CN114925923A - Method for improving depth prediction precision of ponding of non-monitoring unit - Google Patents
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Abstract
The invention discloses a method for improving the accuracy of depth prediction of accumulated water of non-monitoring units, which comprises the steps of obtaining a predicted value of the depth of the accumulated water of a monitoring point by utilizing a STARMA model; obtaining a surface water depth forecast value by utilizing an InfoWorks ICM model; dividing the earth surface unit into a monitoring unit and a non-monitoring unit according to whether the earth surface unit contains a monitoring point; determining a monitoring point corresponding to the monitoring unit; for the monitoring unit, a predicted value obtained by fusing an InfoWorks ICM model and a STARMA model is used as a final predicted value; and constructing a neural network for improving the accumulated water depth forecasting precision of the non-monitoring units, training the neural network by using the final forecast value of the monitoring units, the forecast value of the InfoWorks ICM model and the forecast value of the STARMA model, and improving the accumulated water depth forecasting precision of the non-monitoring units based on the neural network. The invention has wide application prospect in the fields of urban drainage waterlogging prevention, sponge cities, emergency management and the like.
Description
Technical Field
The invention relates to a method for improving the accumulated water depth forecasting precision of a non-monitoring unit, and belongs to the technical application fields of urban drainage waterlogging prevention, emergency management, sponge cities and the like.
Background
Urban inland inundation seriously threatens the life and property safety of people. The accurate surface water depth forecast can provide reliable decision basis for urban drainage waterlogging prevention, emergency dispatching and the like, so that waterlogging harm is effectively relieved.
Generally, an urban rainfall FLOOD model is used for simulating and forecasting the depth of surface water, and the commonly used urban rainfall FLOOD model mainly comprises models such as MIKE FLOOD, InfoWorks ICM, LISFLOOD-FP, XP-SWMM 2D and the like. Due to the fact that urban rainfall and underlying surfaces have strong spatial heterogeneity, high-precision data of the urban rainfall and underlying surfaces are difficult to obtain, and underground pipe network data are missing, wrong and the like, the surface water depth prediction precision of an existing urban rainfall flood model still needs to be improved. In order to meet the requirement of urban drainage and waterlogging prevention, a series of waterlogging monitoring devices are distributed in many cities and used for monitoring the waterlogging depth of urban waterlogging points. Actually, a monitoring point water accumulation forecasting model is established by using data such as precipitation, water accumulation and the like of an waterlogging monitoring point, and high water accumulation depth forecasting precision can be obtained. The urban rainfall flood model and the monitoring point ponding forecast model have complementarity, and the two models are effectively combined to obtain a more accurate surface ponding depth forecast value.
The urban land surface is discretized into a series of land surface units, and the land surface units are divided into monitoring units and non-monitoring units according to whether waterlogging monitoring points are included. For the monitoring unit, the forecast values of the urban rainfall flood model and the monitoring point ponding forecasting model can be fused by using a model fusion method (such as weighted average, Bagging, Boosting and Stacking) and the fused forecast values are used as final forecast values, so that the ponding depth forecasting precision is improved. However, an effective technical means is lacked if the accumulated water depth prediction accuracy of the non-monitoring units is improved by using the prediction results of the urban rainfall flood model and the accumulated water prediction model of the monitoring points.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for improving the ponding depth forecasting precision of a non-monitoring unit, wherein the method divides a surface unit into a monitoring unit and a non-monitoring unit; for the monitoring unit, a Bayesian model is used for weighted average to obtain a final accumulated water depth forecast value; and constructing a neural network for improving the prediction precision of the water depth of the non-monitoring unit on the basis.
The method for improving the accuracy of the depth forecast of the ponding of the non-monitoring unit specifically comprises the following steps:
step (1) waterlogging depth forecast at waterlogging monitoring point
Calculating to obtain a water depth forecast value of each waterlogging monitoring point by using a STARMA model of rainfall water, wherein the water depth forecast value of the waterlogging monitoring point based on the model is referred to as the STARMA forecast value for short below;
step (2) depth prediction and discretization of prediction value of surface water
Carrying out analog prediction on the surface water depth by utilizing an InfoWorks ICM model, carrying out spatial discretization on a surface water depth prediction value of the model, and shortening the surface water depth prediction value based on the InfoWorks ICM model to be an InfoWorks ICM prediction value;
step (3) surface discretization and classification
Discretizing the earth surface into a series of earth surface units in a discretization mode consistent with the step (2); dividing the surface unit into a surface unit containing the waterlogging monitoring point and a surface unit not containing the waterlogging monitoring point according to whether the surface unit has the waterlogging monitoring point or not; the surface unit containing the waterlogging monitoring point is hereinafter referred to as a monitoring unit, and the surface unit not containing the waterlogging monitoring point is hereinafter referred to as a non-monitoring unit;
step (4) determining the waterlogging monitoring points corresponding to the monitoring units
The invention assumes that one monitoring unit corresponds to one waterlogging monitoring point: for a monitoring unit, if the unit only comprises one waterlogging monitoring point, the waterlogging monitoring point is used as the waterlogging monitoring point corresponding to the unit; if the unit comprises a plurality of waterlogging monitoring points, a waterlogging monitoring point is 'virtually' taken out, the monitoring value of the virtual waterlogging monitoring point is the mean value of the monitoring values of the plurality of waterlogging monitoring points, the forecast value of the virtual waterlogging monitoring point is the mean value of the forecast values of the plurality of waterlogging monitoring points, and the virtual waterlogging monitoring point is taken as the waterlogging monitoring point corresponding to the unit.
Step (5) improving the accumulated water depth forecasting precision of the monitoring unit by using Bayesian model weighted average
For each monitoring unit, carrying out weighted average on an InfoWorks ICM predicted value of the monitoring unit and a STARMA predicted value of a corresponding monitoring point of the monitoring unit by using Bayesian model weighted average to obtain a fused predicted value, wherein the fused predicted value is used as a final accumulated water depth predicted value of the monitoring unit; calculating the weight of the InfoWorks ICM model and the STARMA model of the rainfall ponding by using an expectation-maximization algorithm;
step (6) constructing a neural network for improving the accuracy of the non-monitoring unit water depth prediction
Constructing a neural network for improving the water depth forecasting precision of the non-monitoring units, and training the neural network by using the final forecast value, the InfoWorks ICM forecast value and the STARMA forecast value of the monitoring points corresponding to the monitoring units of the monitoring units; for any non-monitoring unit, the input of the neural network is the InfWorks ICM predicted value of the non-monitoring unit, the InfWorks ICM predicted values of n monitoring units nearest to the non-monitoring unit and the STARMA predicted values of the monitoring points corresponding to the n monitoring units, and the output of the neural network is the final accumulated water depth predicted value of the non-monitoring unit.
Preferably, the discretization mode in the step (2) is a regular grid mode or an irregular triangular grid mode.
Preferably, the accuracy of the ponding depth forecast of the monitoring unit is improved by using a bayesian model weighted average, which specifically comprises the following steps:
and for each monitoring unit, improving the accumulated water depth forecasting precision by performing the steps of I-IV.
Normal conversion
Acquiring an InfoWorks ICM forecast value sequence of a monitoring unit, a ponding depth monitoring data sequence of a monitoring point corresponding to the monitoring unit and a STARMA forecast value sequence of a monitoring point corresponding to the monitoring unit; carrying out normal conversion on the sequences by using Box-Cox transformation to obtain related sequences conforming to normal distribution;
determining a weighted average forecasting formula of the ponding depth Bayes model as follows:
wherein D is the forecast volume, namely the depth of the accumulated water; o is accumulated water depth monitoring data of a monitoring point corresponding to the monitoring unit; p (d) 1 I O) is the STARMA predicted value d of the corresponding monitoring point of the monitoring unit under the condition of given monitoring data O 1 A posteriori probability of (i.e. weight value w of STARMA model of rainfall water) 1 ；p(d 2 I O) is the predicted value d of the monitoring unit InfoWorks ICM under the condition of given monitoring data O 2 The posterior probability of (1), i.e. the weight value w of the InfoWorks ICM model 2 ；
(iii) weight calculation
Calculating the related sequence data conforming to the normal distribution in the formula (1) by an expectation-maximization algorithm
w 1 And w 2 ；
Method application
For the monitoring unit, firstly, the Box-Cox conversion is utilized to carry out normal conversion on the STARMA predicted value of the monitoring point corresponding to the monitoring unit and the InfoWorks ICM predicted value of the monitoring unit to obtain d 1 'and d' 2 Calculating by using a formula (2) to obtain a Bayesian model weighted average predicted value d'; and performing inverse Box-Cox transformation on the d', wherein the numerical value after inverse transformation is used as the final accumulated water depth forecast value of the monitoring unit.
d'＝w 1 d 1 '+w 2 d' 2 (2)
Preferably, the constructing of the neural network model for improving the accuracy of the non-monitoring unit water depth prediction specifically includes the following steps:
determining neural network structure
The neural network adopts a three-layer structure and comprises an input layer, a hidden layer and an output layer, wherein the number of neurons in the hidden layer is a hyper-parameter and is determined through experiments;
determining inputs and outputs of neural networks
For any non-monitoring unit, the input of the neural network is an InfoWorks ICM predicted value of the non-monitoring unit, InfoWorks ICM predicted values of n monitoring units nearest to the non-monitoring unit and STARMA predicted values of monitoring points corresponding to the n monitoring units; the output of the neural network is the final accumulated water depth forecast value of the non-monitoring unit; n is a hyper-parameter and is determined through experiments;
training neural network
Firstly, a data set used by the neural network is constructed: for each monitoring unit, taking the InfoWorks ICM predicted value of the monitoring unit, the InfoWorks ICM predicted values of other n monitoring units closest to the monitoring unit and the STARMA predicted values of corresponding monitoring points of the n monitoring units as input parts of a data set, and taking the final accumulated water depth predicted value of the monitoring unit as an output part of the data set;
then, randomly dividing the data set into a training set and a testing set, training a neural network by using the training set, and optimizing the neural network by adopting a gradient descent method;
method application
For each non-monitoring unit, inputting the InfWorks ICM predicted value of the non-monitoring unit, InfWorks ICM predicted values of n monitoring units nearest to the non-monitoring unit and STARMA predicted values of corresponding monitoring points of the n monitoring units into a trained neural network, and calculating to obtain a final ponding depth predicted value of the non-monitoring unit.
The invention has the beneficial effects that:
1. the method can effectively improve the accumulated water depth forecasting precision of the non-monitoring unit;
2. the method has strong portability and is suitable for other types of rainfall flood models and monitoring point ponding forecasting models.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
FIG. 2 is a schematic diagram of a neural network model for improving accuracy of non-monitoring unit water depth prediction.
Detailed Description
The following describes a specific implementation method of the present invention with reference to the flowchart shown in fig. 1:
step (1) waterlogging depth forecast at waterlogging monitoring point
Calculating a water depth forecast value of each waterlogging monitoring point by using a STARMA model of the rainfall water, wherein the water depth forecast value of the waterlogging monitoring points based on the model is referred to as the STARMA forecast value hereinafter, and the STARMA model of the rainfall water is referred to in the literature ' Zhengsai, Wanqing, & Jaminyuan ' (2014) ', short-time prediction of the water at urban rainstorm water accumulation points based on the STARMA model, geoscience progress, 33(7), 949-;
step (2) depth prediction and discretization of prediction value of surface water
The method comprises the steps of utilizing an InfoWorks ICM model to carry out analog forecasting on surface water depth, carrying out spatial discretization on a surface water depth forecasting value of the model, wherein the discretization mode supports modes such as regular grids, irregular triangular grids and the like, and the surface water depth forecasting value based on the InfoWorks ICM model is hereinafter referred to as InfoWorks ICM forecasting value;
step (3) discretizing and classifying the surface units
Dividing the urban ground surface into a series of ground surface units by adopting a space discretization mode consistent with the step (2); according to whether the surface unit contains the waterlogging monitoring point or not, dividing the surface unit into a surface unit (hereinafter referred to as a monitoring unit) containing the waterlogging monitoring point and a surface unit (hereinafter referred to as a non-monitoring unit) not containing the waterlogging monitoring point;
step (4) determining waterlogging monitoring points corresponding to the monitoring units
The invention assumes that one monitoring unit corresponds to one waterlogging monitoring point: for one monitoring unit, if the monitoring unit only comprises one waterlogging monitoring point, the waterlogging monitoring point is used as the waterlogging monitoring point corresponding to the monitoring unit; if the monitoring unit comprises a plurality of waterlogging monitoring points, one waterlogging monitoring point is 'virtually' taken out, the monitoring value of the virtual waterlogging monitoring point is the mean value of the monitoring values of the plurality of waterlogging monitoring points, the forecast value of the virtual waterlogging monitoring point is the mean value of the forecast values of the plurality of waterlogging monitoring points, and the virtual waterlogging monitoring point is taken as the waterlogging monitoring point corresponding to the monitoring unit.
Step (5) improving the accumulated water depth forecasting precision of the monitoring unit by using Bayes model weighted average
For each monitoring unit, model prediction value fusion is realized by executing the steps of (i) - (iv), wherein the step of (i) - (iv) is a modeling step, and the step of (iv) is an application step:
obtaining an InfWorks ICM predicted value sequence of a monitoring unit, a water accumulation depth monitoring data sequence of a monitoring point corresponding to the monitoring unit and a STARMA predicted value sequence of the monitoring point corresponding to the monitoring unit; carrying out normal conversion on the time sequence data by using Box-Cox transformation to obtain a related sequence conforming to normal distribution;
determining a water accumulation depth BMA forecasting formula as follows:
wherein D is the forecast amount, namely the depth of accumulated water; o is accumulated water depth monitoring data of a monitoring point corresponding to the monitoring unit; p (d) 1 I O) is the STARMA predicted value d of the corresponding monitoring point of the monitoring unit under the condition of given monitoring data O 1 A posteriori probability of (i.e. weight value w of STARMA model of rainfall water) 1 ；p(d 2 I O) is the InfoWorks ICM predicted value d of the monitoring unit under the condition of given monitoring data O 2 The posterior probability of (1), i.e. the weight value w of the InfoWorks ICM model 2 ；
Is a mean value d 1 Variance, variance
Normal distribution of (2);
is a mean value d 2 Variance, variance
Normal distribution of (2); assume O, d in equation (1) 1 、d 2 All are subjected to normal conversion and accord with normal distribution;
thirdly, weight calculation, namely calculating the sequence data in the formula (1) by an expectation-maximization algorithm by using the sequence data which accords with normal distribution in the step (i)
w 1 And w 2 ；
The monitoring unit is normally converted by using Box-Cox conversion to obtain d 1 'and d' 2 Calculating by using a formula (2) to obtain a Bayesian model weighted average predicted value d'; performing inverse Box-Cox transformation on the d', wherein the numerical value after the inverse transformation is the final accumulated water depth forecast value of the monitoring unit;
d'＝w 1 d 1 '+w 2 d' 2 (2)
step (6) constructing a neural network for improving the accuracy of the non-monitoring unit water depth prediction
For a non-monitoring unit, besides the InfoWorks ICM predicted value of the non-monitoring unit, the InfoWorks ICM predicted values of n monitoring units nearest to the non-monitoring unit and the STARMA predicted values of monitoring points corresponding to the n monitoring units can also provide extra 'information quantity' for improving the accuracy of the predicted values of the non-monitoring unit, and the three 'information quantities' are actually the predicted values of an InfoWorks ICM model and a STARMA model of rainfall ponding; in order to fully utilize the three information quantities (forecast values of two types of models) to improve the accumulated water depth forecasting precision of the non-monitoring unit and construct a neural network for improving the accumulated water depth forecasting precision of the non-monitoring unit, the concrete steps are as follows (wherein, the third step is a modeling step, and the fourth step is an application step):
determining a neural network structure, wherein the neural network adopts a three-layer structure and consists of an input layer, a hidden layer and an output layer (figure 2), the number of neurons in the hidden layer is a hyper-parameter and is determined through experiments;
for any non-monitoring unit, the input of the neural network is the InfoWorks ICM predicted value of the non-monitoring unit, the InfoWorks ICM predicted values of n monitoring units nearest to the non-monitoring unit and the STARMA predicted values of corresponding monitoring points of the n monitoring units, and the output of the neural network is the final accumulated water depth predicted value of the non-monitoring unit; n is used as a hyper-parameter and is determined through experiments;
training a neural network:
firstly, a data set used by the neural network is constructed: for each monitoring unit, taking the InfoWorks ICM predicted value of the monitoring unit, the InfoWorks ICM predicted values of n other monitoring units closest to the monitoring unit and STARMA predicted values of monitoring points corresponding to the n monitoring units as input parts of a data set, and taking the final ponding depth predicted value of the monitoring unit (obtained by calculating' the ponding depth prediction accuracy of the monitoring unit is improved by using Bayesian model weighted average in step (5)) as an output part of the data set;
then, randomly dividing the data set into a training set and a test set, training a neural network by using the training set, and optimizing the neural network by adopting a gradient descent method;
and fourthly, for each non-monitoring unit, inputting the InfWorks ICM predicted value of the non-monitoring unit, the InfWorks ICM predicted values of n monitoring units nearest to the non-monitoring unit and the STARMA predicted values of monitoring points corresponding to the n monitoring units into the trained neural network, and calculating to obtain the final ponding depth predicted value of the non-monitoring unit.
Claims (4)
1. A method for improving the accuracy of water depth forecast of a non-monitoring unit is characterized by comprising the following steps:
step (1) waterlogging depth forecast at waterlogging monitoring point
Calculating by using a STARMA model of rainfall water to obtain a water accumulation depth forecast value of each waterlogging monitoring point, wherein the water accumulation depth forecast value of the waterlogging monitoring point based on the model is referred to as the STARMA forecast value for short;
step (2) depth prediction and discretization of prediction value of surface water
Carrying out analog prediction on the surface water depth by utilizing an InfoWorks ICM model, carrying out spatial discretization on a surface water depth prediction value of the model, and shortening the surface water depth prediction value based on the InfoWorks ICM model to be an InfoWorks ICM prediction value;
step (3) surface discretization and classification
Discretizing the earth surface into a series of earth surface units in a discretization mode consistent with the step (2); dividing the surface unit into a surface unit containing the monitoring points and a surface unit not containing the monitoring points according to whether the surface unit contains the waterlogging monitoring points or not; the earth surface unit containing the monitoring point is hereinafter referred to as a monitoring unit, and the earth surface unit not containing the monitoring point is hereinafter referred to as a non-monitoring unit;
step (4) determining the waterlogging monitoring points corresponding to the monitoring units
Assuming that one monitoring unit corresponds to one waterlogging monitoring point: for one monitoring unit, if the unit only comprises one waterlogging monitoring point, the waterlogging monitoring point is used as the waterlogging monitoring point corresponding to the unit; if the unit comprises a plurality of waterlogging monitoring points, a waterlogging monitoring point is 'virtually' taken out, the monitoring value of the virtual waterlogging monitoring point is the mean value of the monitoring values of the plurality of waterlogging monitoring points, the forecast value of the virtual waterlogging monitoring point is the mean value of the forecast values of the plurality of waterlogging monitoring points, and the virtual waterlogging monitoring point is taken as the waterlogging monitoring point corresponding to the unit;
step (5) improving the accumulated water depth forecasting precision of the monitoring unit by using Bayesian model weighted average
For each monitoring unit, carrying out weighted average on an InfoWorks ICM predicted value of the monitoring unit and a STARMA predicted value of a corresponding monitoring point of the monitoring unit by using Bayesian model weighted average to obtain a fused predicted value, wherein the fused predicted value is used as a final accumulated water depth predicted value of the monitoring unit; calculating the weight of the InfoWorks ICM model and the STARMA model of the rainfall ponding by using an expectation-maximization algorithm;
step (6) constructing a neural network for improving the accuracy of non-monitoring unit water depth prediction
Constructing a neural network for improving the water depth forecasting precision of the non-monitoring units, and training the neural network by using the final forecast value, the InfoWorks ICM forecast value and the STARMA forecast value of the monitoring points corresponding to the monitoring units of the monitoring units; for any non-monitoring unit, the input of the neural network is the InfWorks ICM predicted value of the non-monitoring unit, the InfWorks ICM predicted values of n monitoring units nearest to the non-monitoring unit and the STARMA predicted values of the monitoring points corresponding to the n monitoring units, and the output of the neural network is the final accumulated water depth predicted value of the non-monitoring unit.
2. The method for improving the accuracy of the non-monitoring unit ponding depth forecast of claim 1, characterized by: and (3) the discretization mode in the step (2) is a regular grid mode or an irregular triangular grid mode.
3. The method for improving the accuracy of the non-monitoring unit ponding depth forecast of claim 1, characterized by: and (5) improving the accumulated water depth forecasting precision of the monitoring unit by using Bayesian model weighted average, which specifically comprises the following steps:
for each monitoring unit, improving the accumulated water depth forecasting precision by executing the steps of (i) - (iv);
normal conversion
Acquiring an InfWorks ICM predicted value sequence of a monitoring unit, a water accumulation depth monitoring data sequence of a monitoring point corresponding to the monitoring unit and a STARMA predicted value sequence of the monitoring point corresponding to the monitoring unit; respectively carrying out normal conversion on the sequences by using Box-Cox transformation to obtain related sequences conforming to normal distribution;
determining a Bayes model weighted average forecasting formula of surface water depth as follows:
wherein D is the forecast amount, namely the depth of accumulated water; o is accumulated water depth monitoring data of a monitoring point corresponding to the monitoring unit; p (d) 1 I O) is STARMA of the corresponding monitoring point of the monitoring unit under the condition of given monitoring data OPredicted value d 1 Posterior probability of (2), i.e. weight value w of STARMA model of rainfall ponding 1 ；p(d 2 I O) is the predicted value d of the monitoring unit InfoWorks ICM under the condition of given monitoring data O 2 The posterior probability of (a), i.e. the weight value w of the InfoWorks ICM model 2 ；
Is a mean value d 1 Variance, variance
Normal distribution of (2);
is a mean value d 2 Variance, variance
Normal distribution of (2); assume O, d in equation (1) 1 、d 2 All are subjected to normal conversion and accord with normal distribution;
(iii) weight calculation
Calculating the related sequence data conforming to the normal distribution in the formula (1) by an expectation-maximization algorithm
w 1 And w 2 ；
Method application
For the monitoring unit, firstly, carrying out normal conversion on the STARMA predicted value of the monitoring point corresponding to the monitoring unit and the InfWorks ICM predicted value of the monitoring unit by using Box-Cox conversion to obtain d' 1 And d' 2 Calculating to obtain a weighted average predicted value d' of the Bayes model by using a formula (2); then d' is subjected to inverse Box-Cox transformation, the numerical value after inverse transformation is used as the final accumulated water depth forecast value of the monitoring unit,
d′＝w 1 d′ 1 +w 2 d′ 2 (2)。
4. the method for improving the accuracy of the non-monitoring unit ponding depth forecast according to claim 1, characterized in that: the method for constructing the neural network for improving the prediction accuracy of the non-monitoring unit water depth specifically comprises the following steps:
determining neural network structure
The neural network adopts a three-layer structure and comprises an input layer, a hidden layer and an output layer, wherein the number of neurons in the hidden layer is a hyper-parameter and is determined through experiments;
② determining input and output of neural network
For any non-monitoring unit, the input of the neural network is the InfoWorks ICM predicted value of the non-monitoring unit, InfoWorks ICM predicted values of n monitoring units nearest to the non-monitoring unit and STARMA predicted values of monitoring points corresponding to the n monitoring units; the output of the neural network is the final accumulated water depth forecast value of the non-monitoring unit; n is a hyper-parameter and is determined through experiments;
training neural network
Firstly, a data set used by the neural network is constructed by utilizing a monitoring unit: for each monitoring unit, taking the InfoWorks ICM predicted value of the monitoring unit, the InfoWorks ICM predicted values of n other monitoring units nearest to the monitoring unit and STARMA predicted values of monitoring points corresponding to the n monitoring units as input parts of a data set, and taking the final ponding depth predicted value of the monitoring unit as an output part of the data set;
then, randomly dividing the data set into a training set and a test set, training a neural network by using the training set, and optimizing the neural network by adopting a gradient descent method;
method application
For each non-monitoring unit, inputting the InfoWorks ICM predicted value of the non-monitoring unit, the InfoWorks ICM predicted values of n monitoring units nearest to the non-monitoring unit and the STARMA predicted values of monitoring points corresponding to the n monitoring units into a trained neural network, and calculating to obtain the final ponding depth predicted value of the non-monitoring unit.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205091884U (en) * | 2015-10-30 | 2016-03-16 | 南京汉森思物联网科技有限公司 | Flood prevention system of intelligent city based on torrential rain forecasting model |
| CN108107488A (en) * | 2017-12-21 | 2018-06-01 | 广州积雨云科技有限公司 | Urban waterlogging method for early warning |
| CN110533885A (en) * | 2019-08-15 | 2019-12-03 | 江苏省气象服务中心 | Urban waterlogging monitoring and warning system and method for early warning |
| CN111882830A (en) * | 2020-07-31 | 2020-11-03 | 珠江水利委员会珠江水利科学研究院 | Urban waterlogging monitoring, forecasting and early warning method, device and system and storage medium |
| KR20200127091A (en) * | 2019-04-30 | 2020-11-10 | 경북대학교 산학협력단 | Apparatus and method for real-time flooding prediction |
| US20200387785A1 (en) * | 2019-06-05 | 2020-12-10 | Wuhan University | Power equipment fault detecting and positioning method of artificial intelligence inference fusion |
| CN113344305A (en) * | 2021-08-05 | 2021-09-03 | 中国水利水电科学研究院 | Rapid prediction method for rainstorm waterlogging event |
| CN114254561A (en) * | 2021-12-17 | 2022-03-29 | 奥格科技股份有限公司 | Waterlogging prediction method, waterlogging prediction system and storage medium |
-
2022
- 2022-05-27 CN CN202210592618.6A patent/CN114925923B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205091884U (en) * | 2015-10-30 | 2016-03-16 | 南京汉森思物联网科技有限公司 | Flood prevention system of intelligent city based on torrential rain forecasting model |
| CN108107488A (en) * | 2017-12-21 | 2018-06-01 | 广州积雨云科技有限公司 | Urban waterlogging method for early warning |
| KR20200127091A (en) * | 2019-04-30 | 2020-11-10 | 경북대학교 산학협력단 | Apparatus and method for real-time flooding prediction |
| US20200387785A1 (en) * | 2019-06-05 | 2020-12-10 | Wuhan University | Power equipment fault detecting and positioning method of artificial intelligence inference fusion |
| CN110533885A (en) * | 2019-08-15 | 2019-12-03 | 江苏省气象服务中心 | Urban waterlogging monitoring and warning system and method for early warning |
| CN111882830A (en) * | 2020-07-31 | 2020-11-03 | 珠江水利委员会珠江水利科学研究院 | Urban waterlogging monitoring, forecasting and early warning method, device and system and storage medium |
| CN113344305A (en) * | 2021-08-05 | 2021-09-03 | 中国水利水电科学研究院 | Rapid prediction method for rainstorm waterlogging event |
| CN114254561A (en) * | 2021-12-17 | 2022-03-29 | 奥格科技股份有限公司 | Waterlogging prediction method, waterlogging prediction system and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 郑姗姗: "基于STARMA模型的城市暴雨积水点积水短时预测", 《地理科学进展》 * |
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