CN115719161B - Method for comprehensively evaluating risk of collapse disaster - Google Patents
Method for comprehensively evaluating risk of collapse disaster Download PDFInfo
- Publication number
- CN115719161B CN115719161B CN202211394371.3A CN202211394371A CN115719161B CN 115719161 B CN115719161 B CN 115719161B CN 202211394371 A CN202211394371 A CN 202211394371A CN 115719161 B CN115719161 B CN 115719161B
- Authority
- CN
- China
- Prior art keywords
- risk
- collapse
- post
- value
- erosion
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 230000003628 erosive effect Effects 0.000 claims abstract description 63
- 238000011282 treatment Methods 0.000 claims abstract description 38
- 238000009826 distribution Methods 0.000 claims abstract description 35
- 238000010586 diagram Methods 0.000 claims abstract description 17
- 238000004458 analytical method Methods 0.000 claims abstract description 8
- 238000013507 mapping Methods 0.000 claims abstract description 5
- 238000010801 machine learning Methods 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 claims description 2
- 238000004162 soil erosion Methods 0.000 abstract description 4
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000011161 development Methods 0.000 description 4
- 239000002689 soil Substances 0.000 description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
- 230000000694 effects Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000010438 granite Substances 0.000 description 2
- 239000013049 sediment Substances 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 239000002253 acid Substances 0.000 description 1
- 238000012271 agricultural production Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 238000011269 treatment regimen Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Abstract
The invention discloses a method for comprehensively evaluating risk of a collapse disaster, which comprises the following steps: dividing a digital elevation model of a target area into a plurality of sub-drainage basins through hydrologic analysis; taking each sub-river basin as a minimum unit, respectively calculating an erosion risk value and a disaster risk value of each minimum unit, accumulating the corresponding erosion risk values and disaster risk values, and defining the accumulated erosion risk values and disaster risk values as a guard collapse comprehensive risk value of the minimum unit; and dividing a plurality of comprehensive risk values of the collapse sentry into risk grades by using a natural breakpoint method, and mapping the risk grades to a target area to obtain a spatial distribution diagram of the priority of the collapse sentry treatment. The beneficial effects of the invention are as follows: the risk of the collapse sentry is comprehensively evaluated from two aspects of erosion risk and disaster risk, and the priority of the collapse sentry treatment is formulated, so that a theoretical basis can be provided for scientific and accurate treatment of the collapse sentry in the red soil erosion area in the south of China.
Description
Technical Field
The invention relates to the technical field of risk management of natural disasters, in particular to a method for comprehensively evaluating risk of a collapse disaster.
Background
The collapse is a special soil erosion type of the southern granite region in China, and has complex cause and huge erosion modulus, seriously damages the ecological environment of the mountain region and restricts the economic development of the mountain region. The sediment produced by the erosion of the sentry is huge, so that the surface soil loss cannot be utilized, the sediment produced by the sentry is transported to the downstream, so that the riverbed, the reservoir, the pond and the channel are deposited, the farmland is lost, the life and property safety of people can be threatened even, and moreover, the yellow muddy acid water formed by the sentry is largely invaded into farmlands, so that great harm is brought to agricultural production, ecological environment and people's life. The characteristics of large erosion amount, strong explosiveness, high development speed and strong sudden nature of the collapse sentry are that the collapse sentry treatment becomes important and difficult for preventing and controlling the water and soil loss in the southern granite area.
In recent years, scholars have developed a great deal of work in aspects of classification, development process, formation mechanism, treatment measures and the like of the collapse, have obtained a plurality of achievements, and provide theoretical guidance and scientific basis for preventing and controlling the collapse. However, the focus of most of the above studies is mainly on the prediction of the potential risk of occurrence of a post collapse, and the risk assessment of the actual occurrence of a post collapse, which is in need of treatment, is not developed.
Disclosure of Invention
Aiming at the problems, the invention provides a method for comprehensively evaluating the risk of a collapse sentry disaster, which aims to solve the problem that the existing collapse sentry research lacks the risk evaluation of the associated treatment priority.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a method for comprehensively evaluating risk of a collapse disaster comprises the following steps:
dividing a digital elevation model of a target area into a plurality of sub-drainage basins through hydrologic analysis;
taking each sub-river basin as a minimum unit, respectively calculating an erosion risk value and a disaster risk value of each minimum unit, accumulating the corresponding erosion risk values and disaster risk values, and defining the accumulated erosion risk values and disaster risk values as a guard collapse comprehensive risk value of the minimum unit;
and dividing a plurality of the comprehensive risk values of the collapse sentry into risk grades by using a natural breakpoint method, and mapping the risk grades to the target area to obtain a spatial distribution diagram of the priority of the collapse sentry treatment.
In some embodiments, the target digital elevation model at 10m resolution is input into ArcGIS, and the sub-watershed is partitioned by a hydrologic analysis module provided by ArcGIS.
In some embodiments, the erosion risk values include a panel erosion value and a collapse shift erosion value, wherein the panel erosion value is divided into 6 erosion intensity levels from low to high, assigning 0, 0.2, 0.4, 0.6, 0.8, and 1, respectively; and assigning the post collapse erosion value after normalizing the post collapse density, wherein the weights of the planar erosion value and the post collapse erosion value are respectively set to 0.25 and 0.75.
In some embodiments, the method for calculating the erosion risk value is:
E risk ＝E s ×0.25+E b ×0.75
wherein E is risk For erosion risk value E s For the planar erosion value E b Is the collapse sentry erosion value.
In some embodiments, the distance between the disaster-stricken body and the post-collapse is defined as x, when x is less than or equal to 50m, the disaster-stricken risk value is assigned to 1, when x is less than or equal to 50m and less than or equal to 100m, the disaster-stricken risk value is assigned to 0.75, when x is less than or equal to 100m and less than or equal to 200m, the disaster-stricken risk value is assigned to 0.5, when x is less than or equal to 200m and less than or equal to 400m, the disaster-stricken risk value is assigned to 0.25, and when x is more than 400 m.
In some embodiments, the method for calculating the guard collapse comprehensive risk value includes:
A risk ＝E risk +D risk
wherein A is risk A guard collapse comprehensive risk value of the minimum unit E risk As the erosion risk value of the minimum unit, D risk Is a disaster risk value.
In some embodiments, according to the latest google remote sensing image and unmanned aerial vehicle image, and through visual interpretation of the collapse post as a training sample of machine learning, a first collapse post spatial distribution map is obtained, the first collapse post spatial distribution map is corrected into a second collapse post spatial distribution map according to field verification, the collapse post area is obtained from the second collapse post spatial distribution map, the sub-river basin boundary is used as a calculation unit, and the collapse post density is calculated by using the collapse post area as a weight.
In some embodiments, the model used in the machine learning is Swin Transformer, and the second post collapse spatial distribution map is obtained by performing spot check and verification on the post collapse dataset through field check and correction.
In some embodiments, the second post-collapse spatial distribution diagram is overlapped with the corresponding post-collapse comprehensive risk value, the vector boundary of each post-collapse in the target area is taken as a reference, the average value of the post-collapse comprehensive risk values in the minimum unit range is counted, the average value is defined as the final post-collapse comprehensive risk value of a single post-collapse, the fields of a plurality of final post-collapse comprehensive risk values are ordered in descending order in the attribute table of the second post-collapse spatial distribution diagram, the post-collapse treatment priority order of the second post-collapse spatial distribution diagram is obtained, the risk classification is carried out on the optimal post-collapse treatment priority order, and the optimal post-collapse treatment priority spatial distribution diagram is mapped to the target area.
In some embodiments, the collapse control priority order is divided into three risk classes, high, medium and low, using a natural breakpoint method.
The beneficial effects of the invention are as follows: the risk of the collapse sentry is comprehensively evaluated from two aspects of erosion risk and disaster risk, and the priority of the collapse sentry treatment is formulated, so that a theoretical basis can be provided for scientific and accurate treatment of the collapse sentry in the red soil erosion area in the south of China.
Drawings
FIG. 1 is a block flow diagram of a method for comprehensively evaluating risk of a post collapse disaster according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems solved by the present invention, the technical solutions adopted and the technical effects achieved more clear, the technical solutions of the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
The collapse sentry is very widely distributed in the south red soil area, mainly relates to Guangdong, guangxi, hunan, jiangxi, hubei, anhui province 7 Fujian provinces (autonomous region), wherein Guangdong province is taken as the most serious province of the collapse sentry, and the 10.78 ten thousand of collapse sentry are developed and account for 45.1 percent of the total amount of the collapse sentry in the south, and has typical representativeness.
The embodiment provides a method for comprehensively evaluating risk of a collapse disaster, wherein a flow frame of evaluation is shown in fig. 1, and a specific process comprises steps S1-S3:
s1, dividing a digital elevation model of a target area into a plurality of sub-watershed through hydrologic analysis;
inputting a target digital elevation model with the resolution of 10m into an ArcGIS, filling holes in grids, calculating flow directions and integrating flow in the module by using a hydrological analysis module provided by the ArcGIS, extracting grid of a ditch network/river according to a given threshold value of a grid calculator, and linking and grading the grid of the ditch network/river to obtain a ditch network/river grade diagram; the flow direction and the ditch net/river are used as input data, and the sub-river basin command is utilized to divide the sub-river basin.
S2, taking each sub-river basin as a minimum unit, respectively calculating an erosion risk value and a disaster risk value of each minimum unit, accumulating the corresponding erosion risk values and disaster risk values, and defining the accumulated erosion risk values and disaster risk values as a guard collapse comprehensive risk value of the minimum unit;
in one example, the erosion risk values include a panel erosion value and a collapse shift erosion value, wherein the panel erosion value is divided into 6 erosion intensity levels from low to high, with 0, 0.2, 0.4, 0.6, 0.8, and 1 respectively assigned; and after the collapse post density is normalized, assigning an collapse post erosion value, wherein the weights of the planar erosion value and the collapse post erosion value are respectively set to 0.25 and 0.75, and the planar erosion value and the collapse post erosion value are overlapped according to the weights, so that an erosion risk value can be obtained.
The specific calculation method of the erosion risk value comprises the following steps:
E risk ＝E s ×0.25+E b ×0.75
wherein E is risk For erosion risk value E s For the planar erosion value E b Is the collapse sentry erosion value.
Specifically, based on the remote sensing image and the unmanned aerial vehicle image according to the latest google, and by visually interpreting the collapse post as a training sample for machine learning, a first collapse post spatial distribution map is obtained, the first collapse post spatial distribution map is corrected to a second collapse post spatial distribution map according to field verification, and the collapse post area is obtained from the second collapse post spatial distribution map. Because the larger the collapse area is, the larger the damage is, the collapse density is calculated by using a density analysis tool of ArcGIS and using the boundary of the sub-river basin as a calculation unit and the collapse area as a weight.
The model used in machine learning is Swin Transformer, and the collapse post data set is subjected to spot check and verification through field check, and a second collapse post spatial distribution diagram is obtained through check and correction.
The distance between a disaster-stricken body and a collapse post contained in the sub-river basin is defined as x, when x is less than or equal to 50m, the disaster-stricken risk value is assigned to 1, when x is less than or equal to 50m and less than or equal to 100m, the disaster-stricken risk value is assigned to 0.75, when x is less than or equal to 100m and less than or equal to 200m, the disaster-stricken risk value is assigned to 0.5, when x is less than or equal to 200m and less than or equal to 400m, the disaster-stricken risk value is assigned to 0.25, and when x is more than 400m, the disaster-stricken risk value is assigned to 0. The disaster-stricken risk value spatial distribution map (D) of different disaster-stricken bodies (houses, farmlands and water bodies) can be obtained through the disaster-stricken risk values risk-fw 、D risk-nt 、D risk-st Disaster risk values of houses, farmlands and water bodies respectively), and the disaster risk values of different disaster subjects are superimposed to obtain a comprehensive disaster risk value spatial distribution diagram, specifically D risk ＝D risk-fw +D risk-nt +D risk-st 。
Specifically, the method for calculating the collapse comprehensive risk value comprises the following steps:
A risk ＝E risk +D risk
wherein A is risk A guard collapse comprehensive risk value of the minimum unit E risk As the smallest unitErosion risk value, D risk Is a disaster risk value.
S3, dividing a plurality of comprehensive risk values of the broken sentry into risk grades by using a natural breakpoint method, and mapping the risk grades to a target area to obtain a spatial distribution diagram of the broken sentry treatment priority.
Superposing the second post-collapse space distribution diagram with the corresponding post-collapse comprehensive risk values, counting the average value of the post-collapse comprehensive risk values in each minimum unit range by taking the vector boundary of each post-collapse in the target area as a reference, defining the average value as the final post-collapse comprehensive risk value of a single post-collapse, ordering the fields of a plurality of final post-collapse comprehensive risk values in the attribute table of the second post-collapse space distribution diagram in a descending order to obtain the post-collapse treatment priority sequence of the second post-collapse space distribution diagram, carrying out risk grading on the optimal post-collapse treatment priority sequence, and mapping the optimal post-collapse treatment priority sequence to the target area to obtain the post-collapse treatment priority space distribution diagram.
The priority order of the collapse sentry management is divided into three risk levels of high, medium and low by using a natural breakpoint method.
The red soil area in the south is numerous in number of collapse sentry, and all collapse sentry cannot be effectively treated in a short period. Therefore, the priority of the collapse sentry treatment needs to be analyzed according to the risk of the collapse sentry comprehensive erosion hazard. The regional results of the comprehensive erosion hazard risk of the broken sentry are combined through the broken sentry distribution, the broken sentry treatment priority is divided into three categories of high, medium and low treatment priority, and further classified management is carried out on the broken sentry risk. The collapse sentry erosion risk and the disaster risk with high treatment priority are both high, concentrated resource priority treatment is needed, the resource utilization efficiency can be improved, and the expected treatment effect is most obvious; in addition, although the urgency of treatment is relatively low in treatment priority or low in treatment, a certain proportion is also required to be selected for treatment, so that the treatment method can help to know the development process of the collapse sentry, promote the treatment strategy of the early stage and the middle stage of the collapse sentry and prevent the collapse sentry from continuing to develop. And extracting the comprehensive erosion hazard risk value of each collapse post according to the collapse post distribution data, and then sequencing the collapse posts according to the sequence from large to small, wherein the earlier the sequencing indicates that the collapse post hazard is larger, and the higher the treatment priority is.
In the embodiment, the risk of the collapse sentry is comprehensively estimated from two aspects of erosion risk and disaster risk, and the priority of the collapse sentry treatment is formulated, so that a theoretical basis can be provided for scientific and accurate treatment of the collapse sentry in the red soil erosion area in the south of China.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in the foregoing embodiments, and that the embodiments described in the foregoing embodiments may be combined appropriately to form other embodiments that will be understood by those skilled in the art.
The technical principle of the present invention is described above in connection with the specific embodiments. The description is made for the purpose of illustrating the general principles of the invention and should not be taken in any way as limiting the scope of the invention. Other embodiments of the invention will be apparent to those skilled in the art from consideration of this specification without undue burden.
Claims (5)
1. The method for comprehensively evaluating the risk of the collapse disaster is characterized by comprising the following steps of:
dividing a digital elevation model of a target area into a plurality of sub-drainage basins through hydrologic analysis;
taking each sub-river basin as a minimum unit, respectively calculating an erosion risk value and a disaster risk value of each minimum unit, accumulating the corresponding erosion risk values and disaster risk values, and defining the accumulated erosion risk values and disaster risk values as a guard collapse comprehensive risk value of the minimum unit;
the erosion risk value comprises a planar erosion value and a sentry erosion value, wherein the planar erosion value is divided into 6 erosion intensity levels from low to high, and 0, 0.2, 0.4, 0.6 and 0.8 are respectively assigned, and 1; after the collapse sentry density is normalized, assigning the collapse sentry erosion value, wherein the weights of the planar erosion value and the collapse sentry erosion value are respectively set to 0.25 and 0.75; the method for calculating the erosion risk value comprises the following steps:
E risk = E s ×0.25+ E b ×0.75
wherein E is risk For erosion risk value E s For the planar erosion value E b Is the collapse erosion value;
the distance between a disaster-stricken body contained in the sub-watershed and the post-collapse is defined as x, when x is less than or equal to 50m, the disaster-stricken risk value is assigned to 1, when x is less than or equal to 50m and less than or equal to 100m, the disaster-stricken risk value is assigned to 0.75, when x is less than or equal to 100m and less than or equal to 200m, the disaster-stricken risk value is assigned to 0.5, when x is less than or equal to 200m and less than or equal to 400m, the disaster-stricken risk value is assigned to 0.25, and when x is more than 400m, the disaster-stricken risk value is assigned to 0; the calculation method of the collapse guard comprehensive risk value comprises the following steps:
A risk = E risk + D risk
wherein A is risk A guard collapse comprehensive risk value of the minimum unit E risk As the erosion risk value of the minimum unit, D risk Is a disaster risk value;
dividing a plurality of the comprehensive risk values of the collapse sentry into risk grades by using a natural breakpoint method, and mapping the risk grades to the target area to obtain a spatial distribution diagram of the priority of the collapse sentry treatment;
according to the latest Google remote sensing image and unmanned aerial vehicle image, and through visual interpretation of the collapse post as a training sample for machine learning, a first collapse post spatial distribution map is obtained, the first collapse post spatial distribution map is corrected to a second collapse post spatial distribution map according to field checking, the collapse post area is obtained from the second collapse post spatial distribution map, the boundary of a sub-river basin is used as a calculating unit, and the collapse post density is calculated by using the collapse post area as a weight.
2. The method for comprehensively evaluating risk of a collapse sentry disaster according to claim 1, wherein the digital elevation model with a resolution of 10m is input into an ArcGIS, and the sub-watershed is partitioned by a hydrologic analysis module provided by the ArcGIS.
3. The method for comprehensively evaluating the risk of a post-collapse disaster according to claim 1, wherein a model used by machine learning is a Swin Transformer, the post-collapse dataset is subjected to spot check and verification through field check, and the second post-collapse spatial distribution diagram is obtained through check and correction.
4. The method of claim 1, wherein the second spatial distribution map of the post is superimposed with the corresponding comprehensive risk values of the post, the average value of the comprehensive risk values of the post in each minimum unit range is counted based on the vector boundary of each post in the target area, the average value is defined as the final comprehensive risk value of the post, the fields of the final comprehensive risk values of the post are sorted in descending order in the attribute table of the second spatial distribution map of the post to obtain the priority order of post treatment of the spatial distribution map of the second post, the priority order of post treatment is divided, and the priority order of post treatment is mapped to the target area to obtain the spatial distribution map of post treatment priority.
5. The method for comprehensively evaluating risk of a trip disaster according to claim 4, wherein the trip treatment priority sequence is divided into three risk levels of high, medium and low by using a natural breakpoint method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211394371.3A CN115719161B (en) | 2022-11-08 | 2022-11-08 | Method for comprehensively evaluating risk of collapse disaster |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211394371.3A CN115719161B (en) | 2022-11-08 | 2022-11-08 | Method for comprehensively evaluating risk of collapse disaster |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115719161A CN115719161A (en) | 2023-02-28 |
| CN115719161B true CN115719161B (en) | 2024-02-20 |
Family
ID=85254943
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211394371.3A Active CN115719161B (en) | 2022-11-08 | 2022-11-08 | Method for comprehensively evaluating risk of collapse disaster |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115719161B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104318085A (en) * | 2014-10-11 | 2015-01-28 | 福建师范大学 | Torrential flood risk identification and extraction method of drainage basins |
| CN110322118A (en) * | 2019-06-06 | 2019-10-11 | 重庆工商大学融智学院 | Geological disaster space distribution rule and assessment of easy generation method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10529029B2 (en) * | 2016-09-23 | 2020-01-07 | Aon Benfield Inc. | Platform, systems, and methods for identifying property characteristics and property feature maintenance through aerial imagery analysis |
-
2022
- 2022-11-08 CN CN202211394371.3A patent/CN115719161B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104318085A (en) * | 2014-10-11 | 2015-01-28 | 福建师范大学 | Torrential flood risk identification and extraction method of drainage basins |
| CN110322118A (en) * | 2019-06-06 | 2019-10-11 | 重庆工商大学融智学院 | Geological disaster space distribution rule and assessment of easy generation method |
Non-Patent Citations (3)
| Title |
|---|
| SwinLS:Adapting Swin Transformer to Landslid Detection;DongZhao 等;CDCEO 2022: 2nd Workshop on Complex Data Challenges in Earth Observation;第1-5页 * |
| 基于GIS的兰成原油管道地质灾害风险评价;冼国栋 等;油气储运;第38卷(第4期);第379-383页 * |
| 多尺度土壤侵蚀评价指数的技术与方法;赵文武 等;地理科学进展;第27卷(第2期);第47-51页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115719161A (en) | 2023-02-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110210621A (en) | A kind of object detection method based on residual error network improvement | |
| CN104408900B (en) | Neutral net flood warning devices and methods therefor based on dynamic optimization | |
| CN105279556B (en) | A kind of Enteromorpha detection method and device | |
| CN104732070B (en) | A kind of rock burst grade prediction technique based on information vector machine | |
| CN104318077B (en) | Climate change and mankind's activity change quantitative analysis method to river flow | |
| KR20180116820A (en) | Assessment of water use vulnerability in the unit watersheds using TOPSIS approach with subjective and objective weights | |
| Wang et al. | Evolution of coordination degree of eco-economic system and early-warning in the Yangtze River Delta | |
| CN106355540A (en) | Small- and medium-sized reservoir dam safety evaluating method based on GRA-BP (grey relational analysis and back propagation) neural network | |
| CN115063020B (en) | Multi-dimensional safety scheduling device and method for cascade hydropower station based on risk monitoring fusion | |
| CN106339796A (en) | Marine oil spill risk assessment method | |
| CN105653826A (en) | Improved maritime search and rescue region predicting method and system | |
| CN114842208A (en) | Power grid harmful bird species target detection method based on deep learning | |
| CN107632590A (en) | A kind of bottom event sort method based on priority | |
| Zhang et al. | Integrated hydrological modelling of small-and medium-sized water storages with application to the upper Fengman Reservoir Basin of China | |
| CN103870995A (en) | High and cold sand land vegetation recovery potential estimation method | |
| CN114255250A (en) | River protection engineering slope collapse detection method based on deep learning | |
| CN111709592A (en) | Deep foundation pit deformation prediction method and system based on neural network and rough set classification | |
| CN115438918A (en) | Method for evaluating ecological restoration effect of surface mine | |
| CN104036123A (en) | Short time trend predicating method for sea wave significant wave height based on reanalysis data | |
| Chen et al. | The application of the genetic adaptive neural network in landslide disaster assessment | |
| CN103729552B (en) | A kind of method of exit point during automatic identification wetlands in watersheds | |
| CN115719161B (en) | Method for comprehensively evaluating risk of collapse disaster | |
| Anilan et al. | Application of Artificial Neural Networks and regression analysis to L-moments based regional frequency analysis in the Eastern Black Sea Basin, Turkey | |
| CN104021288A (en) | Fundamental wave determining method for jacket platform frequency spectrum fatigue analysis | |
| Liu et al. | Prediction of dam horizontal displacement based on CNN-LSTM and attention mechanism |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |