Water level prediction system

Abstract

This system provides a simple prediction method for forecasting river water levels. [Solution] The water level prediction system predicts the future water level at a prediction point in a river and comprises: a rainfall acquisition unit 1 that acquires the future rainfall amount of the catchment area for the prediction point; a storage unit 2 that stores parameters of a first-order lag system that takes the rainfall amount as input and outputs the water level at the prediction point in the river; and a water level prediction unit 3 that takes the rainfall amount acquired by the rainfall acquisition unit 1 as input and calculates the predicted water level at the prediction point in the river from the first-order lag system using the parameters of the first-order lag system stored in the storage unit 2, wherein the time constant of the first-order lag system is set according to the water level at the prediction point in the river.

Description

Technical Field

[0001] The present disclosure relates to a water level prediction system.

Background Art

[0002] As a water level prediction system for predicting the water level of a river, those using AI, neural networks, probability, and statistical methods are variously known (for example, Non-Patent Document 1). Those water level prediction systems target the middle and lower reaches of large rivers.

[0003] Further, Patent Document 1 describes a water level prediction system for a small river. Patent Document 1 describes estimating the precipitation flowing into the prediction point and predicting the water level based on the estimated precipitation and a river model.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Non-Patent Documents

[0005]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] Small and medium-sized rivers in mountainous areas or adjacent regions often lack the full-fledged levees found in the middle and lower reaches of large rivers. This is because the rivers have steep gradients and the water flows quickly even during heavy rains, preventing flooding even without levees.

[0007] Traditional water level forecasts generally focused on Class A rivers managed by the national government, and water level forecasts had not been conducted for smaller rivers managed by prefectural governments. This was due to the large number of smaller rivers and the fact that, as mentioned above, they were not considered likely to flood.

[0008] However, due to the effects of recent global warming, small and medium-sized rivers such as the Kinugawa and Kumagawa are overflowing, resulting in unprecedented flood damage. In the future, it is expected that many major floods resulting in loss of life will occur in the upper reaches of these small and medium-sized rivers. Therefore, there has been a need for an inexpensive and simple water level prediction system that can predict water levels even in small and medium-sized rivers.

[0009] Furthermore, while Patent Document 1 describes a water level prediction system that can be applied to small rivers, it was difficult to create a river model with high prediction accuracy.

[0010] This disclosure is made in light of the above circumstances and aims to provide a water level prediction system that can easily predict river water levels. [Means for solving the problem]

[0011] One aspect of this disclosure is, A water level prediction system that predicts future water levels at prediction points in rivers, A rainfall acquisition unit that acquires the future rainfall amount for the catchment area at the aforementioned prediction point, A storage unit that stores parameters for a first-order lag system, which takes the rainfall amount as input and outputs the water level of the river at the predicted point, A water level prediction unit that takes the rainfall obtained by the rainfall acquisition unit as input and calculates a predicted water level at the predicted location of the river from the first-order lag system using the parameters of the first-order lag system stored in the storage unit; In the water level prediction system, the time constant of the first-order lag system is set according to the water level at the predicted location of the river.

Advantages of the Invention

[0012] In the above aspect, the predicted water level is calculated by a first-order lag system that takes rainfall as input and outputs the water level at the predicted location of the river, and the time constant of the first-order lag system is set according to the water level at the predicted location of the river. Therefore, the predicted water level can be easily calculated.

[0013] As described above, according to the above aspect, it is possible to provide a water level prediction system that can easily predict the water level of a river.

Brief Description of the Drawings

[0014] [Figure 1] It is a block diagram showing the configuration of the water level prediction system in the first embodiment. [Figure 2] It is a diagram schematically showing the catchment area of the river for which the water level is to be predicted. [Figure 3] It is a diagram schematically showing the configuration of the satellite radio wave intensity meter. [Figure 4] It is a diagram showing the rainfall in the vicinity of the predicted location and the water level at the predicted location. [Figure 5] It is a diagram schematically showing the relationship between the water level, the river width, and the decrease in the water level. [Figure 6] It is a diagram schematically showing the relationship between the gradient of the river and the decrease in the water level. [Figure 7] It is a diagram schematically showing the relationship between the difference in river scale and the decrease in the water level. [Figure 8] It is a diagram schematically showing the river and the predicted location of the water level. [Figure 9] It is a diagram schematically showing the delay of the rain that fell at point A. [Figure 10]A diagram schematically showing the delay of rain that fell at location B. [Figure 11] A diagram schematically showing the rainfall amount at the prediction location. [Figure 12] A diagram showing a flowchart for explaining the operation of the water level prediction system in the first embodiment. [Figure 13] A graph showing the curves of the actual water level and the predicted water level. [Figure 14] A graph showing the curves of the actual water level and the predicted water level. [Figure 15] A graph showing the curves of the actual water level and the predicted water level. [Figure 16] A graph showing the relationship between the water level at the prediction location and the value of the time constant. [Figure 17] A graph showing the rainfall intensity, the predicted water level, and the actual water level. [Figure 18] A graph showing the predicted water level and the actual water level. [Figure 19] A graph showing the predicted water level and the actual water level.

Mode for Carrying Out the Invention

[0015] The water level prediction system is a water level prediction system that predicts the future water level at a prediction location of a river, and includes a rainfall acquisition unit that acquires the future rainfall amount in the catchment area for the prediction location, a storage unit that stores the parameters of a first-order lag system that takes the rainfall as input and outputs the water level at the prediction location of the river, and a water level prediction unit that takes the rainfall acquired by the rainfall acquisition unit as input and calculates the predicted water level at the prediction location of the river from the first-order lag system using the parameters of the first-order lag system stored in the storage unit. The time constant of the first-order lag system is set according to the water level at the prediction location of the river.

[0016] In the above water level prediction system, the time constant in the first-order lag system may be set based on measuring the water level at the prediction location of the river after the rain stops and based on the decline characteristics of the measured water level. The time constant can be set accurately, and the prediction accuracy of the water level can be improved.

[0017] In the above-described water level prediction system, the rainfall acquisition unit may acquire rainfall forecast data from the Japan Meteorological Agency as rainfall data.

[0018] In the above-described water level prediction system, the water level prediction unit may convert the Japan Meteorological Agency's rainfall forecast data into data with equal time intervals and smoothed values, and use that data as the amount of rainfall per unit time. This can improve the accuracy of the prediction.

[0019] In the above-described water level prediction system, the rainfall acquisition unit may acquire data from the Japan Meteorological Agency's precipitation nowcast (5 minutes) as rainfall amounts from the current time up to one hour ahead, and data from the Japan Meteorological Agency's preliminary analyzed rainfall and preliminary short-term precipitation forecast as rainfall amounts beyond one hour ahead.

[0020] The above water level prediction system further includes a water level measuring unit that measures the actual water level at the prediction point in the river. The water level prediction unit performs a first correction on the predicted water level, and the first correction may be performed so that the value of the predicted water level at the present time matches the value of the actual water level at the present time measured by the water level measuring unit. This can improve the accuracy of water level prediction.

[0021] In the above water level prediction system, the water level prediction unit performs a second correction on the predicted water level after the first correction. The second correction may be performed so that the time change value of the predicted water level after the first correction at the current time matches the time change value of the actual water level at the current time measured by the water level measurement unit. This can further improve the accuracy of water level prediction.

[0022] In the above-described water level prediction system, the water level prediction unit may perform both the first and second corrections for water levels from the current time to a predetermined time in the future, and perform only the first correction and not the second correction for water levels beyond the predetermined time. This improves the prediction accuracy of water levels beyond the predetermined time.

[0023] In the above-described water level prediction system, the water level prediction unit may perform both a first correction and a second correction. If there is a section in the corrected predicted water level that exceeds a predetermined upper limit, the first correction may be applied to that section, and the second correction may not be applied. This can further improve the accuracy of water level prediction.

[0024] In the above-described water level prediction system, the system further includes a water level measuring unit that measures the actual water level at the prediction point in the river. The water level prediction unit corrects the predicted water level by multiplying it by a coefficient, and the coefficient may be determined such that the sum of the differences between the actual water level measured by the water level measuring unit and the predicted water level at a certain time is zero. This can further improve the accuracy of water level prediction.

[0025] In the above-described water level prediction system, the system further includes a water level measuring unit that measures the actual water level at the prediction point in the river. The water level prediction unit corrects the predicted water level by multiplying it by a coefficient, and the coefficient may be determined so as to minimize the sum of squares of the difference between the actual water level measured by the water level measuring unit and the predicted water level over a certain period of time. This can further improve the accuracy of water level prediction.

[0026] In the above-described water level prediction system, the system further includes a water level measuring unit that measures the actual water level at the prediction point in the river. The water level prediction unit may correct the predicted water level, calculate the inflection point of the actual water level, and make the absolute value of the correction amount after the time of the inflection point smaller than the absolute value of the correction amount before the time of the inflection point. This can further improve the accuracy of water level prediction.

[0027] In the above-described water level prediction system, a satellite signal strength meter may be provided in the catchment area to receive satellite broadcast signals. The water level prediction unit may calculate rainfall from the signal strength received by the satellite signal strength meter and use the calculated rainfall to correct the rainfall amount acquired by the rainfall acquisition unit. This can improve the accuracy of water level prediction. In particular, in mountainous areas, weather radar signals are focused on locations higher than the mountains, and rain clouds at altitudes lower than the mountains are not visible. Therefore, correcting the rainfall amount using the satellite signal strength meter can improve the accuracy of water level prediction.

[0028] In the above-described water level prediction system, the water level prediction unit may calculate the delay time it takes for water to reach the prediction point from the rainfall point to the prediction point based on the length of the river channel from the rainfall point to the prediction point and the flow velocity of the water flowing in the river, and then correct the rainfall amount for the delay time. This can improve the accuracy of water level prediction.

[0029] The above water level prediction system may further include a display unit that shows the predicted water level and rainfall based on the precipitation nowcast (5 minutes) and the preliminary analyzed rainfall and preliminary short-term precipitation forecast. The update of the predicted water level may be synchronized with the update of the precipitation nowcast (5 minutes) and the preliminary analyzed rainfall and preliminary short-term precipitation forecast. The predicted water level can be displayed to consumers in an easy-to-understand manner.

[0030] (First Embodiment) 1. Configuration of the water level prediction system Figure 1 is a block diagram showing the configuration of the water level prediction system in the first embodiment. The water level prediction system in the first embodiment is a system that predicts the water level at prediction points in a river that is subject to water level prediction. As shown in Figure 1, the water level prediction system in the first embodiment includes a rainfall acquisition unit 1, a storage unit 2, a water level prediction unit 3, a display unit 4, a flow velocity meter 5, a water level meter 6, a satellite radio wave intensity meter 7, and a rain gauge 8.

[0031] The rainfall acquisition unit 1 acquires rainfall data within the catchment area 20 from the Japan Meteorological Agency server 10 via the network NW. The rainfall acquisition unit 1 also acquires various data from the flow meter 5, water level meter 6, satellite signal strength meter 7, and rain gauge 8 from the cloud server 11 via the network NW.

[0032] Figure 2 schematically shows the river for which water level predictions are made and its catchment area 20. The catchment area 20 is the area upstream of the prediction point on the river, and is the area where rainwater flows into the river during rainfall.

[0033] The rainfall data consists of predicted rainfall amounts for each point in the 20-point drainage area. For the period from the current time to one hour ahead, precipitation nowcast (5-minute) data is used; for periods beyond one hour ahead, preliminary analyzed rainfall and preliminary short-term precipitation forecast data are used. In addition, actual rainfall data from prior to the current time may also be used.

[0034] The precipitation nowcast (5-minute) data predicts rainfall intensity every 5 minutes for each 1km square mesh, up to 1 hour ahead. The preliminary analyzed rainfall and preliminary short-term precipitation forecast data predicts rainfall intensity every 10 minutes for each 1km square mesh, from 1 hour to 6 hours ahead, and predicts rainfall amount every hour for each 5km square mesh, from 6 hours to 15 hours ahead.

[0035] For the range of 30 minutes ahead or 1 hour ahead from the current time, high-resolution precipitation nowcast data may be used instead of the 5-minute precipitation nowcast. This can further improve the accuracy of water level predictions. Alternatively, rainfall data provided by other Japan Meteorological Agency sources may be used. Specifically, the nationwide precipitation nowcast GPV, analyzed rainfall, short-term precipitation forecasts, and 15-hour precipitation forecasts may be used.

[0036] Furthermore, rainfall data may be obtained from sources other than the Japan Meteorological Agency, or from both the Japan Meteorological Agency and other sources.

[0037] The memory unit 2 stores various parameters of a first-order lag system, which takes rainfall as input and outputs the water level at the river water level prediction point, as well as various data acquired from the flow meter 5, water level meter 6, satellite signal strength meter 7, and rain gauge 8. The parameters of the first-order lag system include a time constant. The memory unit 2 also stores map data of the catchment area 20. Furthermore, the memory unit 2 stores the channel length from each point in the catchment area 20 to the prediction point.

[0038] The time constant is set according to the water level at the prediction point in the river. This allows the shape of the river and other factors to be reflected in the water level prediction, thereby improving the accuracy of the prediction. For a first-order lag system, the time constant is set, for example, by measuring the water level at the prediction point in the river after the rain has stopped and based on the characteristics of the decrease in the measured water level. Since the water level stabilizes after the rain stops compared to during rainfall, the time constant can be determined with high accuracy. Also, during a rise in water level, there are fluctuations in water level due to rainfall, making it difficult to measure the time constant. By using the characteristics during a decrease in water level, the time constant can be measured simply.

[0039] The water level prediction unit 3 calculates the predicted water level at the prediction point as a response characteristic of a first-order lag system, taking the rainfall amount from the rainfall data acquired by the rainfall acquisition unit 1 for the catchment area 20 as input. The time constant of the first-order lag system stored in the memory unit 2 is used for the calculation of the first-order lag system. As described above, the time constant is not a constant used in general first-order lag systems, but is set according to the water level at the prediction point of the river. Therefore, in the following, it may be called a nonlinear first-order lag system to distinguish it from general first-order lag systems. In addition, the rainfall data is corrected using various data acquired from the flow meter 5, water level meter 6, satellite radio wave intensity meter 7, and rain gauge 8. The specific method for calculating the predicted water level will be described later.

[0040] The display unit 4 is a device that displays the water level predicted by the water level prediction unit 3. For example, it displays the predicted water level on the screen of a smartphone, tablet, or PC.

[0041] The predicted water level may be displayed as follows: A map of the area near the prediction point is displayed, the predicted water level is divided into different colors according to its height, and the color of the prediction point on the map is changed to the color corresponding to the predicted water level. The predicted water level may also be updated in synchronization with the Japan Meteorological Agency's precipitation nowcast (5 minutes) and the updates of the preliminary analyzed rainfall and preliminary short-term precipitation forecast. This makes the predicted water level easy for consumers to understand.

[0042] Furthermore, a warning may be displayed if the predicted water level exceeds a specified level. The warning may also be given by voice. Additionally, if the predicted water level exceeds a specified level, information on the predicted water level may be transmitted to smartphones, tablet devices, etc., owned by the head of the local government or administrator to notify them.

[0043] The flow meter 5 is a device that measures the flow velocity of water flowing in a river. The flow meter 5 also has a communication device and transmits the measured flow velocity data to the cloud server 11. As shown in Figure 2, multiple flow meters 5 are placed in the river to be predicted, upstream of the prediction point. The spacing between the flow meters 5 is, for example, every 1 to 5 km of the river's channel length.

[0044] The water level gauge 6 is a device for measuring the water level of a river. The water level gauge 6 also has a communication device and transmits the measured water level data to the cloud server 11. As shown in Figure 2, multiple water level gauges 6 are placed in the vicinity of the prediction point in the river being predicted, and further upstream from the prediction point. The spacing between the water level gauges 6 is, for example, every 1 to 5 km of the river's channel length.

[0045] The satellite signal strength meter 7 is a device that receives satellite broadcast signals and measures their reception strength. The satellite signal strength meter 7 transmits the satellite signal strength data to the cloud server 11. As shown in Figure 2, multiple satellite signal strength meters 7 are installed within the water catchment area 20. For example, they are placed at intervals of approximately 5 to 10 km square.

[0046] Figure 3 is a schematic diagram showing the configuration of the satellite radio wave intensity meter 7. As shown in Figure 3, the satellite radio wave intensity meter 7 includes an antenna 70, a data generation unit 71, a solar panel 72, a power supply unit 73, and a wireless device 74.

[0047] Antenna 70 is a device that receives radio waves from satellite broadcasting. The frequency of the radio waves to be received is, for example, in the 12 GHz band. Data generation unit 71 is a device that generates data on the received strength of the radio waves received by antenna 70. Solar panel 72 is a device that converts solar energy into electricity and outputs it. Power supply unit 73 is a device that drives data generation unit 71 and wireless device 74. Power supply unit 73 also has a storage battery. This storage battery is charged by the power from solar panel 72. Wireless device 74 transmits the received strength data generated by data generation unit 71 to cloud server 11.

[0048] The satellite signal strength data measured by the satellite signal strength meter 7 is used to measure rainfall in the area where the satellite signal strength meter 7 is installed. There is a correlation between the level of received satellite signals and rainfall. Rainfall attenuates satellite signals, and the amount of attenuation is related to the rainfall intensity. Therefore, rainfall can be calculated from the satellite signal strength data.

[0049] The rain gauge 8 is a device that measures rainfall. The rain gauge 8 also has a communication device and transmits rainfall data to the cloud server 11. As shown in Figure 2, multiple rain gauges 8 are installed within the catchment area 20. Rainfall is measured by the rain gauges 8, for example, every 1 to 5 minutes.

[0050] 2. Overview of Water Level Forecast Next, an overview of the water level prediction in the first embodiment will be described.

[0051] To predict the water level of a river at a specific prediction point, we will examine the relationship between rainfall and water level at that point. A basic model for water level prediction is the tank model. This model approximates the relationship between rainfall and water level to the relationship between the amount of water flowing into and out of a tank. In other words, the water level is predicted using a first-order lag system, where rainfall at the prediction point is the input and the water level at the prediction point is the output.

[0052] Figure 4 shows the rainfall amount and water level at the predicted location. If there is pulse-like rainfall at the predicted location at a certain time t0, the water level will instantaneously increase to a certain value at time t0 and then gradually decrease. Here, we define the time constant T as the time from t0 to t1, where the water level becomes 0 with respect to the tangent line of the water level decrease curve at time t0.

[0053] Here, the actual water level is influenced by factors such as the river's shape, gradient, and the time it takes for rainwater to reach the river. For example, as shown in Figure 5, if the riverbank is sloped, the river widens as the water level rises. Therefore, when the water level is low, the decrease in water level is gradual, and the time constant T is considered to be large. On the other hand, when the water level is high, the decrease in water level is rapid, and the time constant T is considered to be small.

[0054] Furthermore, as shown in Figure 6, when the river gradient is gentle, the water flow velocity slows down, so the decrease in water level is gradual, and the time constant T is thought to be large. On the other hand, when the river gradient is steep, the water flow velocity increases, so the decrease in water level is rapid, and the time constant T is thought to be small.

[0055] Thus, in actual rivers, the time constant T is thought to vary depending on the river's shape, gradient, etc. Figure 7 schematically shows the decrease in water level for large, medium, and small rivers. In medium and small rivers, the river width is narrow, the riverbed is shallow, and the gradient is steep, so the time constant T tends to be small. In large rivers, the river width is wide, the riverbed is deep, and the gradient is gentle, so the time constant T tends to be large. Also, the rise in water level during rainfall is small in large rivers, while it is large in medium and small rivers.

[0056] Therefore, the inventors considered that if the time constant T of the first-order lag system were not kept constant but treated as a function of the water level, it would be possible to account for the fact that the time constant T varies depending on the shape and gradient of the river. In other words, they came up with the idea of ​​predicting the water level using a nonlinear first-order lag system with rainfall as the input and water level as the output, where the time constant T is a function of the water level. As described later, they found that the water level could be predicted with high accuracy using a nonlinear first-order lag system. The water level prediction system in the first embodiment is a system that predicts the water level using such a nonlinear first-order lag system where the time constant T is a function of the water level.

[0057] Furthermore, in actual water level predictions, there is a time delay between the point of rainfall and the point of prediction. Figure 8 is a schematic diagram showing a river and water level prediction points. As shown in Figure 8, point A is taken upstream of the prediction point in the river, and point B is taken even further upstream of point B. Then, it is assumed that there was pulse-like rainfall at points A and B at a certain time t0.

[0058] Rain that falls at point A flows into the river and travels along the river to the prediction point. In other words, as shown in Figure 9, the rain that falls at point A reaches the prediction point with a delay corresponding to the length of the channel from point A to the prediction point. For example, if the length of the channel from point A to the prediction point is LA, the velocity of the water flowing in the river is v, and the time when the water reaches the prediction point is tA, then tA = t0 + LA / v.

[0059] Furthermore, as shown in Figure 10, rain that falls at point B also reaches the prediction point with a delay corresponding to the length of the channel from point A to the prediction point. Here, since point B is upstream of point A, it reaches the prediction point even later than the rain that falls at point A. If the time when the rain reaches the prediction point is tB, then tB > tA.

[0060] The rainfall reaching the predicted location is the sum of the rain that fell at location A and the rain that fell at location B. Therefore, as shown in Figure 11, the amount of rainfall at the predicted location is the sum of the rainfall at location A delayed by tA and the rainfall at location B delayed by tB. This total amount of rainfall is used as the input to a nonlinear first-order lag system.

[0061] As described above, the rainfall amount used as input to the nonlinear first-order lag system is corrected for a delay corresponding to the channel length from the rainfall point to the prediction point. This improves the accuracy of water level prediction.

[0062] 3. Operation of the water level prediction system Next, the operation of the water level prediction system in the first embodiment will be described with reference to the flowchart in Figure 12.

[0063] First, the rainfall acquisition unit 1 acquires rainfall forecast data from the Japan Meteorological Agency server 10 and stores it in the storage unit 2. For the range of 1 hour from the current time, it acquires precipitation nowcast (5 minutes) data as rainfall forecast data, and for the range of more than 1 hour, it acquires preliminary analyzed rainfall and preliminary short-term precipitation forecast data. Then, the water level prediction unit 3 compares the rainfall forecast data with the map data of the catchment area 20 stored in the storage unit 2 and extracts the rainfall forecast data for the catchment area 20 (step S1 in Figure 12).

[0064] Furthermore, the rainfall acquisition unit 1 acquires various data from the flow meter 5, water level meter 6, satellite signal strength meter 7, and rain gauge 8. The storage unit 2 stores the various data acquired by the rainfall acquisition unit 1.

[0065] In parallel with this, the water level prediction unit 3 calculates the amount of rainfall in the area where the satellite signal strength meter 7 is installed from the satellite signal strength data. The relationship between satellite signal strength and rainfall is stored in the memory unit 2 beforehand, and the rainfall is calculated using this relationship. The memory unit 2 stores the calculated rainfall.

[0066] Next, the water level prediction unit 3 corrects the rainfall forecast data based on the amount of rainfall in the area where the satellite radio wave intensity meter 7 is installed, as determined by the satellite radio wave intensity meter 7, and the amount of rainfall in the area where the rain gauge 8 is installed, as measured by the rain gauge 8 (step S2 in Figure 12). For example, the Japan Meteorological Agency's rainfall data is divided into 1-minute intervals, and the data for 1 minute ahead from the current time is replaced with the amount of rainfall determined from the satellite radio wave intensity meter 7 or the amount of rainfall measured by the rain gauge 8.

[0067] The Japan Meteorological Agency's rainfall data predicts rainfall by detecting rain clouds at an altitude of around 3000m using radar reflection to avoid reflection from mountains in mountainous areas. However, the influence of mountain shapes is not always fully considered. Therefore, rainfall from rain clouds lower than the mountains may not be predicted accurately. In contrast, rainfall measurement using satellite radio wave intensity can accurately measure rainfall from rain clouds lower than the mountains.

[0068] In this way, by correcting the Japan Meteorological Agency's rainfall forecast data with rainfall measured by satellite radio wave intensity gauges 7 and rain gauges 8, the accuracy of rainfall predictions can be improved. As a result, the accuracy of water level predictions can be improved.

[0069] Next, the water level prediction unit 3 corrects the rainfall forecast data based on the delay time of the river water (step S3 in Figure 12). Rain that falls at a certain point in the catchment area 20 reaches the prediction point through the river. Therefore, it is delayed according to the length of the river channel and the flow velocity. This delay time can be obtained by dividing the length of the channel from the rainfall point to the prediction point by the flow velocity. The water level prediction unit 3 then calculates the delay time from the length of the channel from each point to the prediction point stored in the memory unit 2 and the flow velocity measured by the flow meter 5. Then, it shifts the amount of rainfall at each point by the delay time. For example, if the amount of rainfall at a certain point i is ui(t) and the delay time is ti, then it becomes ui(t-ti). In this way, by correcting the rainfall forecast data by the delay time, the accuracy of water level prediction can be improved.

[0070] In the first embodiment, the flow velocity measured by the flow meter 5 is used to calculate the delay time, but the flow velocity predicted from rainfall or the like may also be used. Alternatively, the predicted flow velocity may be corrected using the flow velocity measured by the flow meter 5, and the delay time may be calculated using the corrected flow velocity. Furthermore, the delay time may be corrected using the water level measured by each water level gauge.

[0071] Next, the water level prediction unit 3 divides the rainfall amount at each point in the rainfall forecast data into equal intervals at predetermined time intervals and converts it into rainfall amount per unit time. It then corrects and smooths the rainfall amount per unit time so that the temporal change in rainfall amount becomes smoother (step S4 in Figure 12). The water level prediction unit 3 then calculates the total rainfall amount by adding up the rainfall amounts at each point (rainfall amount per unit time). Alternatively, the rainfall amounts at each point may be added up first and then smoothed. For example, the time course of rainfall may be divided into predetermined intervals, each interval may be approximated by a polynomial, and these polynomials may be joined together.

[0072] Next, the water level prediction unit 3 calculates the predicted water level, which is the output, from a first-order lag system that takes the smoothed rainfall amount as input and outputs the water level at the prediction point (step S5 in Figure 12). Here, the time constant of the first-order lag system is a value that changes according to the water level at the prediction point.

[0073] Next, the water level prediction unit 3 corrects the calculated predicted water level (step S6 in Figure 12). The specific correction method will be explained with reference to Figure 13. Figure 13(a) is a graph showing the curves of the actual water level and the predicted water level before correction. As shown in Figure 13(a), the actual water level at the present time and the calculated predicted water level at the present time usually do not match.

[0074] First, as shown in Figure 13(b), a correction is made to the predicted water level curve by multiplying it by a coefficient so that the actual water level near the prediction point measured by the water level gauge 6 at the current time matches the calculated predicted water level at the current time. This is hereafter referred to as the first correction. Next, as shown in Figure 13(c), the predicted water level curve is rotated around the water level value at the current time so that the slope of the actual water level at the current time (time change of the actual water level) matches the slope of the predicted water level at the current time. This is hereafter referred to as the second correction. By correcting the predicted water level twice, as described above, the actual water level and the predicted water level match sufficiently around the current time. In other words, the accuracy of water level prediction can be improved.

[0075] Furthermore, time restrictions may be imposed when performing the second correction. As shown in Figure 14, the predicted water levels for the range from the current time to a predetermined time ahead may be calculated using both the first and second corrections, while the predicted water levels for the range beyond the predetermined time may be calculated using only the first correction. This can further improve the accuracy of the prediction. For example, the time range for performing the second correction may be from the current time to 1 to 2 hours ahead.

[0076] Furthermore, a water level restriction may be added when performing the second correction. As shown in Figure 15, for the predicted water level after the second correction, the range below the upper limit water level may be the predicted water level of the first and second corrections, while the range above the upper limit water level may be returned to the predicted water level of the first correction without performing the second correction. This can further improve the accuracy of the prediction. The upper limit water level is, for example, in the range of 200-300% of the actual water level at the current time.

[0077] Corrections to predicted water levels should be made sequentially each time a predicted water level for a predetermined time is calculated. This can improve prediction accuracy. For example, corrections can be made sequentially each time a predicted water level for 1 to 6 hours ahead is calculated. In particular, sequential corrections can be made each time a predicted water level for 1 to 3 hours ahead is calculated, resulting in even higher accuracy in predicting water levels.

[0078] Next, the display unit 4 displays the predicted water level calculated by the water level prediction unit. The display unit 4 may also display the predicted water level numerically. Alternatively, it may display a map of the area around the prediction point and indicate the prediction point on the map with a color corresponding to the predicted water level. Furthermore, the map may also display precipitation nowcasts (5 minutes) and rainfall amounts based on the preliminary analyzed rainfall and preliminary short-term precipitation forecasts, and the update of the predicted water level may be synchronized with the update of the precipitation nowcasts (5 minutes) and preliminary analyzed rainfall and preliminary short-term precipitation forecasts. This makes the predicted water level easier for consumers to understand. This is particularly suitable when there are multiple prediction points.

[0079] If the predicted water level exceeds a predetermined threshold, an alarm may be sounded. Alternatively, the predicted water level may be notified to the smartphones or tablets of local government administrators or mayors.

[0080] In the water level prediction system of the first embodiment, the river water level is predicted as the output of a nonlinear first-order lag system whose input is rainfall in the catchment area. The time constant of the nonlinear first-order lag system is set according to the water level at the prediction point in the river. Therefore, the water level at the prediction point in the river can be predicted simply and accurately. Furthermore, the water level prediction system of the first embodiment is suitable for predicting the water level of small and medium-sized rivers, and is particularly suitable for small and medium-sized rivers in mountainous areas.

[0081] Next, we will explain the simulation results for the water level prediction system in the first embodiment.

[0082] The water level prediction system of the first embodiment was used to predict the water level of the Tsubogawa River in October 2019. The prediction point was a specific location in Kaminoho, Seki City. The time constant was determined by measuring the decrease in water level after rainfall from April 2018 to March 2021. Figure 16 is a graph showing the relationship between the water level at the prediction point and the value of the time constant.

[0083] The predicted water level was calculated using a nonlinear first-order lag system with the time constant shown in Figure 16. The input rainfall data used was measured by the Japan Meteorological Agency in the catchment area 20. Furthermore, efforts were made to match the peak of the predicted water level with the peak of the actual water level. Figure 17 is a graph showing rainfall intensity, predicted water level, and actual water level. It was confirmed that the curve for the predicted water level roughly coincides with the curve for the actual water level.

[0084] Furthermore, after calculating the predicted water level up to one hour in advance, the predicted water level was corrected to match the actual water level at the current time, and then corrected to match the slope of the actual water level at the current time with the slope of the predicted water level. This correction was repeated each time the predicted water level up to one hour in advance was calculated.

[0085] Figure 18 is a graph showing the actual water level and the predicted water level. As shown in Figure 18, the curves for the actual water level and the predicted water level are almost identical, indicating that the water level at the prediction point can be predicted with very high accuracy by successively correcting the predicted water level.

[0086] Furthermore, the time horizon was changed from one hour ahead to three hours ahead, and the same sequential correction process as in Figure 18 was repeated each time the predicted water level up to three hours ahead was calculated. Figure 19 is a graph showing the actual water level and the predicted water level. As shown in Figure 19, even with corrections every three hours, the curves of the actual water level and the predicted water level roughly coincide, indicating that the water level at the prediction point can be predicted with high accuracy.

[0087] As shown in Figures 18 and 19, by successively correcting the predicted water level each time a water level forecast for one hour or three hours in advance is calculated, it was possible to make the predicted water level and the actual water level nearly match.

[0088] (Modified form of the first embodiment) In the first embodiment, a correction is performed to make the actual water level at the current time match the predicted water level, and to make the slope of the actual water level at the current time match the slope of the predicted water level. However, other correction methods may be used.

[0089] For example, the coefficient A multiplied by the predicted water level can be determined such that the sum of the differences between the actual water level and the predicted water level is zero. That is, let W(t) be the actual water level at a certain time t, and R(t) be the predicted water level. Let S be the sum of the differences between the actual water level W(t) and the predicted water level R(t) from a certain time t0 to a certain time ti. Determine A such that S = Σ(W(t) - A × R(t)) = 0. For example, determine A such that S = 0 for the period from 3 hours before the current time to the current time. By determining A in this way and correcting the predicted water level R(t) by A × R(t), the predicted water level can be brought closer to the actual water level.

[0090] Alternatively, for example, the coefficient B multiplied by the predicted water level may be determined by the least squares method. That is, if W(t) is the actual water level at a certain time t and R(t) is the predicted water level, then RSS is the sum of squares of the difference between the actual water level W(t) and the predicted water level R(t) from a certain time t0 to a certain time ti, then RSS = Σ(W(t) - B × R(t)). 2 The coefficient B is determined such that the expression is minimized. By finding B in this way and correcting the predicted water level R(t) to B × R(t), the predicted water level can be brought closer to the actual water level.

[0091] Alternatively, the inflection point of the actual water level can be determined, and the absolute value of the correction amount for the predicted water level after the time of the inflection point can be made smaller than the correction amount before the time of the inflection point. For example, coefficients A and B are used before the time of the inflection point, and coefficients smaller than A and B are used after the time of the inflection point. As the water level approaches the peak, an inflection point appears where the first time derivative of the water level is maximum and the second time derivative of the water level is zero. After the time of this inflection point, the increase in water level slows down. Therefore, by changing the correction amount of the predicted water level before and after the time of the inflection point as described above, the accuracy of water level prediction can be improved.

[0092] It is preferable to update the time constant of the first-order lag system as needed. For example, the time period after the rain has stopped is detected from the rainfall data, and each time such a time period is detected, the time constant is set and updated based on the characteristics of the water level drop at the predicted river point during that time period. In this case, the rainfall data is obtained from satellite radio wave intensity meters 7 and rain gauges 8, and the water level at the predicted point is obtained from water level gauges 6. [Explanation of symbols]

[0093] 1: Rainfall acquisition part 2: Storage part 3: Water level prediction section 4:Display section 5: Velocity meter 6: Water level gauge 7:Satellite radio wave strength meter 8:Rain gauge 10: Japan Meteorological Agency Server 11: Cloud Server 20: Collection water area

Claims

1. A water level prediction system that predicts future water levels at prediction points in a river, A rainfall acquisition unit that acquires the future rainfall amount for the catchment area at the aforementioned prediction point, A storage unit that stores parameters for a first-order lag system, which takes the rainfall amount as input and outputs the water level of the river at the predicted point, The system includes a water level prediction unit that takes the rainfall amount acquired by the rainfall amount acquisition unit as input and calculates the predicted water level of the river at the prediction point from the first-order lag system using the parameters of the first-order lag system stored in the storage unit, A water level prediction system in which the time constant of the first-order lag system is set according to the water level of the river at the prediction point.

2. The water level prediction system according to claim 1, wherein the time constant in the delay system is set based on the water level of the river at the prediction point after the rain has stopped, and the characteristics of the decrease in the measured water level.

3. The water level prediction system according to claim 1, wherein the rainfall amount acquisition unit acquires rainfall forecast data from the Japan Meteorological Agency as the rainfall amount.

4. The water level prediction system according to claim 3, wherein the water level prediction unit converts the rainfall forecast data of the Japan Meteorological Agency into data with equal time intervals and smoothed values, and uses that data as the amount of rainfall per unit time.

5. The water level prediction system according to claim 3 or claim 4, wherein the rainfall acquisition unit acquires data from the Japan Meteorological Agency's precipitation nowcast (5 minutes) as the rainfall amount from the current time to one hour ahead, and acquires data from the Japan Meteorological Agency's preliminary analyzed rainfall and preliminary short-term precipitation forecast as the rainfall amount beyond one hour ahead.

6. Furthermore, it has a water level measuring unit that measures the actual water level at the prediction point of the river, The water level prediction unit performs a first correction on the predicted water level, The water level prediction system according to claim 1, wherein the first correction is performed so that the predicted water level value at the current time matches the actual water level value at the current time measured by the water level measuring unit.

7. The water level prediction unit performs a second correction on the predicted water level after the first correction. The water level prediction system according to claim 6, wherein the second correction is performed so that the value of the time change of the predicted water level after the first correction at the current time matches the value of the time change of the actual water level at the current time measured by the water level measuring unit.

8. The water level prediction unit is, For the water level from the current time to a predetermined time in the future, both the first correction and the second correction are performed. The water level prediction system according to claim 7, wherein the first correction is performed and the second correction is not performed for water levels beyond a predetermined time.

9. The water level prediction unit performs both the first correction and the second correction, and if there is a section in the corrected predicted water level that exceeds a predetermined upper limit, it performs the first correction for that section and returns it to a value where the second correction is not performed, according to claim 7 or claim 8.

10. Furthermore, it has a water level measuring unit that measures the actual water level at the prediction point of the river, The water level prediction unit corrects the predicted water level by multiplying it by a coefficient. The water level prediction system according to claim 1, wherein the coefficient is determined such that the sum of the differences between the actual water level measured by the water level measuring unit and the predicted water level at a certain time is zero.

11. Furthermore, it has a water level measuring unit that measures the actual water level at the prediction point of the river, The water level prediction unit corrects the predicted water level by multiplying it by a coefficient. The water level prediction system according to claim 1, wherein the coefficient is determined such that the sum of the squares of the differences between the actual water level measured by the water level measuring unit and the predicted water level at a certain time is minimized.

12. Furthermore, it has a water level measuring unit that measures the actual water level at the prediction point of the river, The water level prediction system according to claim 1, wherein the water level prediction unit corrects the predicted water level, calculates the inflection point of the actual water level, and makes the absolute value of the correction amount after the time of the inflection point smaller than the absolute value of the correction amount before the time of the inflection point.

13. Furthermore, the water catchment area is equipped with a satellite signal strength meter that receives satellite broadcast signals, The water level prediction system according to claim 1, wherein the water level prediction unit calculates the amount of rainfall from the intensity of radio waves received by the satellite radio wave intensity meter, and corrects the amount of rainfall acquired by the rainfall acquisition unit using the calculated amount of rainfall.

14. The water level prediction system according to claim 1, wherein the water level prediction unit calculates the delay time it takes for water to reach the prediction point from the rainfall point based on the length of the river channel from the rainfall point to the prediction point and the flow velocity of the water flowing in the river, and corrects the amount of rainfall for the delay time.

15. Furthermore, it has a display unit that shows the predicted water level and the rainfall amount based on the precipitation nowcast (5 minutes) and the preliminary analyzed rainfall and preliminary short-term precipitation forecast. The water level prediction system according to claim 5, wherein the update of the predicted water level is synchronized with the update of the precipitation nowcast (5 minutes) and the preliminary analyzed rainfall and preliminary short-term precipitation forecast.

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