CN111144656A - Disaster evaluation analysis method based on GIS - Google Patents
Disaster evaluation analysis method based on GIS Download PDFInfo
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- CN111144656A CN111144656A CN201911377683.1A CN201911377683A CN111144656A CN 111144656 A CN111144656 A CN 111144656A CN 201911377683 A CN201911377683 A CN 201911377683A CN 111144656 A CN111144656 A CN 111144656A
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Abstract
The invention discloses a disaster evaluation and analysis method based on a GIS (geographic information System), which relates to the technical field of geological disaster analysis and evaluation, and comprises the steps of collecting historical meteorological data, geological information basic data and historical geological disaster data of an observation place; preprocessing, namely dividing the data into a training data set and a testing data set; constructing a GIS-based neural network analysis model, training the analysis model, and reversely optimizing model parameters; testing the analysis model and outputting the trained analysis model; acquiring meteorological data and geological data of an observation place in real time, analyzing the meteorological data and the geological data by using a trained analysis model, and outputting a real-time analysis result; the early warning grading model is constructed by utilizing the real-time analysis result, the historical meteorological data, the historical geological disaster data and the geological information basic data, and the early warning grading model outputs the current best early warning measure according to the real-time analysis result.
Description
Technical Field
The invention relates to the technical field of geological disaster analysis and evaluation, in particular to a disaster evaluation and analysis method based on a GIS.
Background
Geographic Information Systems (GIS) are sometimes also referred to as "Geographic Information systems". It is a specific and very important spatial information system. The system is a technical system for collecting, storing, managing, operating, analyzing, displaying and describing relevant geographic distribution data in the whole or partial earth surface (including the atmosphere) space under the support of a computer hardware and software system. GIS is a computer-based tool that can analyze and process spatial information, and in short, map and analyze phenomena and events occurring on the earth. GIS technology integrates this unique visualization and geographic analysis function of maps with general database operations (e.g., queries and statistical analysis, etc.).
At present, GIS is more and more popular in the meteorological field, more meteorological workers have recognized the use value of the geographic information system technology, and the geographic information system plays an irreplaceable role in the aspects of meteorological data management, agricultural climate zoning, meteorological disaster assessment, atmospheric composition change trend prediction, meteorological modeling analysis and evaluation and auxiliary decision providing. With the development of meteorological services and the continuous maturity and perfection of 3S technology, GIS technology can be widely and deeply applied in the meteorological field, and the meteorological services are bound to begin a rapid development stage. As an important information technology, the GIS has been deeply applied to weather services such as weather forecast application, weather modification, geological disaster weather forecast, weather disaster assessment, weather data management and visual analysis, and integrated weather service systems, and its technology has been expanded from the traditional two-dimensional to three-dimensional. The MapGIS IGSS 3D is oriented to professional three-dimensional GIS application requirements, management and analysis of meteorological data are achieved, meteorological achievements are visually represented in a three-dimensional visualization mode, the meteorological industry informatization meets the requirements of meteorological services, scientific research and services on meteorological information resource sharing, and accuracy of meteorological analysis and scientificity of emergency decision are improved. China has wide regions, complex topography, large difference of weather space-time distribution and frequent natural disasters. From ancient times to present, people in China both benefit from weather and suffer from weather. With the change of the environment, the meteorological problem is more and more emphasized, and the advanced 3D GIS technology is introduced on the basis of the traditional data collection, planning and management technology, so that the information acquisition and updating can be accelerated, and the development of the meteorological industry is promoted.
The existing disaster monitoring and early warning method is mainly characterized in that a geological field is monitored through electronic equipment such as a CCD (charge coupled device) sensor, such as a camera or a video camera, a rain gauge and the like, whether a disaster occurs and the scale of the occurrence are judged visually, however, the method cannot be used for early warning the impending disaster and analyzing and evaluating the occurring disaster, and effective protective rescue measures are provided.
Disclosure of Invention
The invention aims to: the invention provides a disaster evaluation and analysis method based on a GIS (geographic information system) and aims to solve the problems that in the prior art, early warning cannot be carried out on an impending disaster, analysis and evaluation cannot be carried out on the occurring disaster, effective protection and rescue measures cannot be provided, and serious damage is caused to the disaster.
The invention specifically adopts the following technical scheme for realizing the purpose:
a disaster evaluation and analysis method based on GIS comprises the following steps:
s1: collecting historical meteorological data, geological information basic data and historical geological disaster data of an observation area, and constructing a multi-source GIS database of the observation area based on the geological information basic data;
s2: preprocessing historical meteorological data and historical geological disaster data, and then randomly dividing the preprocessed historical meteorological data and historical geological disaster data into a training data set and a testing data set according to a preset proportion;
s3: constructing a GIS-based neural network analysis model, training the analysis model by using data in a training data set, and reversely optimizing model parameters;
s4: testing the analysis model by using the test data set until the analysis accuracy of the analysis model reaches a threshold value, and outputting a trained analysis model;
s5: acquiring meteorological data and geological data of an observation place in real time, analyzing the meteorological data and the geological data by using a trained analysis model, outputting a real-time analysis result to obtain a result of whether a disaster occurs, and if the disaster occurs, evaluating the real-time analysis result by the type, the damage strength and the damage capability of the current disaster;
s6: and constructing an early warning hierarchical model by utilizing the real-time analysis result, the historical meteorological data, the historical geological disaster data and the geological information basic data, and outputting the current best early warning measure by the early warning hierarchical model according to the real-time analysis result.
Further, in S1, the historical meteorological data includes, but is not limited to: observing the rainfall of the ground in four seasons, temperature value, humidity value, high-altitude cyclone pressure, wind direction, wind power and air pressure around a wind field.
Further, in S1, the geological information basic data includes, but is not limited to: observation of geographical location of the land, socioeconomic profile, river water system, geological appearance, soil type and distribution, and land use type.
Further, in S1, the historical geological disaster data includes, but is not limited to: flood and river water flow, critical water amount of debris flow disasters, basin area, the moving distance of the whole body to the lower part of the slope due to shear displacement generated by a soft structural surface or structural zone of the mountain slope, the moving distance of the crust and the temperature in the crust.
Further, in S2, the preprocessing is performed on the historical meteorological data and the historical geological disaster data, specifically: and normalizing the historical meteorological data and the historical geological disaster data, and then performing correlation matching on the normalized historical meteorological data and the normalized historical geological disaster data.
Further, the GIS-based neural network analysis model has an input layer, three convolutional layers, two depth separable convolutional layers, and an output layer.
Further, in S3, the FI evaluation model is used for the reverse optimization.
Furthermore, the early warning grading model learns historical meteorological data and historical geological disaster data, and an optimal early warning measure is made based on the evaluation of the category, the destruction strength and the destruction capability of the current disaster in the real-time analysis result and geological information basic data.
The invention has the following beneficial effects:
1. the invention evaluates and analyzes the meteorological data and geological data monitored in real time through the GIS-based neural network analysis model, and the accuracy of the real-time analysis result is ensured because the analysis model is strictly trained.
2. According to the method, the real-time analysis results are graded through the early warning grading model, the disaster grade is established according to two different disaster types, damage strength and damage capacity of landslide and debris flow, the optimal early warning measure is output, and the loss caused by the disaster is reduced to the minimum.
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FIG. 1 is a schematic flow diagram of the process of the present invention.
Detailed Description
For a better understanding of the present invention by those skilled in the art, the present invention will be described in further detail below with reference to the accompanying drawings and the following examples.
Example 1
As shown in fig. 1, the present embodiment provides a method for evaluating and analyzing a disaster based on a GIS, which includes the following steps:
s1: collecting historical meteorological data, geological information basic data and historical geological disaster data of an observation area, and constructing a multi-source GIS database of the observation area based on the geological information basic data;
wherein, the historical meteorological data includes but is not limited to: observing the rainfall of the ground in four seasons, temperature value, humidity value, high-altitude cyclone pressure, wind direction, wind power and air pressure around a wind field; geological information base data includes, but is not limited to: observing geographical locations of the land, socioeconomic profiles, river systems, geological appearances, soil types and distributions, and land utilization types; historical geological disaster data includes, but is not limited to: flood disaster river water flow, debris flow disaster critical water quantity, basin area, distance of moving the whole body to the lower part of the slope by shear displacement generated by a soft structural surface or structural zone of the mountain slope, distance of moving the earth crust and temperature in the earth crust;
s2: preprocessing historical meteorological data and historical geological disaster data, randomly dividing the preprocessed historical meteorological data and historical geological disaster data into a training data set and a testing data set according to a preset proportion, specifically, normalizing the historical meteorological data and the historical geological disaster data, and then performing association matching on the normalized historical meteorological data and the historical geological disaster data, wherein the association matching is to enable an analysis model to be capable of pre-judging corresponding geological disasters when corresponding meteorological conditions occur, so that the training accuracy of the analysis model is improved, and finally dividing the normalized historical meteorological data and the normalized historical geological disaster data which are matched into the training data set and the testing data set according to the proportion of 7: 3;
s3: constructing a GIS-based neural network analysis model, training the analysis model by using data in a training data set, optimizing network parameters of the analysis model by using an F1 evaluation model, and reversely optimizing model parameters;
the GIS-based neural network analysis model is provided with an input layer, three convolutional layers, two depth separable convolutional layers and an output layer, wherein the convolutional layers comprise 32 convolutional kernels, the size of each convolutional kernel is 3 x 3, and the step length is 1; the depth separable convolutional layer comprises 256 convolutional kernels, each convolutional kernel being 3 x 3 in size;
the training process is as follows: initializing analysis model network parameters and loss functions, inputting data in a training data set into an analysis model, calculating to obtain actual output, loss functions of each layer and an F1 evaluation model, minimizing the loss functions in a random gradient decreasing mode, reversely calculating the analysis model network parameters by utilizing the minimized loss functions, and updating the model parameters until the F1 evaluation model results are ideal;
s4: testing the analysis model by using the test data set until the analysis accuracy of the analysis model reaches a threshold, wherein the threshold is set to be 85% in the embodiment, and outputting the trained analysis model;
s5: acquiring meteorological data and geological data of an observation place in real time, analyzing the meteorological data and the geological data by using a trained analysis model, outputting a real-time analysis result to obtain a result of whether a disaster occurs, displaying the real-time analysis result in a multi-source GIS database, and if the disaster occurs, evaluating the type, damage strength and damage capacity of the current disaster according to the real-time analysis result;
s6: and constructing an early warning grading model by using the real-time analysis result, the historical meteorological data, the historical geological disaster data and the geological information basic data, learning the historical meteorological data and the historical geological disaster data by using the early warning grading model, making an optimal early warning measure based on the evaluation on the category, the destruction strength and the destruction capability of the current disaster in the real-time analysis result and the geological information basic data, and outputting the current optimal early warning measure by using the early warning grading model according to the real-time analysis result.
The above description is only a preferred embodiment of the present invention, and not intended to limit the present invention, the scope of the present invention is defined by the appended claims, and all structural changes that can be made by using the contents of the description and the drawings of the present invention are intended to be embraced therein.
Claims (8)
1. A disaster evaluation analysis method based on GIS is characterized by comprising the following steps:
s1: collecting historical meteorological data, geological information basic data and historical geological disaster data of an observation area, and constructing a multi-source GIS database of the observation area based on the geological information basic data;
s2: preprocessing historical meteorological data and historical geological disaster data, and then randomly dividing the preprocessed historical meteorological data and historical geological disaster data into a training data set and a testing data set according to a preset proportion;
s3: constructing a GIS-based neural network analysis model, training the analysis model by using data in a training data set, and reversely optimizing model parameters;
s4: testing the analysis model by using the test data set until the analysis accuracy of the analysis model reaches a threshold value, and outputting a trained analysis model;
s5: acquiring meteorological data and geological data of an observation place in real time, analyzing the meteorological data and the geological data by using a trained analysis model, outputting a real-time analysis result to obtain a result of whether a disaster occurs, and if the disaster occurs, evaluating the real-time analysis result by the type, the damage strength and the damage capability of the current disaster;
s6: and constructing an early warning hierarchical model by utilizing the real-time analysis result, the historical meteorological data, the historical geological disaster data and the geological information basic data, and outputting the current best early warning measure by the early warning hierarchical model according to the real-time analysis result.
2. A GIS-based disaster evaluation analysis method according to claim 1, wherein in S1, the historical meteorological data includes but is not limited to: observing the rainfall of the ground in four seasons, temperature value, humidity value, high-altitude cyclone pressure, wind direction, wind power and air pressure around a wind field.
3. The GIS-based disaster evaluation analysis method according to claim 1, wherein in S1, the geological information basic data includes but is not limited to: observation of geographical location of the land, socioeconomic profile, river water system, geological appearance, soil type and distribution, and land use type.
4. A GIS-based disaster evaluation analysis method according to claim 1, wherein in S1, the historical geological disaster data includes but is not limited to: flood and river water flow, critical water amount of debris flow disasters, basin area, the moving distance of the whole body to the lower part of the slope due to shear displacement generated by a soft structural surface or structural zone of the mountain slope, the moving distance of the crust and the temperature in the crust.
5. A method for disaster evaluation and analysis based on GIS according to claim 1, wherein in S2, the historical meteorological data and the historical geological disaster data are preprocessed, specifically: and normalizing the historical meteorological data and the historical geological disaster data, and then performing correlation matching on the normalized historical meteorological data and the normalized historical geological disaster data.
6. The method of claim 1, wherein the GIS based neural network analysis model comprises an input layer, a three-layer convolutional layer, a two-layer depth separable convolutional layer, and an output layer.
7. The GIS-based disaster evaluation analysis method according to claim 1, wherein in S3, an FI evaluation model is used for reverse optimization.
8. The GIS-based disaster evaluation and analysis method according to claim 1, wherein the early warning hierarchical model learns historical meteorological data and historical geological disaster data, and makes optimal early warning measures based on the evaluation of the category, destruction strength and destruction capability of the current disaster in the real-time analysis results and geological information basic data.
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| CN112382043A (en) * | 2020-10-23 | 2021-02-19 | 杭州翔毅科技有限公司 | Disaster early warning method, device, storage medium and device based on satellite monitoring |
| CN112508732A (en) * | 2020-11-05 | 2021-03-16 | 生态环境部南京环境科学研究所 | Water environment safety early warning prediction method |
| CN112735094A (en) * | 2020-12-17 | 2021-04-30 | 中国地质环境监测院 | Geological disaster prediction method and device based on machine learning and electronic equipment |
| CN113159532A (en) * | 2021-04-01 | 2021-07-23 | 兰州天泉信息科技有限公司 | Intelligent fire-fighting command system-oriented auxiliary decision-making key technology |
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| CN113379326A (en) * | 2020-07-08 | 2021-09-10 | 贵州电网有限责任公司 | Power grid disaster emergency drilling management system based on deep neural network |
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| CN111898802A (en) * | 2020-07-08 | 2020-11-06 | 贵州电网有限责任公司 | Power grid disaster situation evaluation method based on deep neural network |
| CN112382043A (en) * | 2020-10-23 | 2021-02-19 | 杭州翔毅科技有限公司 | Disaster early warning method, device, storage medium and device based on satellite monitoring |
| CN112508732A (en) * | 2020-11-05 | 2021-03-16 | 生态环境部南京环境科学研究所 | Water environment safety early warning prediction method |
| CN112735094A (en) * | 2020-12-17 | 2021-04-30 | 中国地质环境监测院 | Geological disaster prediction method and device based on machine learning and electronic equipment |
| CN113159532A (en) * | 2021-04-01 | 2021-07-23 | 兰州天泉信息科技有限公司 | Intelligent fire-fighting command system-oriented auxiliary decision-making key technology |
| CN113611084A (en) * | 2021-09-29 | 2021-11-05 | 中通服建设有限公司 | Visual monitoring and early warning method, device and equipment for natural disasters |
| CN113611084B (en) * | 2021-09-29 | 2021-12-21 | 中通服建设有限公司 | Visual monitoring and early warning method, device and equipment for natural disasters |
| CN114723912A (en) * | 2022-06-10 | 2022-07-08 | 中国地质科学院地质力学研究所 | Geological detection data analysis method and system based on GIS modeling |
| CN114723912B (en) * | 2022-06-10 | 2022-08-30 | 中国地质科学院地质力学研究所 | Geological detection data analysis method and system based on GIS modeling |
| CN115660424A (en) * | 2022-10-28 | 2023-01-31 | 国网四川省电力公司 | Disaster factor analysis early warning system based on GIS |
| CN115660424B (en) * | 2022-10-28 | 2024-02-13 | 国网四川省电力公司 | Disaster element analysis early warning system based on GIS |
| CN116912070A (en) * | 2023-09-14 | 2023-10-20 | 北京国信华源科技有限公司 | Safety pre-alarm method and system for GIS and multi-source data fusion |
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