TWI618016B - Display system and method for water level predicting of reservoirs - Google Patents

Abstract

A predictive display system for a water level of a reservoir, comprising: a predictive module, wherein at least one predictive data is calculated according to at least one input data and a type of neural network algorithm, wherein the input data includes at least real-time data of the reservoir water level, and the predicted data includes at least a reservoir water level forecasting data; a database for receiving and storing the forecasting data and/or the input data from the forecasting module; and a user operating interface comprising: an operating module for receiving user operating conditions, the user The operating conditions include at least the predicted data and/or historical data selected by the user; and a predictive data display module that retrieves the predicted and/or historical data of the selected browsing according to the user operating conditions. And display the predicted and/or historical data of the selected browsing for the user to browse.

Description

Reservoir water level prediction display system and method

The invention relates to a display system for predicting water level of a reservoir, in particular to a display system and method for predicting water level of a reservoir by using multiple types of real-time hydrological data.

Hydropower plants must consider both flood control and water supply when considering how to generate electricity efficiently. Therefore, the power generation operation of hydropower plants is closely related to the water level of the reservoir. In the power generation operation, the current reservoir water level and the future reservoir water level must be taken into consideration together, otherwise it will not be able to achieve high power generation efficiency, flood control and water supply at the same time.

However, at present, when judging the predicted value of the water level of the reservoir, it is only carried out by the operating personnel of the hydropower plant based on the current water level of the reservoir and some relevant information, based on personal experience judgment. Due to the large amount of data and the high diversity, the data collection and integration analysis is extremely difficult. If only manpower is used, such complicated operations cannot be effectively carried out, and appropriate judgments or predictions can be made accordingly. There is a lack of a system that can synthesize all types of information and provide operational judgment by hydropower plant operators in an easy-to-understand manner.

Therefore, there is a need for a collection, integration and analysis of reservoir water level related information, and the integrated and analyzed data can be presented to the user in an easy to understand and easy to operate manner, so that users can easily understand the relevant information of the reservoir water level. Forecast display system and method.

In order to solve the above problems, an object of the present invention is to provide a predictive display system and method for a reservoir water level.

Another object of the present invention is to provide a system and method for predicting and displaying a reservoir water level through a neural network algorithm for reservoir water level prediction.

Still another object of the present invention is to provide a predictive display system and method for a reservoir water level that can graphically display predicted and historical data.

Another object of the present invention is to provide a predictive display system and method for graphically displaying a reservoir water level by simulating future power generation and flood discharge data.

Another object of the present invention is to provide a predictive display system and method for a reservoir water level that can graphically display predicted data and historical data such as hydrology, rainfall, and power generation.

To achieve the above objective, the present invention provides a predictive display system for a water level of a reservoir, comprising: a predictive module, a database, and a user interface. The prediction module calculates at least one prediction data according to at least one input data and through a neural network algorithm. The input data includes at least a reservoir water level real-time data, and the prediction data includes at least a reservoir water level prediction data. The database receives and stores the forecast data and/or the input data from the predictive module. The user operation interface includes: an operation module and a prediction data display module. The operation module receives a user operation condition, and the user operation condition includes at least a predicted data and/or historical data selected by the user. And the forecasting data display module is configured to retrieve the predicted data and/or historical data of the selected browsing according to the user operating conditions, and display the predicted and/or historical data of the selected browsing for use. Browse.

In a preferred embodiment of the present invention, the predicted data and/or historical data showing the selected browsing is displayed graphically.

In a preferred embodiment of the present invention, the predictive data and/or historical data showing the selected browsing is displayed in a graphical manner having a plurality of vertical axes.

In a preferred embodiment of the present invention, the forecasting data and/or historical data showing the selected browsing includes: reservoir inflow forecast data, reservoir inflow historical data, reservoir water level prediction data, reservoir water level historical data, Rainfall forecast data, rainfall historical data, power generation simulation data, power generation historical data, flood discharge simulation data, flood discharge historical data, or a combination thereof.

In a preferred embodiment of the invention, the user operating condition includes a portion of the input data.

In a preferred embodiment of the present invention, the user operating conditions include flood control simulation data or power generation simulation data.

In a preferred embodiment of the invention, the predictive data includes reservoir inflow prediction data.

According to the object of the present invention, a method for predicting and displaying a water level of a reservoir is provided, comprising: calculating at least one forecast data by using at least one input data through a type of neural network algorithm, the input data including at least real-time data of the reservoir water level, and the forecast data At least the reservoir water level prediction data is received; the user operating conditions are received, wherein the user operating conditions include at least the predicted data and/or historical data selected by the user; and the predicted browsing data of the selected browsing is obtained according to the user operating conditions and / or historical data; display forecast data and / or history of the selected browsing data.

In a preferred embodiment of the present invention, the predicted data and/or historical data showing the selected browsing is displayed graphically.

In a preferred embodiment of the present invention, the predictive data and/or historical data showing the selected browsing is displayed in a graphical manner having a plurality of vertical axes.

In a preferred embodiment of the present invention, the forecasting data and/or historical data showing the selected browsing includes: reservoir inflow forecast data, reservoir inflow historical data, reservoir water level prediction data, reservoir water level historical data, Rainfall forecast data, rainfall historical data, power generation simulation data, power generation historical data, flood discharge simulation data, flood discharge historical data, or a combination thereof.

In a preferred embodiment of the invention, the user operating condition includes a portion of the input data.

In a preferred embodiment of the present invention, the user operating conditions include flood control simulation data or power generation simulation data.

In a preferred embodiment of the invention, the predictive data includes reservoir inflow prediction data.

The foregoing aspects and other aspects of the invention will be apparent from the description of the appended claims appended claims

100‧‧‧ class neural network system

200‧‧‧ Training materials

220‧‧‧Data Server

240‧‧‧ Reservoir Water Level Prediction System

250‧‧‧ Prediction Module

252‧‧‧ Neural Network Learning Module

254‧‧‧ Forecast Data Calculation Module

256‧‧‧Input data storage module

260‧‧‧User interface

262‧‧‧Operating module

264‧‧‧ Forecast data display module

270‧‧‧Database

280‧‧‧Users

300‧‧‧ forecasting mechanism flow chart

310-350‧‧‧ operation

400‧‧‧ class neural network model

402‧‧‧Power generation data

404‧‧‧ Reservoir water level data

406‧‧‧ Flood discharge data

408a‧‧‧First-class neural network

408b‧‧‧Second-level neural network

408c‧‧‧Layer 3 neural network

412‧‧‧ Reservoir Inflow Information

424‧‧‧Catchment observation data of catchment area

426‧‧‧Catchment meteorological forecast data

432‧‧‧ Reservoir Inflow Forecast Data

442‧‧‧ Reservoir water level forecast data

500‧‧‧User operation display flow chart

510-550‧‧‧ operation

610‧‧‧Graphic display area

612‧‧‧ vertical axis

614‧‧‧ Reservoir Inflow Forecasting Line Chart

616‧‧‧ Reservoir water level prediction line chart

618‧‧‧ vertical axis

619‧‧‧ horizontal axis

620‧‧‧Graphic display area

622‧‧‧ vertical axis

624‧‧‧ horizontal axis

626‧‧‧Watershed rainfall forecast bar chart

720‧‧‧Graphic display area

722‧‧‧ vertical axis

724‧‧‧ horizontal axis

728‧‧‧ Historical rainfall bar chart

730‧‧‧ Forecasting rainfall direct bar chart

The first picture is a schematic diagram of a general neural network system.

The second figure is a frame of a reservoir-based water level prediction system based on a neural network. Composition.

The third figure is a flow chart of the method for predicting the water level of the reservoir based on the neural network.

The fourth figure is a neural network model diagram of a specific embodiment of a reservoir-based water level prediction system based on a neural network.

The fifth figure is a flow chart of the user operation display of the reservoir water level prediction method based on the neural network.

The sixth figure is a schematic diagram of a specific embodiment of the prediction result display of the reservoir-based water level prediction system based on the neural network.

The seventh figure is a schematic diagram of another specific embodiment of the prediction result display of the reservoir-based water level prediction system based on the neural network.

The invention is based on a neural network-based reservoir water level prediction system and method thereof, which utilizes a neural network having the characteristics of learning the relationship between input data and output data under the condition that no mathematical transformation is required. The neural network is trained with a large amount of training data to mold the weights of each input node and each hidden layer neuron, the weight of each hidden layer neuron and the output node, and the partial weight of each node. The hydrological data obtained is used as input data for each input node to predict the reservoir water level. The following will be further explained in conjunction with the drawings.

The first picture is a schematic diagram of a general neural network system. The invention is based on a neural network-based reservoir water level prediction system, which is trained by using a neural network system 100 as shown. Practicing to mold each weight and each bias value. In the figure, the neural network system 100 has an input node S1 and an input node S2 in the first layer, and a hidden layer neuron S3 in the second layer. The hidden layer neuron S4, the hidden layer neuron S5, has an output node S6 at the third layer.

It should be understood that the neural network-like system 100 is merely exemplary herein, and that the present invention is not limited to the use of a single layer of hidden layer neurons, but one or more layers of hidden layer neurons may be used as desired. The number of input nodes, the number of hidden layer neurons, and the number of output nodes of the neural network system 100 are also not limited to the present invention. The number of input nodes, the number of hidden layer neurons, and the number of output nodes of the present invention are not limited. Adjust to any number depending on the situation.

Referring to the first figure, each input node of the neural network system 100 has a corresponding weight with each hidden layer neuron. For example, the input node S1 and the hidden layer neuron S5 have a weight W15, and the input node S2 is hidden. The layer neuron S5 has a weight W25. At the same time, each hidden layer neuron of the neural network system 100 also has a corresponding weight. For example, the hidden layer neuron S3 and the output node S6 have a weight W36, and the hidden layer neuron S5 and the output node S6 have a weight. Weight W56. Each input node, hidden layer neuron, and output node each have a respective bias value. For example, the input node S2 has an offset weight θ2, the hidden layer neuron S5 has an offset weight θ5, and the output node S6 has an offset weight θ6. The calculation of the transmission value transmitted by one node to the next node is as follows. Assuming that a total of n nodes transmit their respective transmission values to the node Y, the formula of the transmission value y transmitted by the node Y to the next node is: Where Wi is the weight corresponding to the i-th node and the node Y of the n nodes transmitting the transmission value to the node Y, Xi is the transmission value of the i-th node transmitted to the node Y, and θ is the deviation of the node Y Weight.

Taking the node S5 as an example, the weight corresponding to the input node S1 and the hidden layer neuron S5 is W15, the weight corresponding to the input node S2 and the hidden layer neuron S5 is W25, and the bias value of the hidden layer neuron S5 is θ5. . Let the transmission value of the input node S1 transmitted to the hidden layer neuron S5 be X1, and the transmission value of the input node S2 transmitted to the hidden layer neuron S5 is X2, then the transmission value of the hidden layer neuron S5 to the output node S6 is: (W15.X1+W25.X2)-θ5

In the process of training the neural network system 100, a large amount of known input data and output data are first used as training input data and output data, and the neural network system 100 is trained to mold each weight. And each bias value. Traditional gradient descent algorithms have traditionally been employed to correct individual weights. After molding each weight and each bias value, the neural network system 100 can be utilized to predict the output data with instant input data.

The second figure is an architectural diagram of a neural network-based reservoir water level prediction system according to the present invention. As shown, the reservoir water level prediction system 240 includes a prediction module 250, a user operation interface 260, and a database 270. The prediction module 250 includes an input data storage module 256 for capturing and storing at least one input data by a plurality of servers, and the input data includes at least reservoir water level real-time data; a neural network learning module 252, Forming at least one weight and at least one bias value according to the input data stored in the input data storage module 256; and a predictive data computing module 254, according to the neural network learning module 252 Simulating the weight and the bias value, calculating the input data to obtain at least one prediction data, and the prediction data includes at least reservoir water level prediction data; the user operation interface 260 includes an operation module 262, for receiving user operating conditions, the user operating conditions include at least the predicted data and/or historical data selected by the user; and a predictive data display module 264 for using the user operating conditions to The database 270 retrieves the predicted and/or historical data of the selected browsing and displays the predicted and/or historical data of the selected browsing for the user to browse.

The prediction module 250 trains the neural network learning module 252 by a large amount of training data 200, thereby molding each weight and each bias value. Then, the input data storage module 256 periodically collects and stores various types of real-time data required by the data server 220, and then stores the data stored in the input data storage module 256 by the prediction data computing module 254. As input data, each weight and each bias value molded by the neural network learning module 252 are used to further calculate the predicted data, and the predicted data is stored through the database 270. The input data used by the prediction data calculation module 254 in performing the prediction calculation may further include data input by the user through the operation module 262 in addition to the data stored in the input data storage module. The user can determine the predicted data and/or historical data range to be displayed through the operation module 262. Then, the user operation interface 260 will retrieve the predicted data from the database 270 according to the predicted data range selected by the user. / or historical data, and through the forecast data display module 264 to present the predicted data and / or historical data to the user.

Please refer to the third figure and refer to the fourth figure. The third figure is the flow chart of the reservoir water level prediction method based on the neural network. The fourth picture is the water level prediction of the reservoir based on the neural network. A neural network model diagram of a particular embodiment of the system. The invention is based on a neural network-based reservoir water level prediction method, and includes the following steps: First, in step 310, a user first constructs a neural network model, which includes determining input data of each input node as He, wherein the neural network model can be a single-layer neural network model or a multi-layer neural network model.

In this embodiment, at step 310, the constructed neural network model is a neural network model 400 such as shown in the fourth figure, and the neural network model 400 is used to perform, for example, a 48-hour reservoir water level forecast. As shown, the neural network model 400 is a three-layer neural network model, in which the number of hidden layer neurons of the first layer neural network 408a can be 5 to 20, and the second layer neural network The number of hidden layer neurons of the road 408b may be 5 to 20, and the number of hidden layer neurons of the third layer type neural network 408c may be 5 to 20.

It should be understood that the number of hidden layer neurons of each layer of neural network is merely exemplified herein, and the present invention is not limited to the use of 5-20 hidden layer neurons, and any number of hidden layer neurons may be set as needed.

In addition, in the embodiment, at step 310, the power generation amount data 402, the reservoir water level data 404, and the flood discharge amount data 406 are used as input data of the first layer type neural network 408a, and the reservoir inflow data 412 is used as the first Output data of a layer of neural network 408a. The input data of the second layer neural network 408b is the reservoir inflow data 412, the catchment rainfall station observation data 424, and the catchment weather forecast data 426, and the output data of the second layer neural network 408b is the reservoir inlet. Traffic prediction data 432. The input data of the third layer neural network 408c is the reservoir inflow prediction data 432, the power generation data 402 and the flood discharge data 406, and the output data of the third layer neural network 408c is the reservoir water level prediction data 442.

After step 310 is completed, step 320 is performed. At step 320, the user further determines which historical data is used as training data for training each layer of neural network, and thereby Each weight and partial weight of each layer of neural network model is molded.

In the embodiment, the historical data is used as the training input data of the first layer type neural network 408a from the power generation data 402 of the A reservoir from 2012 to 2013, the reservoir water level data 404, and the flood discharge amount data 406, and From 2012 to 2013, the reservoir inflow data 412 of the A reservoir was used as training output data for the first layer of neural network 408a. From 2012 to 2013, the reservoir inflow data 412 of the A reservoir, the catchment area observation data 424 and the catchment meteorological forecast data 426 are used as training input materials for the second layer neural network 408b, and 2012. From 2017 to 2013, the reservoir inflow prediction data 432 of the A reservoir is used as training output data for the second-layer neural network 408b. From 2012 to 2013, the reservoir inflow forecast data 432, power generation data 402 and flood discharge data 406 of the A reservoir are used as training input materials for the third layer neural network 408c, and then from 2012 to 2013. The reservoir water level prediction data 442 of the reservoir is used as training output data for the third layer neural network 408c. Wherein, the data time point of the reservoir inflow prediction data 432 for the training of the second layer neural network 408b is compared with the observation data 424 of the catchment rainfall station for training of the second layer neural network 408b, and training The data time of the catchment weather forecast data 426 is delayed by 1 to 48 hours to simulate the relationship between the input data and the output data of the second layer neural network 408b, which is the relationship between the input data and the predicted data. The data time point of the reservoir water level prediction data 442 for the training of the third layer neural network 408c is higher than that of the training data for the training of the third layer neural network 408c, and the data time of the flood discharge data for training. The relationship between the input data and the output data of the third-layer neural network 408c is simulated by 1 to 48 hours, which is the relationship between the input data and the predicted data.

In deciding what historical data to use as training materials for training various layers of neural networks After 200, step 330 is performed. At step 330, the neural network of each layer of the neural network learning module 252 is trained with the determined historical data to mold each weight and each of the neural networks of each layer. Partial weight.

In this embodiment, at step 330, the neural network model 400 is trained by using the training input data and the training output data of the above 2012 to 2013 to mold the first layer neural network. The respective weights and bias values of the road 408a, the second layer neural network 408b, and the third layer neural network 408c.

Then, step 340 is performed. At step 340, the input data storage module 256 periodically retrieves the required real-time data from the data server 220 and stores the data. The captured data can also be used as a training history in the future. Information and/or as historical information for the user to view. The captured real-time data can be simultaneously stored in the database 270 for use by the user operation interface 260.

In this embodiment, at step 340, the water level data, the power generation data, and the rainfall station data of the reservoir catchment area are retrieved from the input data storage module 256 to the A server every hour at an interval of one hour. And store, go to the B server to collect the meteorological forecast data of the catchment area and store it. And when the predictive data storage module 254 performs the predictive calculation, the data is provided for the reservoir to predict the water level of the reservoir for 1 to 48 hours.

It should be understood that the above embodiments are merely examples, and the present invention is not limited to performing data capture every hour, but for several minutes to several days as the execution interval of data retrieval. At the same time, the present invention is not only used to predict reservoir water levels after 1 to 48 hours, but reservoir predictions of at least hours to days, weeks, months, and years may be performed as needed.

Next, step 350 is performed. At step 350, the prediction data calculation module 254 performs prediction operations by using the weights and the partial weight values molded by the neural network learning module 252, and the system is stored in the input data. The data of the storage module 256 is used as input data, and the predicted data is calculated for the input data, and then the calculated predicted data is stored in the database 270.

In this embodiment, the weights and partial weights of the first layer neural network 408a, the second layer neural network 408b, and the third layer neural network 408c are molded by the above-mentioned modeling. The neural network model 400 performs a water level prediction of the A reservoir and stores the predicted data in the database 270. The input data for prediction is obtained from the data collected and stored by the input data storage module 256 on the A server and the B server, that is, the method for predicting the water level of the reservoir based on the neural network is completed.

The fifth figure is a user operation display flow chart of the neural network-based reservoir water level prediction system of the present invention. The user operation display flow chart 500 starts at step 510, and the operation module 262 confirms whether a user is a legitimate user. . In one embodiment, the user enters an account password for confirmation by the operating module 262.

After confirming that the user is legally logged in, the operation module 262 is provided by the operation module 262 to use the operation interface, and at step 530, the operation module 262 receives the operation condition input by the user. The operating conditions may include, but are not limited to, the range of data selected by the user and/or some of the input data (for example, flooding simulation data and/or power generation simulation data) for prediction. The information selected by the user may be predicted data and/or historical data.

Then, in step 540, based on the operating conditions entered by the user, the predicted data display module 264 to the database 270 and/or the input data storage module 256 retrieves the predicted data and/or the user selected by the user. Selected historical data for browsing. At step 550, the predicted data display module 264 displays the data retrieved from the database 270 and/or the input data storage module 256 for viewing by the user. The displaying of the data from the database 270 and/or the input data storage module 256 may be performed by directly displaying the values of the data and/or graphically displaying the data. The graphic display may be displayed in the form of a line graph and/or a bar graph, but is not limited thereto.

The sixth figure is a schematic diagram of a specific embodiment of the prediction result display of the reservoir-based water level prediction system based on the neural network. As shown in the figure, after the user selects the predicted data range to be browsed, the prediction data display module 264 That is, according to the predicted data range selected by the user, the corresponding forecast data is retrieved from the database 270. And in the graphic display area 610, the user-introduced reservoir inflow forecasting line graph 614 and the reservoir water level prediction line graph 616 are displayed in the form of a line graph, wherein the corresponding vertical axis 612 of the reservoir inflow forecasting line graph 614 indicates the reservoir prediction. Inflow value (CMS), horizontal axis 619 indicates the predicted time point. The corresponding vertical axis 618 of the reservoir water level prediction line chart 616 indicates the predicted water level height (m) of the reservoir, and the horizontal axis 619 indicates the predicted time point. The graphical display area 620 displays the catchment rainfall prediction bar graph 626 in the form of a bar graph, with the corresponding vertical axis 622 indicating the predicted rainfall amount (mm) and the horizontal axis 624 indicating the predicted time point.

It should be understood that the sixth figure is only a schematic diagram, and the final display graphic of the present invention is not limited thereto. For example, the graphic display area 610 can also display the reservoir inflow prediction data, the reservoir inflow historical data, and the reservoir water level prediction data. And reservoir water level history data. Again, for example, The shape display area 620 also has two sets of vertical axes depending on the situation, and can display the predicted data and/or historical data of the browsing selected by the user in the form of a line graph.

The seventh figure is a schematic diagram of another specific embodiment of the prediction result display of the reservoir-based water level prediction system based on the neural network. As shown in the figure, it is displayed in the graphic display area 720, and each of them is displayed in the form of a bar graph. The rainfall is standing at the historical rainfall direct bar graph 728 and the predicted rainfall direct bar graph 730 at each time point. The corresponding vertical axis 722 of the historical rainfall bar graph 728 and the predicted rainfall bar graph 730 indicates the rainfall amount value (mm), and the corresponding horizontal axis 724 indicates the time point. At the same time, as shown in the figure, the historical rainfall bar graph 728 and the predicted rainfall bar graph 730 at the same time point are closely adjacent to each other, so that the user can recognize the historical rainfall amount and the predicted rainfall amount corresponding to each time point.

As shown in the seventh figure, when the range of data selected by the user to be browsed is historical data, that is, at the graphic display area 720, the selected time is displayed by the historical rainfall bar graph 728 and the predicted rainfall bar graph 730. The historical data and forecast data of the point can be used by the user to compare the actual rainfall value and the predicted rainfall value of each catchment at each time point to analyze the accuracy of the predicted value.

It is to be understood that the seventh drawing is only a schematic view, and the final display of the present invention is not limited thereto.

Claims (10)

A predictive display system for a water level of a reservoir, comprising: a predictive module, wherein at least one predictive data is calculated according to at least one input data and a type of neural network algorithm, the input data comprising at least a reservoir water level real-time data and a catchment area weather forecast data And the forecasting data includes at least the reservoir water level forecasting data; a database for receiving and storing the forecasting data and/or the input data from the forecasting module; and a user operating interface comprising: an operating module for receiving and using Operating conditions, the user operating conditions include at least the predicted data and/or historical data selected by the user; and a predictive data display module that retrieves the selected browsing according to the user operating conditions Predicting data and/or historical data, and displaying the predicted and/or historical data of the selected browsing for viewing by the user; wherein the user operating condition includes a portion of the input data; wherein the user operating condition includes Enter the amount of flood discharge simulation data or power generation simulation data in one part of the data. The predictive display system for a reservoir water level according to claim 1, wherein the forecasting data and/or historical data showing the selected browsing are displayed graphically. The predictive display system for a reservoir water level as described in claim 1, wherein the predictive data and/or historical data showing the selected browsing is displayed in a graphical manner having a plurality of vertical axes. A predictive display system for a reservoir water level as described in claim 1 wherein said display The forecasted data and/or historical data of the selected browsing include: reservoir inflow forecast data, reservoir inflow historical data, reservoir water level forecast data, reservoir water level historical data, rainfall forecast data, rainfall historical data, and power generation simulation data. Historical data of power generation, simulated data of flood discharge, historical data of flood discharge, or a combination thereof. For example, the forecasting display system for the reservoir water level described in claim 1 of the patent scope, wherein the forecast data includes reservoir inflow forecast data. A method for predicting and displaying a water level of a reservoir, comprising: calculating at least one forecast data according to at least one input data through a type of neural network algorithm, the input data comprising at least a reservoir water level real-time data and a catchment area weather forecast data, and the forecast data is at least Included in the reservoir water level prediction data; receiving the user operating conditions, wherein the user operating conditions include at least the predicted data and/or historical data selected by the user; and the predicted browsing data of the selected browsing is obtained according to the user operating conditions and/or Or historical data; displaying predicted data and/or historical data of the selected browsing; wherein the user operating condition includes a portion of the input data; wherein the user operating condition includes a flood discharge simulation data or a power generation as part of the input data Volume simulation data. The method for predicting and displaying a water level of a reservoir according to claim 6, wherein the forecasting data and/or historical data showing the selected browsing are displayed graphically. The method for predicting and displaying a water level of a reservoir according to claim 6, wherein the forecasting data and/or historical data showing the selected browsing is displayed in a graphical manner having a plurality of vertical axes. The method for predicting the water level of a reservoir as described in claim 6 wherein the forecasting data and/or historical data showing the selected browsing includes: reservoir inflow prediction data, reservoir inflow historical data, reservoir Water level forecast data, reservoir water level historical data, rainfall forecast data, rainfall historical data, power generation simulation data, power generation historical data, flood discharge simulation data, flood discharge historical data or a combination thereof. The method for predicting the water level of a reservoir as described in claim 6 of the patent application scope, wherein the forecast data includes prediction data of the reservoir inflow.