area to be predicted, and acquiring a debris flow disaster prediction model according to the historical data of occurrence of the debris flow disaster;
the mud-rock flow disaster prediction model comprises mud-rock flow disaster prediction sub-models in all seasons, and the mud-rock flow disaster prediction sub-models comprise relations between mud-rock flow intensity and surface vegetation coverage proportion, gradient, ground soil thickness and rainfall;
(2) Detecting the surface vegetation coverage proportion, gradient and ground soil thickness of the area to be predicted, and acquiring rainfall information of the area to be predicted;
(3) And substituting the earth surface vegetation coverage proportion, gradient, ground soil thickness and rainfall information of the area to be predicted into a mud-rock flow disaster prediction sub-model of the corresponding season to obtain the mud-rock flow intensity of the area to be predicted.
According to the technical scheme provided by the invention, a debris flow prediction model is established according to the relationship between the surface vegetation coverage proportion, the gradient, the ground soil thickness and the rainfall information and the debris flow intensity, and the intensity of the debris flow in the area to be predicted is calculated by combining the surface vegetation coverage proportion, the gradient, the ground soil thickness and the rainfall of the area to be predicted. According to the technical scheme provided by the invention, the influence factors of the debris flow are fully considered, and the intensity of the debris flow is predicted in a quantitative manner, so that the obtained prediction result is more accurate.
Further, the debris flow predictor model is as follows:
T＝Ax+By-Cz-Dh
wherein T is the mud-rock flow intensity, x is the gradient value, y is the rainfall, z is the vegetation coverage proportion, h is the ground soil thickness, and A, B, C, D is the gradient value, the rainfall, the vegetation coverage proportion and the weight of the ground soil thickness respectively.
By adopting the debris flow predictor model, not only can the influence of various factors be fully considered, but also the calculated amount can be reduced.
Further, the gradient value, the rainfall, the vegetation coverage ratio and the weight of the ground soil thickness are calculated by adopting a linear regression method according to historical data.
And according to the historical data, a linear regression method is adopted, and the calculated gradient value, rainfall, vegetation coverage proportion and weight of the ground soil thickness are more in line with the actual situation.
Further, when the information of the coverage proportion, gradient, ground soil thickness and rainfall of the surface vegetation of the area to be predicted is substituted into the mud-rock flow disaster prediction sub-model of the corresponding season, normalization processing is carried out on the mud-rock flow disaster prediction sub-model.
The normalization processing can ensure the consistency of the data and reduce the calculated amount.
Further, the method for obtaining the coverage proportion of the vegetation on the surface of the area to be predicted comprises the following steps: firstly, obtaining an overall top view of an area to be predicted, then identifying a part covered by vegetation, and finally calculating the vegetation coverage proportion of the surface of the area to be predicted.
The method for calculating the vegetation coverage proportion of the ground surface of the area to be predicted by adopting the image method is simple in calculation process, and the obtained result is more accurate.
Drawings
Fig. 1 is a flowchart of a method for predicting a mountain debris flow disaster according to an embodiment of the present invention.
Detailed Description
Method embodiment:
the embodiment provides a prediction method for debris flow disasters in mountain areas, which aims to solve the problem of poor reliability in debris flow prediction in the prior art.
The flow of the prediction method for the mountain debris flow disaster provided by the embodiment is shown in fig. 1, and the method comprises the following steps:
(1) And acquiring historical data of occurrence of the debris flow in the area to be predicted, and establishing a prediction model of the debris flow disaster in the area to be predicted according to the historical data.
And obtaining geological disaster historical data of the area to be predicted, extracting data of occurrence of the debris flow disaster from the geological disaster historical data, and obtaining information of the coverage proportion, gradient and ground soil thickness of the earth surface vegetation of the area where the debris flow occurs, time of occurrence of the debris flow and rainfall condition during occurrence of the debris flow, so as to obtain a corresponding relation between the intensity of the debris flow of the area to be predicted and the coverage proportion, gradient, ground soil thickness and rainfall intensity of the earth surface vegetation.
On one hand, the occurrence of the debris flow is related to seasons, on the other hand, the intensity of the debris flow is lower as the area covered by the surface vegetation is wider, the intensity of the debris flow is lower as the gradient of the area is lower, the intensity of the debris flow is lower as the ground soil is thicker, and the intensity of the debris flow is higher as the rainfall is larger, so that the intensity of the debris flow in the debris flow disaster prediction model established by the embodiment is inversely related to the coverage area of the surface vegetation and the thickness of the ground soil, and is positively related to the gradient and the rainfall.
The debris flow disaster prediction model established in the embodiment is classified according to seasons, and each season corresponds to one predictor model. The predictor model in this embodiment is:
T＝Ax+By-Cz-Dh
wherein T is the mud-rock flow intensity, x is the gradient value, y is the rainfall, z is the vegetation coverage proportion, h is the ground soil thickness, and A, B, C, D is the gradient value, the rainfall, the vegetation coverage proportion and the weight of the ground soil thickness respectively.
The concrete numerical method for obtaining the weight A, B, C, D of the debris flow disaster model is as follows:
firstly, normalization processing is carried out: the strongest intensity of the debris flow in the historical data is set as 1, the weakest intensity is set as 0, and the rest of the debris flow is valued according to the proportion between 0 and 1 according to the intensity degree. And setting the gradient value as 0 when the gradient is 0, setting the gradient value as 1 when the gradient is 90 degrees, and taking the gradient value in the historical data between 0 and 1 according to the proportion. The most rainfall is set as 1 in the historical data, the least rainfall is set as 0, and the historical data of the rest rainfall takes values between 0 and 1 according to the proportion. The thickness of the soil in the historical data is set to be 1, the thickness of the soil in the historical data is set to be 0, and the thickness of the soil in the historical data is set to be 0 to 1 according to the proportion.
And then selecting four groups of data from the normalized data, and substituting the four groups of data into the predictor model to obtain the A, B, C, D value in the debris flow predictor model in each season, thereby obtaining the debris flow predictor model in each season.
(2) And detecting the information of the coverage proportion, gradient and ground soil thickness of the surface vegetation of the area to be predicted.
In the embodiment, an unmanned aerial vehicle is adopted to carry out aerial photography on the area to be predicted, so that a overlooking image of the whole area to be predicted is obtained; and then extracting the vegetation coverage area by adopting an image recognition method, so as to obtain the surface vegetation coverage proportion of the whole area to be predicted. The gradient information of the area to be predicted is obtained through actual measurement. The ground soil thickness information is obtained by adopting a sampling method, namely a plurality of sampling points are uniformly arranged in a region to be predicted, and the soil thickness of each sampling point is detected; then judging whether the difference between the soil thickness of the sampling point and the soil thickness of other sampling points is larger than a set value, if so, deleting the soil thickness of the sampling point; and finally, calculating the average value of the soil thickness of each area, and taking the average value as the soil thickness of the area.
(3) And obtaining the rainfall of the area to be predicted through weather forecast of the Internet, carrying out normalization processing on the obtained rainfall, the ground soil thickness and the gradient of the area to be predicted, and substituting the input after the normalization processing into a mud-rock flow prediction sub-model of the corresponding season to obtain the mud-rock flow intensity of the area to be predicted.
System embodiment:
the embodiment provides a prediction system for mountain debris flow disasters, which comprises a processor and a memory, wherein a computer program used for being executed on the processor is stored in the memory; when the processor executes the computer program, the method for predicting the mountain debris flow disasters is realized.
Claims (6)
1. The method for predicting the debris flow disaster in the mountain area is characterized by comprising the following steps of:
(1) Acquiring historical data of occurrence of the debris flow disaster in the area to be predicted, and acquiring a debris flow disaster prediction model according to the historical data of occurrence of the debris flow disaster;
the mud-rock flow disaster prediction model comprises mud-rock flow disaster prediction sub-models in all seasons, and the mud-rock flow disaster prediction sub-models comprise relations between mud-rock flow intensity and surface vegetation coverage proportion, gradient, ground soil thickness and rainfall;
(2) Detecting the surface vegetation coverage proportion, gradient and ground soil thickness of the area to be predicted, and acquiring rainfall information of the area to be predicted;
(3) Substituting the earth surface vegetation coverage proportion, gradient, ground soil thickness and rainfall information of the area to be predicted into a mud-rock flow disaster prediction sub-model of the corresponding season to obtain mud-rock flow intensity of the area to be predicted;
wherein, the mud-rock flow predictor model is:
T＝Ax+By-Cz-Dh
wherein T is the intensity of the debris flow, x is the gradient value, y is the rainfall, z is the vegetation coverage ratio, h is the ground soil thickness, and A, B, C, D is the gradient value, the rainfall, the vegetation coverage ratio and the weight of the ground soil thickness respectively;
and the gradient value, the rainfall, the vegetation coverage proportion and the weight of the ground soil thickness are calculated by adopting a linear regression method according to the historical data.
2. The method for predicting the debris flow disasters in the mountain area according to claim 1, wherein the surface vegetation coverage ratio, the gradient, the ground soil thickness and the rainfall information of the area to be predicted are substituted into the debris flow disaster predictor model in the corresponding season, and are normalized.
3. The method for predicting the mountain debris flow disasters according to claim 1, wherein the method for obtaining the coverage ratio of the surface vegetation in the area to be predicted is as follows: firstly, obtaining an overall top view of an area to be predicted, then identifying a part covered by vegetation, and finally calculating the vegetation coverage proportion of the surface of the area to be predicted.
4. A system for predicting a mountain debris flow disaster, comprising a processor and a memory, said memory having stored thereon a computer program for execution on said processor; wherein the processor, when executing the computer program, performs the steps of:
(1) Acquiring historical data of occurrence of the debris flow disaster in the area to be predicted, and acquiring a debris flow disaster prediction model according to the historical data of occurrence of the debris flow disaster;
the mud-rock flow disaster prediction model comprises mud-rock flow disaster prediction sub-models in all seasons, and the mud-rock flow disaster prediction sub-models comprise relations between mud-rock flow intensity and surface vegetation coverage proportion, gradient, ground soil thickness and rainfall;
(2) Detecting the surface vegetation coverage proportion, gradient and ground soil thickness of the area to be predicted, and acquiring rainfall information of the area to be predicted;
(3) Substituting the earth surface vegetation coverage proportion, gradient, ground soil thickness and rainfall information of the area to be predicted into a mud-rock flow disaster prediction sub-model of the corresponding season to obtain mud-rock flow intensity of the area to be predicted;
wherein, the mud-rock flow predictor model is:
T＝Ax+By-Cz-Dh
wherein T is the intensity of the debris flow, x is the gradient value, y is the rainfall, z is the vegetation coverage ratio, h is the ground soil thickness, and A, B, C, D is the gradient value, the rainfall, the vegetation coverage ratio and the weight of the ground soil thickness respectively;
and the gradient value, the rainfall, the vegetation coverage ratio and the weight of the ground soil thickness are calculated by adopting a linear regression method according to the historical data.
5. The system for predicting debris flow disasters in mountain areas according to claim 4, wherein the surface vegetation coverage ratio, the gradient, the ground soil thickness and the rainfall information of the areas to be predicted are substituted into the debris flow disaster prediction sub-model in the corresponding seasons, and are normalized.
6. The system for predicting a mountain debris flow disaster according to claim 4, wherein the method for obtaining the coverage ratio of the surface vegetation in the area to be predicted comprises the following steps: firstly, obtaining an overall top view of an area to be predicted, then identifying a part covered by vegetation, and finally calculating the vegetation coverage proportion of the surface of the area to be predicted.
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| CN103472497A (en) * | 2013-08-23 | 2013-12-25 | 成都理工大学 | Channel-started debris flow outbreak scale calculation method and application |
| CN105260625A (en) * | 2015-11-19 | 2016-01-20 | 阿坝师范学院 | Landslide geological disaster early warning pushing method |
| CN106645650A (en) * | 2017-02-20 | 2017-05-10 | 长沙市玖车测控技术有限公司 | Water and soil loss monitoring and early warning system with forecast function |
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| CN105260625A (en) * | 2015-11-19 | 2016-01-20 | 阿坝师范学院 | Landslide geological disaster early warning pushing method |
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