JP-2026075423-A - Model generation device, flood probability prediction device, model generation method, model generation program, flood probability prediction method, flood probability prediction program, flood probability prediction system, and trained model

JP2026075423AJP 2026075423 AJP2026075423 AJP 2026075423AJP-2026075423-A

Abstract

[Problem] To enable highly accurate water level prediction using a water detector with a simpler configuration compared to a water level gauge. [Solution] The model generation device 3 generates a model that predicts the probability of new flooding occurring at specific locations P0 to Pm after a predetermined time. The model generation device 3 includes a water detector 2 that detects whether or not flooding occurs at specific locations, a training data creation unit 35 that creates a training dataset including training rainfall time series information RL for specific locations and training flooding probability information PL for specific locations after the latest time point of the training rainfall time series information RL based on the detection results of the water detector 2, and a model learning unit 36 that takes the training rainfall time series information RL from the training dataset as input to the model 4 and the training flooding probability information PL as output to the model 4, performs machine learning so that the input/output relationship of the model 4 approaches the input/output relationship of the training dataset, and generates a trained model 4A. [Selection Diagram] Figure 2

Inventors

小林亘

Assignees

学校法人東京電機大学

Dates

Publication Date: 20260508
Application Date: 20241022

Claims (16)

A model generation device for generating a model to predict the probability of new flooding occurring at a specific location after a predetermined time, A water detector installed at the aforementioned specific location detects whether or not flooding has occurred at the aforementioned specific location by detecting a change in the presence or absence of water at a predetermined water level. A training data creation unit creates a training dataset that includes training rainfall time series information relating to the rainfall at a specific location based on the detection results of the water detector, and training flood probability information relating to the probability that new flooding will occur at the specific location after the latest point in time of the training rainfall time series information. A model learning unit generates a trained model by using the training dataset created by the training data creation unit, taking the training rainfall time series information as input to the model and the training flood probability information as output to the model, and performing machine learning so that the input/output relationship of the model approaches the input/output relationship of the training dataset. Equipped with, Model generation device.
The aforementioned Teacher dataset includes a positive dataset and a negative dataset. In the aforementioned positive dataset, the input is rainfall time-series information extracted from past rainfall data, going back from the time when flooding was detected by the water detector to a predetermined time prior, and the probability of flooding occurring in the output is 1. In the negative dataset, the input is time-series rainfall information from past rainfall data for sections where flooding has not been detected by the water detector, and the probability of flooding occurring in the output is 0. The model generation apparatus according to claim 1.
The unit includes an explanatory variable setting unit for setting the time series pattern of the step size of the learning rainfall time series information, The training data creation unit creates the learning rainfall time series information based on the time series pattern of the step size set by the explanatory variable setting unit. A model generation apparatus according to claim 1 or 2.
The explanatory variable setting unit creates rainfall time series information using multiple time series patterns with different step sizes, performs preliminary training on the model for each time series pattern using the created rainfall time series information, and sets the time series pattern with the specified step size based on the results of the preliminary training. The model generation apparatus according to claim 3.
The system includes an explanatory variable setting unit for setting the measurement locations of the rainfall in the aforementioned learning rainfall time-series information, The training data creation unit creates the learning rainfall time series information based on the rainfall information of the measurement location set by the explanatory variable setting unit. A model generation apparatus according to claim 1 or 2.
The explanatory variable setting unit creates rainfall time series information for a section including the specific location and for multiple sections surrounding the section, performs preliminary training of the model for each of the multiple sections using the created rainfall time series information, and sets one of the multiple sections as the measurement location based on the results of the preliminary training. The model generation apparatus according to claim 5.
The machine learning of the aforementioned model learning unit uses logistic regression analysis. The model generation apparatus according to claim 1.
A flood probability prediction device for predicting the probability of new flooding occurring at a specific location after a predetermined time, A trained model obtained by machine learning the correspondence between a training explanatory variable, which includes training rainfall time series information relating to the time series information of rainfall at the specified location, and a training target variable, which includes training flood probability information relating to the probability of new flooding occurring at the specified location after the latest point in time of the training rainfall time series information. A prediction unit inputs explanatory variables into the trained model, including the time at which the prediction will be made and time-series information on rainfall at the specific location prior to the time at which the prediction will be made, and outputs information on the target variable related to the information of the explanatory variables, and predicts the probability after the time at which the prediction will be made based on the target variable. Equipped with, Flooding probability prediction device.
The system includes a flood occurrence determination unit that determines whether or not new flooding will occur at the specific location after the specified time, based on the probability predicted by the prediction unit. The flooding probability prediction device according to claim 8.
The flood occurrence determination unit determines that flooding will occur when the probability predicted by the prediction unit exceeds a predetermined threshold. The flooding probability prediction device according to claim 9.
A model generation method for generating a model to predict the probability of new flooding occurring at a specific location after a predetermined time, A detection step in which a water detector installed at the specified location detects whether or not flooding has occurred at the specified location by detecting a change in the presence or absence of water at a predetermined water level, A training data creation step to create a training dataset that includes training rainfall time series information relating to the rainfall at a specific location based on the detection results of the water detector, and training flood probability information relating to the probability that new flooding will occur at the specific location after the latest point in time of the training rainfall time series information, A model training step involves using the training dataset created in the training data creation step, taking the training rainfall time series information as input to the model and the training flood probability information as output to the model, performing machine learning so that the input-output relationship of the model approaches the input-output relationship of the training dataset, and generating a trained model; including, Model generation method.
A model generation program for generating a model to predict the probability of new flooding occurring at a specific location after a predetermined time, A detection function that detects whether or not flooding has occurred at the specified location by detecting a change in the presence or absence of water at a predetermined water level using a water detector installed at the specified location, A training data creation function that creates a training dataset including training rainfall time series information relating to the rainfall at a specific location based on the detection results of the water detector, and training flood probability information relating to the probability that new flooding will occur at the specific location after the latest point in time of the training rainfall time series information. A model learning function generates a trained model by using the training dataset created by the aforementioned training data creation function, taking the training rainfall time series information as input to the model and the training flood probability information as output to the model, and performing machine learning so that the input/output relationship of the model approaches the input/output relationship of the training dataset. including, Make the computer execute it. Model generation program.
A flood probability prediction method for predicting the probability of new flooding occurring at a specific location after a predetermined time, A model setting step involves setting up a trained model in which a correspondence relationship is obtained by machine learning between a training explanatory variable, which includes training rainfall time series information relating to the time series information of rainfall at the specified location, and a training target variable, which includes training flood probability information relating to the probability of new flooding occurring at the specified location after the latest point in time of the training rainfall time series information; A prediction step in which the trained model set in the model setting step is input to the trained model, which includes the time at which the prediction will be made and includes forecast rainfall time series information relating to the rainfall at the specific location before the time at which the prediction will be made, and outputs information of the target variable relating to the information of the explanatory variables, and predicts the probability after the time at which the prediction will be made based on the target variable, including, Methods for predicting the probability of flooding.
A flood probability prediction program for predicting the probability of new flooding occurring at a specific location after a predetermined time, A model setting function that sets up a trained model in which the correspondence between a training explanatory variable, which includes training rainfall time series information relating to the time series information of rainfall at the specified location, and a training target variable, which includes training flood probability information relating to the probability of new flooding occurring at the specified location after the latest point in time of the training rainfall time series information, is obtained by machine learning. A prediction function that inputs explanatory variables, including the implementation time for performing the prediction and time-series information on rainfall at a specific location prior to the implementation time, into the trained model set by the model setting function, outputs information on the target variable related to the information of the explanatory variables, and predicts the probability after the implementation time based on the target variable, Make the computer execute it. A program for predicting the probability of flooding.
A flood probability prediction system for predicting the probability of new flooding occurring at a specific location after a predetermined time, A model generation device for generating a model for predicting the probability of new flooding occurring at a specific location after a predetermined time point, A flood probability prediction device for predicting the probability that new flooding will occur at the specified location after a predetermined time, It is equipped with, The aforementioned model generation device is A water detector installed at the aforementioned specific location detects whether or not flooding has occurred at the aforementioned specific location by detecting a change in the presence or absence of water at a predetermined water level, A training data creation unit creates a training dataset that includes training rainfall time series information relating to the rainfall at a specific location based on the detection results of the water detector, and training flood probability information relating to the probability of new flooding occurring at the specific location after the latest point in time of the training rainfall time series information. A model learning unit generates a trained model by using the training dataset created by the training data creation unit, taking the training rainfall time series information as input to the model and the training flood probability information as output to the model, and performing machine learning so that the input/output relationship of the model approaches the input/output relationship of the training dataset. Equipped with, The flooding probability prediction device is, A trained model obtained by machine learning the correspondence between a training explanatory variable, which includes training rainfall time series information relating to the time series information of rainfall at the specified location, and a training target variable, which includes training flood probability information relating to the probability of new flooding occurring at the specified location after the latest point in time of the training rainfall time series information. A prediction unit inputs explanatory variables into the trained model, including the time at which the prediction will be made and time-series information on rainfall at the specific location prior to the time at which the prediction will be made, and outputs information on the target variable related to the information of the explanatory variables, and predicts the probability after the time at which the prediction will be made based on the target variable. Equipped with, Flooding probability prediction system.
A trained model obtained through machine learning that establishes the correspondence between a training explanatory variable, which includes training rainfall time series information for predicting the probability of new flooding occurring after a predetermined point in time, and a training target variable, which includes training flooding probability information for predicting the probability of new flooding occurring after the latest point in time of the training rainfall time series information at the said specific location.

Description

This disclosure relates to a model generation device, a flood probability prediction device, a model generation method, a model generation program, a flood probability prediction method, a flood probability prediction program, a flood probability prediction system, and a trained model. Climate change is increasing the frequency of heavy rainfall, raising concerns that more rainwater will flow onto roads in a shorter amount of time than before. This is exacerbated in areas where urbanization has led to increased surface areas where rainwater cannot easily penetrate the ground. Road sections that cannot adequately drain the incoming rainwater will become flooded, requiring traffic restrictions. For road users, residents near flooded areas, and road administrators, predicting the occurrence of traffic restrictions due to road flooding is useful for ensuring safety and efficiency in daily life and travel. For example, based on predictive information, road users can determine safe routes, and road administrators can manage roads more efficiently. As a technology related to such flood prediction, for example, Patent Documents 1 and 2 disclose a method for measuring time-series water level data using water level gauges at multiple points along a river targeted for water level prediction, and then using the measured water level data to predict the river's water level. On the other hand, Patent Document 3 discloses a configuration in which a water detector is installed at the target location to detect when a predetermined water level has been reached, for purposes such as detecting road flooding. Japanese Patent Application Publication No. 09-256338Japanese Patent Publication No. 2020-134300Japanese Patent Publication No. 2023-66488 This figure shows the overall configuration of the flood probability prediction system according to the first embodiment.Functional block diagram of the model generation device according to the first embodimentThis figure shows an example of a time-series pattern for the step size of the learning rainfall time-series information set by the explanatory variable setting unit.A diagram showing an example of rainfall data when flooding is detected.Functional block diagram of the flood probability prediction device according to the first embodimentHardware configuration diagram of the model generation device and flood probability prediction device.Flowchart of model learning control according to the first embodimentFlowchart of flood probability prediction control according to the first embodimentThis figure shows an example of the prediction results for the probability of flooding occurring according to the first embodiment.This figure shows an example of flood prediction and evaluation according to the first embodiment.A diagram showing an example of the occurrence rate of a flood probability of 0.5 or higher.Functional block diagram of the model generation device according to the second embodimentThis figure shows an example of a time-series pattern of multiple step sizes set by the explanatory variable setting unit of the second embodiment.This figure illustrates a modified example of the explanatory variable setting unit.Functional block diagram of the flood probability prediction device according to the second embodiment. The embodiments will be described below with reference to the attached drawings. To facilitate understanding of the explanation, the same reference numerals are used for identical components in each drawing whenever possible, and redundant explanations are omitted. [First Embodiment] A first embodiment will be described with reference to Figures 1 to 12. Figure 1 is a diagram showing the overall configuration of the flooding probability prediction system 1 according to the first embodiment. The flood probability prediction system 1 is a system for predicting the probability (flood occurrence probability) Pe of new flooding occurring after a predetermined time in specific locations P1 to Pm where water detectors 2 are installed. The specific locations P1 to Pm, which are the targets for predicting the flood occurrence probability Pe in this system, include places where water does not normally exist, such as roads and residential areas. In particular, it includes places with a relatively high probability of flooding during rainfall, such as lowlands, depressions, and underpasses, as well as urban facilities with a relatively high probability of flooding, such as basements, subway stations, underground shopping areas, underground parking lots, and electrical rooms. Furthermore, specific locations P1 to Pm can also include places where water normally exists and where there is a relatively high probability of overflow during rainfall, such as rivers, lakes, reservoirs, and their banks. In this embodiment, the term "flooding" is used as a broader concept encompassing the aforementioned events such as inundation, flooding, and overflow. Furthermore, while the specific locatio