CN111461452A - Ensemble runoff prediction system and method based on group intelligent algorithm - Google Patents
Ensemble runoff prediction system and method based on group intelligent algorithm Download PDFInfo
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- CN111461452A CN111461452A CN202010285874.1A CN202010285874A CN111461452A CN 111461452 A CN111461452 A CN 111461452A CN 202010285874 A CN202010285874 A CN 202010285874A CN 111461452 A CN111461452 A CN 111461452A
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Abstract
The invention discloses an aggregate runoff prediction system based on a group intelligent algorithm, belonging to the technical field of aggregate runoff prediction, comprising: a central processing module; the storage module is electrically and bidirectionally connected with the central processing module and used for realizing the storage and extraction of data; the power supply module is electrically connected with the central processing module in an output mode and used for providing electric energy for the central processing module; the wireless transmission module is electrically connected with the central processing module in an output mode and used for realizing transmission of detection data; the geological exploration module is electrically connected with the wireless transmission module in output and is used for mining and analyzing geological information; the rainfall sensor is electrically connected with the wireless transmission module in an output mode, and the rainfall prediction method can perform a good prediction effect according to the rainfall amount, so that the comprehensive effect of the precision and the predictability of the integrated runoff prediction is further improved.
Description
Technical Field
The invention relates to the technical field of aggregate runoff prediction, in particular to an aggregate runoff prediction system and method based on a group intelligent algorithm.
Background
Runoff refers to water flow which is formed by melting rain and ice and snow or flows along the ground surface or underground under the action of gravity when the land is watered. The runoff has different types, and rainfall runoff, melt water runoff and watering runoff can be selected according to the water flow source; the surface runoff and the underground runoff can be separated according to the flowing mode, and the surface runoff is further separated into slope surface flow and river channel flow. In addition, there are solid runoff formed by solid matter (silt) contained in the water flow, ion runoff (see chemical runoff) formed by chemical dissolved matter contained in the water flow, and the like.
Watershed runoff is the first link in runoff formation. In contrast to conventional concepts, the production flow is not only a static concept of water production, but a dynamic concept with spatiotemporal variations. Including the spatial development of the runoff yield area at different moments and the change of the runoff yield intensity along with the time course of the rainfall process. In addition, the runoff yield mainly occurs on the watershed slope, the proportion of the slope area is different for watersheds of different sizes, and various factors influencing the runoff yield, including vegetation, soil, gradient, land utilization condition, the slope area, position and the like, are different in the watersheds of different sizes.
In the prior art, the aggregate runoff prediction is inconvenient to perform according to the implementation situation and the precipitation amount, and meanwhile, the judgment mode of judging an aggregate point of the aggregate runoff is not accurate enough, so that research and development of an aggregate runoff prediction system and method based on a group intelligent algorithm are urgently needed.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention is made in view of the above and/or the problems existing in the existing group intelligence algorithm based aggregate runoff prediction system and method.
Therefore, the invention aims to provide a system and a method for predicting aggregate runoff based on a group intelligent algorithm, which can perform a good prediction effect according to rainfall and further improve the accuracy and predictability of aggregate runoff prediction.
To solve the above technical problem, according to an aspect of the present invention, the present invention provides the following technical solutions:
a group intelligence algorithm based aggregate runoff prediction system, comprising:
a central processing module;
the storage module is electrically and bidirectionally connected with the central processing module and used for realizing the storage and extraction of data;
the power supply module is electrically connected with the central processing module in an output mode and used for providing electric energy for the central processing module;
the wireless transmission module is electrically connected with the central processing module in an output mode and used for realizing transmission of detection data;
the geological exploration module is electrically connected with the wireless transmission module in output and is used for mining and analyzing geological information;
the rainfall sensor is electrically connected with the wireless transmission module in an output mode and used for sensing the rainfall;
the positioning module is electrically connected with the wireless transmission module in an output mode and used for positioning a test site;
the communication module is electrically and bidirectionally connected with the central processing module and is used for realizing the transmission of remote data;
the display module is electrically connected with the central processing module in an output mode and used for displaying results of the detection and data processing;
and the remote terminal is wirelessly and bidirectionally connected with the communication module and is used for realizing remote management.
As an optimal solution of the collective runoff prediction system based on the group intelligent algorithm, the present invention comprises: the geological exploration module is an electric geological exploration device based on a GIS.
As an optimal solution of the collective runoff prediction system based on the group intelligent algorithm, the present invention comprises: the display module is an IPS liquid crystal display.
As an optimal solution of the collective runoff prediction system based on the group intelligent algorithm, the present invention comprises: the remote terminal comprises a mobile phone, a tablet and a computer.
As an optimal solution of the collective runoff prediction system based on the group intelligent algorithm, the present invention comprises: the wireless transmission module is ZigBee or Bluetooth.
A set runoff prediction method based on a group intelligent algorithm comprises the following steps:
the method comprises the following steps: initial state of basin: acquiring hydrological information of a drainage basin according to a website, wherein the hydrological information is used for performing water level observation, flow test, sediment test, water quality, water temperature, ice condition, precipitation, evaporation capacity, soil water content and underground water level observation on the station;
step two: acquiring rainfall factor data: acquiring rainfall of each station by using a rainfall sensor;
step three: history simulation: historical hydrological elements are used as a set of hydrological elements in a prediction period, and initial conditions of prediction dates corresponding to the hydrological elements are selected for historical simulation.
Step four: and (3) condition simulation: and inputting historical hydrological element time sequences for condition simulation by adopting initial conditions of the drainage basin in the current prediction period, and outputting a set of runoff processes, namely calculating the prediction of the set runoff.
Step five: evaluation results were as follows: and after the result of the condition simulation is obtained, carrying out statistical analysis, inspection and evaluation on the result so as to give probabilistic prediction and utilize group intelligence to simulate the process of the aggregate runoff.
As an optimal scheme of the collective runoff prediction method based on the group intelligent algorithm, the method comprises the following steps: the historical simulation aims to adjust parameters of the prediction model so that runoff simulated according to historical hydrological data can be matched with actual runoff as much as possible, and the initial state of the soil in the drainage basin is given.
Compared with the prior art: the aggregate runoff prediction system and method based on the group intelligent algorithm can perform a good prediction effect according to the precipitation amount, and further improve the accuracy and predictability of aggregate runoff prediction.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and detailed embodiments, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise. Wherein:
FIG. 1 is a schematic structural diagram of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described herein, and it will be apparent to those of ordinary skill in the art that the present invention may be practiced without departing from the spirit and scope of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The invention provides a set runoff predicting system based on a group intelligent algorithm, which comprises:
a central processing module 100;
the memory module 200 is electrically connected to the central processing module 100 in a bidirectional manner, and is used for storing and extracting data;
the power module 300, the power module 300 is electrically connected to the central processing module 100 for providing power to the central processing module 100;
the wireless transmission module 400 is electrically connected to the central processing module 100 in an output manner and used for transmitting detection data, and the wireless transmission module 400 is ZigBee or bluetooth;
the geological exploration module 500 is electrically connected with the wireless transmission module 400 in an output mode, and is used for mining and analyzing geological information, and the geological exploration module 500 is a GIS-based electric geological exploration device;
the rainfall sensor 600 is electrically connected with the wireless transmission module 400 in an output mode, and is used for sensing the rainfall;
a positioning module 700, wherein the positioning module 700 is electrically connected to the wireless transmission module 400 for positioning a test site;
the communication module 800 is electrically connected to the central processing module 100 in a bidirectional manner, and is used for realizing transmission of remote data;
the display module 900, the display module 900 is electrically connected to the central processing module 100 for displaying a result of detecting and processing data, and the display module 900 is an IPS liquid crystal display;
the remote terminal 1000 is wirelessly and bidirectionally connected with the communication module 800 and is used for realizing remote management, and the remote terminal 1000 comprises a mobile phone, a tablet and a computer.
A set runoff prediction method based on a group intelligent algorithm comprises the following steps:
the method comprises the following steps: initial state of basin: acquiring hydrological information of a drainage basin according to a website, wherein the hydrological information is used for performing water level observation, flow test, sediment test, water quality, water temperature, ice condition, precipitation, evaporation capacity, soil water content and underground water level observation on the station;
step two: acquiring rainfall factor data: acquiring rainfall of each station by using a rainfall sensor 600;
step three: history simulation: historical hydrological elements are used as a set of hydrological elements in a prediction period, initial conditions of prediction dates corresponding to the hydrological elements are selected, historical simulation is conducted, and the historical simulation aims at adjusting parameters of a prediction model so that runoff simulated according to historical hydrological data can be matched with actual runoff as much as possible, and the initial state of the soil in the watershed is given.
Step four: and (3) condition simulation: and inputting historical hydrological element time sequences for condition simulation by adopting initial conditions of the drainage basin in the current prediction period, and outputting a set of runoff processes, namely calculating the prediction of the set runoff.
Step five: evaluation results were as follows: and after the result of the condition simulation is obtained, carrying out statistical analysis, inspection and evaluation on the result so as to give probabilistic prediction and utilize group intelligence to simulate the process of the aggregate runoff.
The aggregate runoff prediction system and method based on the group intelligent algorithm can perform a good prediction effect according to the precipitation amount, and further improve the accuracy and predictability of aggregate runoff prediction.
While the invention has been described above with reference to an embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the various features of the disclosed embodiments of the invention may be used in any combination, provided that no structural conflict exists, and the combinations are not exhaustively described in this specification merely for the sake of brevity and resource conservation. Therefore, it is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (7)
1. An aggregate runoff prediction system based on a group intelligence algorithm, comprising:
a central processing module (100);
the storage module (200), the storage module (200) is electrically connected with the central processing module (100) in a bidirectional way, and is used for realizing the storage and extraction of data;
the power supply module (300), the electrical output of the power supply module (300) is connected with the central processing module (100) and is used for providing electric energy for the central processing module (100);
the wireless transmission module (400), the wireless transmission module (400) is electrically connected with the central processing module (100) in an output mode, and is used for realizing the transmission of the detection data;
the geological exploration module (500), the geological exploration module (500) is electrically output and connected with the wireless transmission module (400) and is used for mining and analyzing geological information;
the rainfall sensor (600), the said rainfall sensor (600) electrical output connects the said wireless transmission module (400), is used for sensing the amount of rainfall;
the positioning module (700), the positioning module (700) is electrically connected to the wireless transmission module (400) for positioning the test site;
the communication module (800), the communication module (800) is electrically connected with the central processing module (100) in a bidirectional way, and is used for realizing the transmission of remote data;
the display module (900), the said display module (900) electrical output connects the said central processing module (100), the result used for displaying and detecting and processing the data is displayed;
the remote terminal (1000) is wirelessly and bidirectionally connected with the communication module (800) and is used for realizing remote management.
2. The system according to claim 1, wherein the system comprises: the geological exploration module (500) is a GIS-based electrical geological exploration device.
3. The system according to claim 1, wherein the system comprises: the display module (900) is an IPS liquid crystal display.
4. The system according to claim 1, wherein the system comprises: the remote terminal (1000) comprises a mobile phone, a tablet and a computer.
5. The system according to claim 1, wherein the system comprises: the wireless transmission module (400) is ZigBee or Bluetooth.
6. A set runoff prediction method based on a group intelligent algorithm is characterized in that: the aggregate runoff prediction method based on the group intelligent algorithm comprises the following steps:
the method comprises the following steps: initial state of basin: acquiring hydrological information of a drainage basin according to a website, wherein the hydrological information is used for performing water level observation, flow test, sediment test, water quality, water temperature, ice condition, precipitation, evaporation capacity, soil water content and underground water level observation on the station;
step two: acquiring rainfall factor data: acquiring rainfall of each station by using a rainfall sensor (600);
step three: history simulation: historical hydrological elements are used as a set of hydrological elements in a prediction period, and initial conditions of prediction dates corresponding to the hydrological elements are selected for historical simulation.
Step four: and (3) condition simulation: and inputting historical hydrological element time sequences for condition simulation by adopting initial conditions of the drainage basin in the current prediction period, and outputting a set of runoff processes, namely calculating the prediction of the set runoff.
Step five: evaluation results were as follows: and after the result of the condition simulation is obtained, carrying out statistical analysis, inspection and evaluation on the result so as to give probabilistic prediction and utilize group intelligence to simulate the process of the aggregate runoff.
7. The aggregate runoff predicting method based on the group intelligent algorithm as recited in claim 6, wherein: the historical simulation aims to adjust parameters of the prediction model so that runoff simulated according to historical hydrological data can be matched with actual runoff as much as possible, and the initial state of the soil in the drainage basin is given.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109165795A (en) * | 2018-10-11 | 2019-01-08 | 南昌工程学院 | A kind of set Runoff Forecast System and method for based on swarm intelligence algorithm |
| CN110598290A (en) * | 2019-08-30 | 2019-12-20 | 华中科技大学 | Method and system for predicting future hydropower generation capacity of basin considering climate change |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109165795A (en) * | 2018-10-11 | 2019-01-08 | 南昌工程学院 | A kind of set Runoff Forecast System and method for based on swarm intelligence algorithm |
| CN110598290A (en) * | 2019-08-30 | 2019-12-20 | 华中科技大学 | Method and system for predicting future hydropower generation capacity of basin considering climate change |
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