CN114818984B - Refined urban ponding water level fitting method based on artificial intelligence - Google Patents
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Abstract
The invention discloses a refined urban ponding water level fitting method based on artificial intelligence, which comprises the following steps: 1. constructing a dimensionless hydrological feature database; 2. developing cluster analysis based on a graph neural network method; 3. dividing the urban ponding water level subarea; 4. establishing a personalized urban hydrological concept model region by region based on a neural network; 5. according to the ponding water level monitoring information of the ponding monitoring station, combining longitude information, latitude information, time information and ground elevation information, the ponding water level of any position of each subarea is reversely derived. The model has strong applicability, the model integrates the geographic information and the time information of the ponding sites, the space-time distribution characteristic of the ponding water level is effectively extracted, and the fitting capability of the model is strong; the system can provide ponding water level products at any position of the whole area, realizes grid removal, can realize return of historical water level based on a historical synchronization database, and plays an important role in preventing urban waterlogging and reasonably planning cities.
Description
Technical Field
The invention relates to a fitting method for urban accumulated water level, in particular to a refined urban accumulated water level fitting method based on artificial intelligence.
Background
Under the global warming background, extreme events occur frequently, and urban waterlogging is serious day by day, so that the life and property safety of people is seriously influenced. Under the background, high-quality urban ponding water level data has important significance for disaster prevention and reduction and urban future reasonable planning. At present, a part of cities are provided with urban ponding information monitoring systems, ponding information monitoring of a part of important road sections is realized, but in consideration of the cost of a ponding monitoring station and the actual conditions of all road sections, ponding monitoring of all areas of the cities is difficult to realize. In addition, the newly-added ponding monitoring station can not monitor the historical ponding water level at this position. Therefore, fitting is carried out on the water level of the whole area of the water tank based on the water tank monitoring information of the existing water tank monitoring station, and the return of the historical water level is realized.
The method is characterized in that the accumulated water level is fitted based on a simple linear interpolation method, namely linear interpolation is carried out according to the accumulated water levels of a plurality of accumulated water monitoring stations around a target position, but the method neglects the nonlinear characteristic of the accumulated water level distribution, does not consider the space-time distribution characteristic of the accumulated water level, and is difficult to generate high-quality accumulated water products. In addition, the existing method realizes the fitting of the urban accumulated water level based on the same model, neglects the difference of hydrological characteristics of different areas of the city, and has poor pertinence of the model. Meanwhile, regular gridding products are generated by the conventional method, effective fitting of ponding products in any position of the whole area is difficult to realize, and the application scene is limited.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a refined urban ponding water level fitting method based on artificial intelligence.
The technical scheme is as follows: the invention discloses an artificial intelligence-based fitting method for refining urban ponding water level, which comprises the following steps:
step 1, collecting ponding water level monitoring information, ground elevation information, longitude information, latitude information and time information of an urban ponding monitoring station, eliminating abnormal values, then carrying out standardization processing on data, and constructing a dimensionless hydrological feature database;
step 2, performing cluster analysis on the database in the step 1 based on a Graph Neural Network method, and clustering sites with similar hydrological characteristics together;
step 3, dividing the city into subareas based on the clustering result in the step 2, and dividing the subareas of the ponding water level of the city;
step 4, taking ground elevation information, longitude information, latitude information and time information of all the sites and ponding water level monitoring information outside the target site as input variables, taking the ponding water level of the target site as an output variable, and constructing a personalized hydrological conceptual model region by region;
and 5, based on the hydrological conceptual model constructed region by region in the step 4, utilizing the ponding water level monitoring information, the ground elevation information, the longitude information, the latitude information and the time information of the ponding monitoring stations in the sub-regions and the ground elevation information, the longitude information, the latitude information and the time information of the target positions to reversely derive the ponding water level of any position of each sub-region.
Further, in step 1, the calculation formula of the normalization process is:
in the formula,for the purpose of the result after the normalization,representIs determined by the average value of (a) of (b),to representStandard deviation of (d); after all variables are standardized, hydrological feature vectors of all ponding sites are formed:
in the formula,representing the hydrological feature vector for the kth ponding site,and respectively representing the standardized ponding water level monitoring information, the ground elevation information, the longitude information, the latitude information and the time information, thereby forming a dimensionless hydrological feature database.
Further, step 2 specifically comprises:
step 2.1, presume study areaExist ofA site, constructing a topology network based on site distributionTopological networksMainly consists of two parts which are respectively nodesAnd an edgeWherein the nodeRepresenting the stations in the research area, wherein the characteristic vector of each station is shown as formula (2); edge of a containerRepresenting the interrelationship between the various sites;
step 2.2, network based topologyBuilding a corresponding input matrixInput matrixTopology networkImaging, and combining n sitesThe two-dimensional matrix represents the upstream and downstream influences existing among different nodes by using the weight;
step 2.3, constructing Graph Auto Encode, which comprises compiling original input matrix by using encoder EncodeAnd reconstructing the original network structure by using a decoder; first, the input matrix in step 2.2 is encoded using an encoderFeature vector ofIs projected toCoding space of dimensionI.e. by(ii) a At this point, consider the use of a mechanism with attentionThe encoder performs weighted average on the neighborhood nodes, and the calculation formula is written as:
wherein,is composed ofThe function of the function is that of the function,、are respectively nodesIn the encoderLayer and the firstThe information of the layer(s) is,is a nodeOf the neighborhood of the node in the cluster,is a nodeAnd between the neighboring nodesThe attention weight of (1);the calculation formula of the function is:
then, the coding space is calculatedObtaining the inner product between the inner node pairs to obtain the reconstructed original network(ii) a At this time, the error of the graph self-encoder reconstruction network is represented as:
step 2.4, constructing a self-training clustering network, and assuming that a clustering center isThen nodeBelong toProbability of classExpressed as:
wherein,is as followskThe center of the class is classified into a class center,is a nodeAnd cluster centerThe Euclidean distance between the nodes and the similarity between the nodes are represented, in the clustering problem, each node and the corresponding clustering center are forced to tend to the minimum intra-class distance and the maximum inter-class distance, and the target distribution is realizedIs defined as:
at this time, the KL divergence is used to characterize the predicted cluster distributionAnd target distributionError between, defining a self-trained loss function:
in order to be a weight coefficient of the image,reconstructing errors of the network for the image self-encoder by minimizingTraining the model according to the timeJudging nodeThe class to which it most likely belongs.
Further, step 4 specifically includes:
under the same subregion, taking the ground elevation information, longitude information, latitude information and time information of all the sites and the ponding water level monitoring information outside the target site as input variables, taking the ponding water level of the target site as output variables, constructing an individualized hydrological conceptual model region by region based on a neural network, wherein the input variables can be expressed as:
whereinRespectively represents the ponding water level monitoring information, the ground elevation information, the longitude information, the latitude information and the time information,representing the number of water accumulation sites in the sub-area, assuming each site hasData of individual accumulated water, all areThe model is trained by each sample, the model effectively extracts the spatial distribution characteristics of the water level of the ponding water by adding the ground elevation information, the longitude information and the latitude information, the seasonal change and the daily change characteristics of the ponding water level are favorably captured by adding the time information, the fitting capacity of the model is enhanced, and the calculation formula of the neural network is as follows:
,,representing input layer, hidden layer and output layer vectors respectively,in order to hide the number of layers,andfor each of the training parameters of the layers,representing the activation function, which is also a non-linear source of the neural network, the activation function to be adopted by the method is a ReLU function:
has the advantages that: compared with the prior art, the invention has the following remarkable advantages:
(1) the invention carries out cluster analysis on the hydrological characteristics of the ponding monitoring station based on the graph neural network method, divides the city into a plurality of sub-areas, and constructs an individual hydrological model for the actual hydrological characteristics of each sub-area by area, and the model has stronger applicability.
(2) The method is based on the neural network method for modeling, can effectively extract the nonlinear characteristics of the ponding water level, integrates the geographic information (ground elevation information, longitude and latitude information) and time information of the ponding sites, is favorable for extracting the spatial-temporal distribution characteristics of the ponding water level, and improves the fitting capability of the model.
(3) The invention can provide ponding water level products at any position in the whole area, realizes gridding removal, can realize return of historical water level based on the historical synchronization database, has strong application value, and plays an important role in preventing urban waterlogging and reasonably planning cities.
Drawings
FIG. 1 is a general flow chart of the present invention;
FIG. 2 is a schematic diagram of a Graph Neural Network (GNN) according to the present invention;
FIG. 3 is a schematic diagram of a city ponding information site clustering;
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
The flow of the fitting method for refining the urban ponding water level based on artificial intelligence is shown in figure 1.
Step 1, collecting ponding water level monitoring information, ground elevation information, longitude information, latitude information and time information of an urban ponding monitoring station, and carrying out standardized processing on data after abnormal values are eliminated, wherein the calculation formula is as follows:
in the formula,for the purpose of the normalized result, the results,to representIs determined by the average value of (a) of (b),to representStandard deviation of (2). After all variables are standardized, hydrological feature vectors of all ponding sites are formed:
in the formula,representing the hydrological feature vector for the kth ponding site,and respectively representing the standardized ponding water level monitoring information, the ground elevation information, the longitude information, the latitude information and the time information, thereby forming a dimensionless hydrological feature database.
And 2, performing cluster analysis based on a Graph Neural Network (GNN) method, and clustering sites with similar hydrological features. The method specifically comprises the following steps:
step 2.1 postulating the region of investigationExist ofEach site, constructing a topology network based on site distributionAs shown in fig. 2 (a), fig. 2 (a) is mainly composed of two parts, which are nodes respectivelyAnd an edgeWherein the nodeRepresenting the stations in the research area, wherein the characteristic vector of each station is shown as formula (2); edge of a containerRepresenting the interrelationship between the various sites; if the underground drainage network is shared, the terrain is high or low, and the like. Note that the edgesThe vector for the belt direction, as in fig. 2 (b), may represent a unidirectional effect, and may also characterize a bidirectional effect. At this time, the network is topologicalCan use a feature matrix( A station,A feature) and an edgeAnd (4) performing representation.
Step 2.2, known from step 2.1, the nodeThe neighborhood influence is controlled by the constructed topology, and the influence networks of different nodes are very different. Network according to topologyConstructing a corresponding regular input matrixAs in fig. 2 (c). Input matrixTopology networkIs like an elephant and is composed ofThe two-dimensional matrix of (2) represents the upstream and downstream influences existing among different nodes by using the weight.
Step 2.3, constructing Graph Auto encoder (Graph Auto encoder) which includes compiling original input matrix by encoder (encoder)And reconstructing the original network structure using an inverse encoder (Decode). First, the input matrix in step 2.2 is encoded using an encoderFeature vector ofIs projected toCoding space of dimensionI.e. by. At this time, considering that an encoder with attention mechanism is used to perform weighted average on the neighborhood nodes, the calculation formula can be written as:
wherein,is composed ofThe function of the function is that of the function,、are respectively nodesIn the encoder it isLayer and the firstThe information of the layer(s) is,is a nodeThe set of neighborhood nodes of (a) is,is a nodeAnd between the neighboring nodesThe attention weight of (1);the calculation formula of the function is:
then, the coding space is calculatedObtaining the inner product between the inner node pairs to obtain the reconstructed original network(ii) a At this time, the error of the graph self-encoder reconstructing the network is represented as:
step 2.4, constructing a self-training clustering network, and assuming that a clustering center isThen nodeBelong toProbability of classExpressed as:
wherein,is as followskThe center of the class is classified into a class center,is a nodeAnd cluster centerThe Euclidean distance between the nodes and the similarity between the nodes are represented, in the clustering problem, each node and the corresponding clustering center are forced to tend to the minimum intra-class distance and the maximum inter-class distance, and the target distribution is realizedIs defined as:
at this time, the KL divergence is used to characterize the predicted cluster distributionAnd target distributionError between, defining loss of self-trainingFunction:
in order to be the weight coefficient,reconstructing errors of the network for the image self-encoder by minimizingTraining the model according to the timeJudging nodeThe class to which it most likely belongs. Fig. 3 is a schematic diagram of a clustering result, in which a city is taken as an example and is divided into 3 sub-regions.
And 3, dividing the sites with similar hydrological characteristics into the same sub-area based on the clustering result in the step 2, and then dividing the whole city into k sub-areas (the number k of the sub-areas depends on the clustering result).
And 4, constructing the personalized hydrological concept model region by region.
Under the same subregion, the ground elevation information, longitude information, latitude information, time information of all the sites and the ponding water level monitoring information outside the target site are used as input variables, the ponding water level of the target site is used as an output variable, a personalized hydrological conceptual model is constructed region by region based on a neural network, and the schematic diagram of the neural network is shown in fig. 4. The input variables may be represented as:
whereinRespectively represents the ponding water level monitoring information, the ground elevation information, the longitude information, the latitude information and the time information,representing the number of water accumulation sites in the sub-area, assuming each site hasData of water accumulation, all areThe model is trained by each sample, the model effectively extracts the spatial distribution characteristics of the water level of the ponding water by adding the ground elevation information, the longitude information and the latitude information, the seasonal change and the daily change characteristics of the ponding water level are favorably captured by adding the time information, the fitting capacity of the model is enhanced, and the calculation formula of the neural network is as follows:
,,are respectively provided withRepresenting the input layer, hidden layer and output layer vectors,in order to hide the number of layers,andfor each of the training parameters of the layers,representing the activation function, which is also a non-linear source of the neural network, the activation function to be adopted by the method is a ReLU function:
and 5, according to the ponding information of the ponding monitoring station, the ponding information of each subarea is reversely derived.
And (4) inputting longitude information, latitude information, ground elevation information and time information of any target position based on the personalized hydrological model trained region by region in the step 4, and matching the ponding water level monitoring information, the ground elevation information, the longitude information, the latitude information and the time information of all the ponding monitoring stations in the sub-region at the same period to obtain the ponding water level information of the position. The scheme can obtain ponding water level information of all areas at any positions, and can provide high-resolution refined ponding products. In addition, the scheme can realize the return of the water level of the whole area based on the historical contemporaneous water level data.
Claims (2)
1. A refined urban ponding water level fitting method based on artificial intelligence is characterized by comprising the following steps:
step 1, collecting ponding water level monitoring information, ground elevation information, longitude information, latitude information and time information of an urban ponding monitoring station, eliminating abnormal values, then carrying out standardization processing on data, and constructing a dimensionless hydrological feature database;
step 2, performing cluster analysis on the database in the step 1 based on a Graph Neural Network method, and clustering sites with similar hydrological characteristics together, wherein the method comprises the following steps:
step 2.1, presume study areaExist ofEach site, constructing a topology network based on site distributionTopological networksMainly consists of two parts which are respectively nodesAnd an edgeWherein the nodeRepresenting the stations in the research area, wherein the characteristic vector of each station is shown as formula (2); edge of a containerRepresenting the interrelationship between the various sites;
step 2.2, according to the topological networkBuilding a corresponding input matrixInput matrixTopology networkImaging, and combining n sitesThe two-dimensional matrix of (2) represents the upstream and downstream influences existing among different nodes by using the weight;
step 2.3, constructing Graph Auto Encode, which comprises compiling original input matrix by using encoder EncodeAnd reconstructing the original network structure by using a decoder; first, the input matrix in step 2.2 is encoded using an encoderFeature vector ofIs projected toCoding space of dimensionI.e. by(ii) a At this time, considering that an encoder with attention mechanism is used to perform weighted average on the neighborhood nodes, the calculation formula is written as:
wherein,is composed ofThe function of the function is that of the function,、are respectively nodesIn the encoderLayer and the firstThe information of the layer(s) is,is a nodeThe set of neighborhood nodes of (a) is,is a nodeAnd between the neighboring nodesAttention weight of (1);the calculation formula of the function is:
then, the coding space is calculatedObtaining the inner product between the inner node pairs to obtain the reconstructed original network(ii) a At this time, the error of the graph self-encoder reconstructing the network is represented as:
step 2.4, constructing a self-training clustering network, and assuming that a clustering center isThen nodeBelong toProbability of classExpressed as:
wherein,is as followskThe center of the class is classified into a class center,is a nodeAnd cluster centerThe Euclidean distance between the nodes and the similarity between the nodes are represented, in the clustering problem, each node and the corresponding clustering center are forced to tend to the minimum intra-class distance and the maximum inter-class distance, and the target distribution is realizedIs defined as:
at this time, the KL divergence is used to characterize the predicted cluster distributionAnd target distributionError between, defining a self-trained loss function:
in order to be the weight coefficient,reconstructing errors of the network for the image self-encoder by minimizingTraining the model according to the timeJudging nodeThe category to which it most likely belongs;
step 3, dividing the city into subareas based on the clustering result in the step 2, and dividing the subareas of the ponding water level of the city;
step 4, taking the ground elevation information, longitude information, latitude information and time information of all the sites and the monitoring information of the ponding water level outside the target site as input variables, taking the ponding water level of the target site as output variables, and constructing an individualized hydrological conceptual model region by region, wherein the method comprises the following steps:
under the same subregion, taking ground elevation information, longitude information, latitude information and time information of all stations and ponding water level monitoring information outside a target station as input variables, taking the ponding water level of the target station as an output variable, and constructing an individualized hydrological conceptual model region by region based on a neural network, wherein the input variables can be expressed as follows:
whereinRespectively represents the ponding water level monitoring information, the ground elevation information, the longitude information, the latitude information and the time information,representing the number of water accumulation sites in the sub-area, assuming each site hasData of individual accumulated water, all areThe model is trained by each sample, the model effectively extracts the spatial distribution characteristics of the water level of the ponding water by adding the ground elevation information, the longitude information and the latitude information, the seasonal change and the daily change characteristics of the ponding water level are favorably captured by adding the time information, the fitting capacity of the model is enhanced, and the calculation formula of the neural network is as follows:
,,representing input layer, hidden layer and output layer vectors respectively,in order to hide the number of layers,andfor each of the training parameters of the layers,representing the activation function, which is also a non-linear source of the neural network, the activation function to be adopted by the method is a ReLU function:
and 5, based on the hydrological conceptual model constructed region by region in the step 4, utilizing the ponding water level monitoring information, the ground elevation information, the longitude information, the latitude information and the time information of the ponding monitoring stations in the sub-regions and the ground elevation information, the longitude information, the latitude information and the time information of the target positions to reversely derive the ponding water level of any position of each sub-region.
2. The method for fitting the water level of the urban ponding water based on the artificial intelligence refinement of the claim 1, wherein in the step 1, the calculation formula of the standardization processing is as follows:
in the formula,for the purpose of the normalized result, the results,to representIs determined by the average value of (a) of (b),representStandard deviation of (d); after all variables are standardized, hydrological feature vectors of all ponding sites are formed:
in the formula,representing the hydrological feature vector for the kth ponding site,and respectively representing the standardized ponding water level monitoring information, the ground elevation information, the longitude information, the latitude information and the time information, thereby forming a dimensionless hydrological feature database.
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