CN111833202B - Farmland evapotranspiration short-term prediction method considering crop coefficient dynamic change and rainfall - Google Patents
Farmland evapotranspiration short-term prediction method considering crop coefficient dynamic change and rainfall Download PDFInfo
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Abstract
The invention discloses a farmland evapotranspiration short-term prediction method considering crop coefficient dynamic change and rainfall, which comprises the steps of obtaining meteorological data of a farmland crop growth environment; calculating a crop coefficient of the predicted benchmark day according to the reference crop evapotranspiration of the predicted benchmark day and the actual field evapotranspiration; respectively constructing a training set and a test set, and preprocessing; establishing a feedforward neural network model considering crop coefficient dynamic change and rainfall influence, and performing training optimization; and predicting the evapotranspiration of the farmland crops in a short term according to the test set data by using the optimized feedforward neural network model. The invention considers the influence of crop coefficient change and rainfall on the evapotranspiration of farmland crops, effectively constructs the nonlinear relation between the evapotranspiration of farmland reference crops and the driving factors thereof, thereby obtaining the evapotranspiration of crops which more accord with the actual growth conditions of the crops and providing scientific basis for the future water management of the underlying surface of the farmland.
Description
Technical Field
The invention belongs to the technical field of crop transpiration prediction, and particularly relates to a short-term prediction method for farmland transpiration by considering crop coefficient dynamic change and rainfall.
Background
The Evapotranspiration process is an important component of land hydrologic cycle, the Evapotranspiration volume (ET)c) The method has important guiding significance for irrigation planning and regional water resource allocation. Accurate prediction of evapotranspiration can save irrigation water to a certain extent, so accurate prediction of crop evapotranspiration is urgently needed in order to better manage crop irrigation water consumption and improve crop water utilization efficiency.
Currently, methods for predicting evapotranspiration are mainly classified into 4 types: time series method, grey model method, empirical formula method and neural network model method. The time series method has uncertainty in prediction accuracy because the data used is single (only historical data of the evapotranspiration amount is used), and the ultra-historical change under the influence of other factors cannot be fully considered. The gray prediction method is essentially an exponential model, and when the target function increases by zero, the system error is serious, and the error is serious when the prediction period is more. The empirical formula method needs parameter correction for different research areas, needs more meteorological data and is more complex in calculation. The artificial neural network is a nonlinear theory developed in recent years, does not need to know specific structural conditions inside a nonlinear system, has the functions of self-organization, self-adaptation and self-learning, is very suitable for simulating and processing systems with multiple influencing factors and complex relationships, and provides an effective way for time series prediction and judgment of highly nonlinear dynamic relationships. The method introduces a calculation method of a neural network to establish a prediction model of a nonlinear artificial neural network aiming at the defects of large blindness, low fitting precision and easy distortion of prediction of a conventional water consumption prediction model in prediction, can consider the influence of a plurality of factors on the evapotranspiration, and has the advantages of high prediction precision, simplicity, convenience and practicability and good application and popularization values.
At present, the method for predicting the evapotranspiration by adopting a BP neural network model mainly comprises the steps of training and testing conventional variables, and mostly adopts FAO recommended crop coefficients or historically measured crop coefficients to predict the evapotranspiration. However, the crop coefficient is also changing continuously with the difference between the crop itself and the external conditions, and has obvious regional and time sequence differences. Research shows that the crop coefficient value recommended based on the FAO is suitable for the calculation of the process with larger time step, but cannot reflect the daily dynamic change condition of crops, and when the evapotranspiration amount is predicted, a predicted value has a slightly larger deviation with an actual value. Therefore, the influence of the growth stage of the crop on the dynamic change of the crop needs to be considered for obtaining the crop coefficient. In addition, rainfall is an important influence factor of the evapotranspiration amount prediction accuracy, and no scholars directly consider rainfall in the evapotranspiration amount prediction at present.
In summary, many studies for predicting the evapotranspiration have been carried out, but two major problems exist at present: (1) the fixed or simple difference crop coefficient recommended by FAO-56 is suitable for predicting the evapotranspiration in a longer period, the application value for predicting the evapotranspiration in a short period is lower, and the basic crop coefficient curve in the crop coefficient method is subjected to linear difference only by 3 determined nodes, so that the crop growth process is simplified, and larger deviation can be caused; (2) due to uncertainty of rainfall, research on a rainfall evapotranspiration prediction model is less, only deterministic meteorological factors are considered, deviation of evapotranspiration prediction under typical weather is inevitably caused, and applicability is weak.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a farmland evapotranspiration short-term prediction method considering crop coefficient dynamic change and rainfall.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
a farmland evapotranspiration short-term prediction method considering crop coefficient dynamic change and rainfall comprises the following steps:
s1, acquiring meteorological data of the growth environment of the farmland crops, wherein the meteorological data comprises the highest air temperature, the lowest air temperature, the sunshine hours and the rainfall;
s2, calculating a crop coefficient of the prediction reference day according to the reference crop evapotranspiration of the prediction reference day and the actual field measurement evapotranspiration;
s3, respectively constructing a training set and a testing set according to the meteorological data acquired in the step S1, the actual field measurement evapotranspiration in the step S2 and the calculated crop coefficient, and preprocessing the data of the training set and the testing set;
s4, establishing a feedforward neural network model considering crop coefficient dynamic change and rainfall influence, and training and optimizing the model by using training set data;
and S5, predicting the evapotranspiration of the farmland crops according to the test set data by using the feedforward neural network model optimized in the step S4.
Further, in step S2, a penman algorithm is specifically used to calculate the reference crop evapotranspiration of the prediction reference day, and the calculation formula is as follows:
wherein, ET0For reference to crop evapotranspiration, delta is the slope of the saturated water-steam pressure curve, Rn is the net surface radiation, G is the soil heat flux, gamma is the dry-wet constant, TmeanIs a dayAverage temperature u2To set the altitude position wind speed, esSaturated water vapor pressure, eaThe actual water vapor pressure.
Further, in step S2, a vorticity correlation method is specifically used to calculate the actual field measured evapotranspiration of the predicted reference day, and the calculation formula is as follows:
wherein w 'is the pulsating quantity of the vertical wind speed, and q' is the pulsating value of the water vapor density.
Further, the calculation formula of the crop coefficient for the prediction reference day in step S2 is as follows:
wherein, KcTo predict the crop coefficient of the reference day, ETc-ECIs the measured value of the vorticity correlation system;
and setting the crop coefficient of the farmland crops in the set time in the future to be the same as the crop coefficient of the prediction reference day.
Further, the preprocessing of the training set and the test set data in the step S3 specifically includes:
and adopting a hyperbolic tangent transformation function to carry out standardization processing on the sample data measurement value according to the weights of the maximum value and the minimum value of the sample data measurement value in the training set and the test set, wherein the weights are expressed as follows:
wherein, X' is the measured value of the sample data after standardization, X is the measured value of the sample data, Xmax、XminRespectively the maximum and minimum values of the sample data measurement.
Further, the step S4 is specifically:
a feedforward neural network model of a three-layer topological structure comprising an input layer, a hidden layer and an output layer is constructed, and the input layer comprises 4 neurons, the hidden layer comprises 10 neurons and the output layer comprises 1 neuron in the BP neural network.
The invention has the following beneficial effects:
the method considers the influence of the crop coefficient dynamic change and rainfall factors on the short-term prediction of the evapotranspiration of the farmland crops, establishes a feedforward neural network model considering the crop coefficient dynamic change and the rainfall influence, trains and optimizes the model by using the evapotranspiration actually measured by a vorticity correlation method as an actually measured value, effectively constructs the nonlinear relation between the reference crop evapotranspiration of the farmland and the driving factors thereof, can obtain the crop evapotranspiration more conforming to the actual growth condition of the crops, and provides scientific basis for the future water management of the underlying surface of the farmland. .
Drawings
FIG. 1 is a schematic flow chart of a short-term farmland evapotranspiration prediction method of the present invention considering crop coefficient dynamic changes and rainfall;
FIG. 2 is a comparison graph of predicted values and measured values of the multiple linear regression model according to the present invention;
FIG. 3 is a diagram illustrating the verification results of the predicted values and measured values of the multiple linear regression model according to the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, an embodiment of the present invention provides a short-term prediction method for a field evapotranspiration considering dynamic changes of crop coefficients and rainfall, including the following steps S1 to S5:
s1, acquiring meteorological data of the growth environment of the farmland crops, wherein the meteorological data comprises the highest air temperature, the lowest air temperature, the sunshine hours and the rainfall;
in this embodiment, the present invention uses the great happy area of Beijing as the research area to explain the related data. The great-rise district (39 degrees 26 '-39 degrees 51' N, 116 degrees 13 '-116 degrees 43' E) in Beijing City is positioned in the eternal river strike plain in the north of the North China plain, the total area is 1031km2, the district belongs to temperate zone semi-humid monsoon climate, and the average temperature of many years is 12.1 ℃. The average rainfall in many years is 540mm, the rainfall is more in 7 months and 9 months, and the rainfall accounts for more than 80% of the total rainfall in all years. The bottom surface of a great-rise test station in Beijing city is mainly a farmland, and comprises corn/wheat and soybean, wherein the corn and wheat are mainly used, the whole growth period of winter wheat is about 260 days (10 months 1 day-6 months 30 days in the next year), and the winter wheat needs to be supplemented with irrigation in normal years so as to ensure the requirement of crops on water. The growth period of summer corn is about 90 days (7 months 1 day-9 months 30 days), and water is not added into the summer corn in the whole growth period. No water stress exists in the growth stage of the crops in the research area. Compared with the great-rise area, the climate and the underlying surface condition of the test station are analyzed, and the test station has better typicality.
According to the invention, historical meteorological data of 2015-2017 years are collected in a China meteorological science sharing service network, and 1 d-7 d forecast period day-by-day weather forecast data of 2018-2019 years are collected in a weather network. The historical meteorological data includes: air pressure PaWind speed U and maximum air temperature TmaxMinimum air temperature TminAverage relative humidity RH, sunshine hours n, rainfall P, and the like. Weather forecast data and information include: maximum air temperature TmaxMinimum air temperature TminWeather conditions, etc.
The rainfall total grade is estimated to be 11 grades according to the rainfall grade table according to various phenomena caused by the influence of rainfall on the ground. And then determining the rainfall value according to the rainfall forecast information of the weather forecast. The rainfall rating table is shown in table 1.
TABLE 1 rainfall rating table
The number of sunshine hours in the present invention refers to the length of time during which the intensity of the radiation of the sun on a plane perpendicular to its rays exceeds or is equal to 120w/m2 per day. Radiation R per day of the year according to regional geographical location parametersaCan be calculated by the solar constant, the solar inclination angle and the like, and the calculation formula is as follows:
wherein R isaIs the radiation, c is the speed of light, GscIs the sun constant, drThe relative distance between the sun and the earth, W is the sun inclination angle, h is the local latitude, and radian units are adopted; k is a radical ofsThe sunset hour angle.
S2, calculating a crop coefficient of the prediction reference day according to the reference crop evapotranspiration of the prediction reference day and the actual field measurement evapotranspiration;
in this embodiment, in order to obtain a reference crop evapotranspiration amount with a more accurate predicted base day, the reference crop evapotranspiration amount of the predicted base day is calculated by using a (Penman-Monteith) PM method based on meteorological data, and the calculation formula is as follows:
wherein, ET0For reference to crop evapotranspiration, delta is the slope of the saturated water-steam pressure curve, Rn is the net surface radiation, G is the soil heat flux, gamma is the dry-wet constant, TmeanIs the average daily temperature u2To set the wind speed at the altitude, in particular at a height of 2 m, esSaturated water vapor pressure, eaThe actual water vapor pressure.
The invention adopts a vorticity correlation system (Campbell Scientific Inc., USA) to measure the actual field measurement evapotranspiration of a prediction reference day, and the calculation formula is as follows:
wherein w 'is the pulsating quantity of the vertical wind speed, and q' is the pulsating value of the water vapor density.
The vorticity correlation system comprises a CSAT3 type three-dimensional ultrasonic anemograph, an LI7500 CO2/H2O open-circuit gas analyzer, an HMP45C air temperature and humidity sensor, a CR5000 type data acquisition unit and the like. The net radiation Rn is measured by a CNR4 net radiation sensor, the soil heat flux G is measured by two HFP01 soil heat flux plates which are located 2cm below the ground surface, the average value of all the measurement items is 30min and is used as a record value every time, and the daily evapotranspiration is accumulated by 24h data. In the process of processing the vorticity-related actual data, eliminating abnormal data according to the following principles: the precipitation time period and the data of the previous 1h and the next 1 h; data significantly beyond physical meaning; and thirdly, abnormal data of the sensor state. In addition, errors caused by energy non-closure are eliminated by calculating the intraday Bowegian ratio to correct latent heat flux.
The invention calculates the crop coefficient of the prediction reference day according to the reference crop evapotranspiration and the actual farmland evapotranspiration of the prediction reference day obtained by the method, and the crop coefficient is expressed as follows:
wherein, KcTo predict the crop coefficient of the reference day, ETc-ECIs the measured value of the vorticity correlation system;
setting crop coefficient of farmland crops in a set short-term time in the future to be the same as that of the forecast benchmark day, specifically setting crop coefficient in 1-7 days in the future to be the same as that of the dynamic crop coefficient of the forecast benchmark day, namely K'c-var1＝Kc＝K′c-var2……＝K′c-var7。
Because the crop coefficient of the forecast reference day can reflect the influence of meteorological data on the evapotranspiration, and can also reflect the influence of crop types, soil water and fertilizer conditions and field management levels on the evapotranspiration, the method can more accurately forecast the evapotranspiration of the farmland crops by constructing the short-term forecast of the evapotranspiration of the farmland crops by considering the influence of the crop coefficient of the forecast reference day on the short-term forecast of the evapotranspiration of the farmland crops, thereby realizing the short-term accurate forecast of the evapotranspiration of the farmland crops.
S3, respectively constructing a training set and a testing set according to the meteorological data acquired in the step S1, the actual field measurement evapotranspiration in the step S2 and the calculated crop coefficient, and preprocessing the data of the training set and the testing set;
in this embodiment, since some of the meteorological data in 2015 to 2019 acquired in step S1 may be absent or abnormal due to environmental interference or human operation, 900 sets of data sets are finally obtained after the acquired meteorological data are filtered. 100 groups of data are randomly selected from the meteorological data of the 3-month-9 monthly history of 2015-2017 as a training set in each training sample, and 100 groups of data are randomly selected from the meteorological forecast data of the 3-month-9 monthly history of 2018-2019 as a verification set.
Because the dimensions of various data in the data set are different and the magnitude is larger, the invention needs to preprocess the data of the training set and the test set; specifically, the data values are standardized, so that over-training is avoided, the convergence degree and the calculation speed of the ganglionic point numbers of certain layers are improved, and the calculation accuracy is improved.
The invention adopts a hyperbolic tangent transformation function, and carries out standardization processing on sample data measurement values according to weights of the maximum value and the minimum value of the sample data measurement values in a training set and a test set, wherein the weights are expressed as follows:
wherein, X' is the measured value of the sample data after standardization, X is the measured value of the sample data, Xmax、XminRespectively the maximum and minimum values of the sample data measurement.
By normalizing the sample data measurement values, the sample data measurement values can be normalized to be in the range of [ -1,1], thereby exhibiting the most nonlinear characteristics.
S4, establishing a feedforward neural network model considering crop coefficient dynamic change and rainfall influence, and training and optimizing the model by using training set data;
in this embodiment, the BP model modeling includes two stages: preprocessing (including variable selection, data segmentation, and data normalization), training (including architecture and network training processes). And (3) realizing full connection of the neurons of the upper layer of BP to the neurons of the lower layer through a transfer function, wherein the neurons of the same layer are unrelated. When the learning samples are provided to the neural network, the neural network first performs a forward propagation process. If the error between the output and the target output exceeds the expectation, the forward propagation process is switched to a backward propagation process, the error signal is returned along the original connecting path, and the error signal is reduced by modifying the weight of each layer of neuron. With the continuous correction of the error back propagation, the accuracy of the network to the input mode response is continuously improved, and the applicable precision is finally achieved.
The setting range of the number of the hidden layer units is 3-12 according to the following formula:
wherein J is the number of hidden layer units, and A is the number of input layer units; b is the number of output layer units; k is a constant and takes a value of 1-10.
Since the number of neurons in input and output is determined by the target and the connection method is fixed, the structure mainly depends on the number of hidden layer neurons. Too few hidden nodes may affect the functionality of the network, while too many hidden nodes may result in an over-adaptation of the network to the data. Therefore, the optimal BP neural network architecture 4-10-1 is set in the invention, namely an input layer in the BP neural network comprises 4 neurons, a hidden layer comprises 10 neurons and an output layer comprises 1 neuron; and the learning rate and iteration of training are set to 0.1 and 5000, respectively.
The invention trains a feedforward neural network model considering crop coefficient dynamic change and rainfall influence by using training set data as input variables, calculates and predicts actual farmland evapotranspiration of a reference day as output variables by a vorticity correlation method, and finally obtains the optimized feedforward neural network model through repeated iterative training.
And S5, predicting the evapotranspiration of the farmland crops according to the test set data by using the feedforward neural network model optimized in the step S4.
In order to verify the prediction effect of the feedforward neural network model considering the dynamic change of the crop coefficient and the rainfall influence, the feedforward neural network model considering the dynamic change of the crop coefficient and the rainfall influence is compared with a multiple linear regression Model (MLR), and the average absolute error MAE, the root mean square error RMSE and the decision coefficient R are adopted2And forecasting accuracy ACC to evaluate the prediction accuracy of the model.
The calculation formula of the evaluation parameters is as follows:
wherein x isiIs a predicted value of evapotranspiration of the farmland crop, yiAn expected output value of the evapotranspiration of the farmland reference crop; i is a prediction sample sequence, i is 1,2, …, n;
is the average of the predicted value and the expected output value sequence; n is the number of samples of the predicted value. The ACC can give a measure of whether the external prediction ability of a single model achieves the accuracy required by statistics, and the closer the value is to 1, the more the prediction is representedThe more the value matches the measured value, the more ACC is considered>The model has actual prediction value at 0.6.
As shown in fig. 2 and fig. 3, the graphs are compared between the predicted value and the actual value of the feedforward neural network model and the multiple linear regression model in consideration of the dynamic change of the crop coefficient and the influence of rainfall. As can be seen from the figure, the predicted values of the two methods are basically consistent with the change trend of the measured values, but the feedforward neural network model considering the dynamic change of the crop coefficient and the influence of rainfall has higher pre-accuracy rate and smaller errors, and the superiority of the method in reflecting the complex nonlinear relationship is also proved.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (3)
1. A farmland evapotranspiration short-term prediction method considering crop coefficient dynamic change and rainfall is characterized by comprising the following steps:
s1, acquiring meteorological data of the growth environment of the farmland crops according to meteorological forecast data, wherein the meteorological data comprise the highest air temperature, the lowest air temperature, sunshine hours and rainfall;
s2, calculating the crop coefficient of the prediction reference day according to the reference crop evapotranspiration of the prediction reference day and the actual field evapotranspiration, and specifically:
calculating the reference crop evapotranspiration of the predicted reference day by adopting a Peneman algorithm, wherein the calculation formula is as follows:
wherein, ET0For reference to crop evapotranspiration, Delta is saturated water vaporSlope of the pressure curve, Rn is net surface radiation, G is soil heat flux, gamma is dry-wet constant, TmeanIs the average daily temperature u2To set the altitude position wind speed, esSaturated water vapor pressure, eaThe actual water vapor pressure;
calculating the actual field measurement evapotranspiration of the predicted reference day by adopting a vorticity correlation method, wherein the calculation formula is as follows:
wherein w 'is the pulsating quantity of vertical wind speed, and q' is the pulsating value of water vapor density;
calculating the crop coefficient of the prediction reference day according to the reference crop evapotranspiration of the prediction reference day and the actual measurement evapotranspiration of the farmland, wherein the calculation formula is as follows:
wherein, KcTo predict the crop coefficient of the reference day, ETc-ECIs the measured value of the vorticity correlation system;
setting crop coefficients of the farmland crops in the set time in the future to be the same as the crop coefficients of the prediction reference days;
s3, respectively constructing a training set and a testing set according to the meteorological data obtained in the step S1, the actual field measurement evapotranspiration in the step S2 and the calculated crop coefficient, and preprocessing the data of the training set and the testing set;
s4, establishing a feedforward neural network model considering the dynamic change of crop coefficients and rainfall influence, and training and optimizing the model by using training set data;
and S5, predicting the evapotranspiration of the farmland crops according to the test set data by using the feedforward neural network model optimized in the step S4.
2. The method for short-term prediction of farmland evapotranspiration considering crop coefficient dynamic changes and rainfall as claimed in claim 1, wherein the preprocessing of the training set and test set data in step S3 is specifically:
and adopting a hyperbolic tangent transformation function to carry out standardization processing on the sample data measurement value according to the weights of the maximum value and the minimum value of the sample data measurement value in the training set and the test set, wherein the weights are expressed as follows:
wherein, X' is the measured value of the sample data after standardization, X is the measured value of the sample data, Xmax、XminRespectively the maximum and minimum values of the sample data measurement.
3. The method for short-term prediction of farmland evapotranspiration considering crop coefficient dynamic changes and rainfall as claimed in claim 1, wherein said step S4 is specifically:
a feedforward neural network model of a three-layer topological structure comprising an input layer, a hidden layer and an output layer is constructed, and the input layer comprises 4 neurons, the hidden layer comprises 10 neurons and the output layer comprises 1 neuron in the BP neural network.
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| CN114021486B (en) * | 2022-01-05 | 2022-04-08 | 中化现代农业有限公司 | Crop evapotranspiration prediction method and device |
| CN115049126A (en) * | 2022-06-10 | 2022-09-13 | 中国水利水电科学研究院 | Evapotranspiration prediction method based on temperature effect and historical threshold |
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