CN101158674A - Method for predicting chlorophyll a concentration in water based on BP nerval net - Google Patents
Method for predicting chlorophyll a concentration in water based on BP nerval net Download PDFInfo
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- CN101158674A CN101158674A CNA2007101501854A CN200710150185A CN101158674A CN 101158674 A CN101158674 A CN 101158674A CN A2007101501854 A CNA2007101501854 A CN A2007101501854A CN 200710150185 A CN200710150185 A CN 200710150185A CN 101158674 A CN101158674 A CN 101158674A
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Abstract
The invention relates to a density forecast method of the chlorophyll a stemmed from a water body of the BP neural network. The density forecast method comprises the following steps: (1) the chlorophyll a in the tested water body and the value of other correlative water quality index which influences the chlorophyll a are acquired as the examination data. (2) The neural network of an error back propagation is established. (3) The neural network is trained and tested. (4) The neural network which passes the test is utilized to forecast the chlorophyll a in the water body. Other water qualities which influence the chlorophyll a are: Ammonia nitrogen, total nitrogen, total phosphorus, orthophosphate, permanganate index, temperature, dissolved oxygen, pH, suspension, five-day biochemical oxygen demand. The step (1) also comprises a normalization process. The data of the chlorophyll a and other ten water quality indexes are between -1 and +1 after the data of the chlorophyll a and other ten water quality indexes are normalized. The neural network comprises an input layer, an intermediate layer and an output layer. The invention can establish a forecast model related to the chlorophyll a, just needing the experiment which has the limited times. The chlorophyll in the river can be accurately and quickly forecasted through the computer simulation experiment and the science forecast.
Description
Technical field
The present invention relates to chlorophyll Forecasting Methodology in a kind of river, particularly a kind of can predict accurately and rapidly in the river chlorophyllous based on chlorophyll a concentration prediction method in the water body of BP neural network.
Background technology
Have complicated physics, chemistry and bioprocess in the water body, the variation of chlorophyll-a concentration is very difficult after raising for the prediction trophic level.Traditional predicting means (comprising deterministic models and empirical model) is although can predict chlorophyll-a concentration, because determining of the adjustment of data process, particularly some parameters of necessary experience overlength consequently is difficult to directly application.In addition, traditional forecast model is ignored the important factor that influences body eutrophication easily, ecological factor for example, and these factors are with the complicacy of limited model.Neural network (neural networks) is a kind of model of being made up of the elementary factor of some parallel actions, can solve the challenge of many sciemtifec and technical spheres after training.Wherein, BP neural network (Back-Propagation Network, reverse transmittance nerve network) is one of most popular neural network model in the present water quality prediction.Can predict the value of chlorophyll a in the river accurately and rapidly based on the Forecasting Methodology of neural network, save great amount of manpower and material resources.
Summary of the invention
Technical matters to be solved by this invention is, provides a kind of and can solve the problem that traditional forecast model has, predict accurately and rapidly chlorophyll a in the river based on chlorophyll a concentration prediction method in the water body of BP neural network.
The technical solution adopted in the present invention is: a kind of based on chlorophyll a concentration prediction method in the water body of BP neural network, comprise the steps: that (1) obtains in the tested water body chlorophyll a and its influential other correlation water are referred to that target value is as detecting data; (2) set up the error back propagation neural network; (3) neural network is carried out training and testing; (4) utilization is predicted chlorophyll-a concentration in the water body by the neural network of test.
Described influential other water quality of chlorophyll a are meant: ammonia nitrogen, total nitrogen, total phosphorus, orthophosphate, permanganate index, temperature, dissolved oxygen DO, pH, suspension, five-day BOD.
Described step (1) also comprise to the data normalization of chlorophyll a and other 10 water-quality guideline to-1 and+normalization process between 1.
Described neural network comprises an input layer, a middle layer and an output layer.
Described input layer has 11 neurons, and there are 17 neurons in the middle layer, and output layer has 1 neuron.
The neuronic transport function in described middle layer adopts S type tangent activation function, and output layer is a S type logarithmic function.
Describedly neural network is carried out training and testing be, Monitoring Data is divided into two parts, preceding 70% is used for training network, is designated as training sample, and back 30% is used for supervising network, is designated as test samples; To the network repetition training, when error reaches 0.001 between predicted value and Monitoring Data, stop training, begin prediction.
Described training to neural network is to adopt error backpropagation algorithm to train.
In step (3), when neural network to the predicated error of each group test samples when all being lower than prescribed level by test, carry out the prediction work of step (4) then.
In step (4), utilize when predicting by the neural network of test, earlier Monitoring Data is normalized to-1 and+1 between, import again, and the output valve after the network operations carried out anti-normalization, promptly obtain the chlorophyll a predicted value.
Of the present invention based on chlorophyll a concentration prediction method in the water body of BP neural network, solved and predicted chlorophyllous problem in the river accurately and rapidly.Utilize the present invention, only need carry out the limited number of time test, just can set up relevant chlorophyll forecast model, by computer simulation experiment, scientific forecasting, thus significantly reduce the quantity of investigative test, significantly reduce the consumption of material, manpower, the energy, further improve forecast quality.
Description of drawings
Fig. 1 is neural network structure figure;
Fig. 2 is the prognostic chart of the chlorophyll a of training sample;
Fig. 3 is the prognostic chart of the chlorophyll a of test samples.
Embodiment
Make a detailed description based on chlorophyll a concentration prediction method in the water body of BP neural network of the present invention below in conjunction with embodiment.
Of the present invention based on chlorophyll a concentration prediction method in the water body of BP neural network, comprise the steps:
(1) obtains in the tested water body chlorophyll a and its influential other correlation water are referred to that target value is as detecting data.
Obtain in the embodiments of the invention is by to Jin He, 9 monitoring points, Wei Jin river, to adopt water sample one time in per three days, continuous 14 monitorings, mensuration ammonia nitrogen (NH to influential other water quality of chlorophyll a
3-N), total nitrogen (TN), total phosphorus (TP), orthophosphate (DRP), permanganate index (COD
Mn), temperature (T), dissolved oxygen DO (DO), pH, suspension (SS), five-day BOD (BOD
5) and the value of 11 water-quality guideline of chlorophyll a (Chl-a), obtain desired data.
In obtaining the river during value of chlorophyll a, must with chlorophyll a and to the numerical value of its influential 10 water-quality guideline normalize to-1 and+1 between.
(2) set up the error back propagation neural network.
Described neural network is made up of an input layer, a middle layer and an output layer.Wherein, input layer has 11 neurons, respectively corresponding ammonia nitrogen (NH
3-N), total nitrogen (TN), total phosphorus (TP), orthophosphate (DRP), permanganate index (COD
Mn), temperature (T), dissolved oxygen DO (DO), pH, suspension (SS), five-day BOD (BOD
5) and 11 water-quality guideline of chlorophyll a background values (Chl-a); Middle layer neuron number is difficult to be determined, but the influence to the degree of accuracy of model and accuracy is very big, computing repeatedly (this compute mode provides in the back) by comparing the output result of the different situation lower network of middle layer neuron number, determines that finally the middle layer neuron number is 17; The network output layer is the desired value chlorophyll a, so have only 1 neuron, is the chlorophyll a value after three days.The neuronic transport function in described middle layer adopts S type tangent activation function, and the neuronic transport function of output layer adopts S type logarithmic function, all data normalizations to-1 and+1 between.
(3) neural network is carried out training and testing.
Describedly neural network is carried out training and testing be, with the ammonia nitrogen (NH that monitors every day
3-N), total nitrogen (TN), total phosphorus (TP), orthophosphate (DRP), permanganate index (COD
Mn), temperature (T), dissolved oxygen DO (DO), pH, suspension (SS), five-day BOD (BOD
5) and the value of chlorophyll a be designated as one group of data, and all group Monitoring Data are divided into two parts, preceding 70% is called training sample, the back 30% is called test samples.
Training sample with 70% is used for training network, sets up study mechanism, promptly when one group of data of certain day of input, promptly provides ammonia nitrogen (NH
3-N), total nitrogen (TN), total phosphorus (TP), orthophosphate (DRP), permanganate index (COD
Mn), temperature (T), dissolved oxygen DO (DO), pH, suspension (SS), five-day BOD (BOD
5) and the such one group of input of chlorophyll a during data, the middle layer neuron number gets 17, through the automatic computing of network, have an output valve (the chlorophyll a values after three days of prediction), compare the error between output valve and the desired output (actual measurement chlorophyll a value), if error is less than designated precision, then study finishes.Otherwise, with the original access path backpropagation in error signal edge, and progressively adjust the connection weights of each layer, till error is less than designated precision, first group of study this moment is finished, enter next group study, up to connect weights to the predicated error of all training groups all in specified scope, the best weight value of output this moment.The training group is many more, and the study of network is abundant more, and the network empirical value is big more, and precision of prediction is high more.To the network repetition training, when error reaches 0.001, stop training, begin prediction.Forecast model desired value and output valve related coefficient are up to 0.9887 at this moment, and root-mean-square error is 0.2550.
Test samples with other 30% is used for supervising network.After network training finishes, utilize other 30% data to come supervising network, see model gets whether to meet the requirements.Utilize the other 30% group of pairing chlorophyll a value of neural network prediction, the error between contrast model predication value and actual measured value is passed through test when neural network when the predicated error of each group test data all is lower than prescribed level, can be used for prediction work.Related coefficient is 0.8376 between model predication value and measured value at this moment, and root-mean-square error is 0.5785, by test.
In training, when all being lower than prescribed level, the predicated error of each group test data passes through test when neural network, can be used for prediction work.
(4) utilization is predicted by the neural network of test.
Utilization by the neural network of test predict must with the input data normalization to-1 and+1 between, be input to again in the neural network by test, and the output after the network operations carried out anti-normalization, just obtain the chlorophyll a predicted value.
The computing of described in front definite middle layer neuron number is realized by following computation process.
BP neural network model of the present invention is most popular in actual applications neural network model, except input layer and output layer, also can contain one or more middle layers, every layer has several neurons, same interlayer neuron does not connect, and realizes being connected entirely between the lower floor neuron.When a numerical value through the middle layer when output layer is propagated, information is just caught by neuron.Be connected weights between i neuron and j neuron and be designated as w
JiJ neuronic total input vector is each neuron input vector of one deck x before it
iBe connected weight w with it
JiThe summation of product is designated as:
Each neuron output value is by the total input vector U of preceding one deck neuron
jDetermine with activation function f, be designated as:
y
j＝f(u
j) (2)
Wherein, Chang Yong f function is the logarithm or the tangent activation function of S type.
Earlier to weight w in the network layer
JiGet random quantity between (1 ,+1) as initial value, import sample then and learn.Whenever finish one time, the output result of comparative sample and the error of desired output, if error less than designated precision, is then learnt end, and output best weight value at this moment.Otherwise, error signal along original access path backpropagation, and is progressively adjusted the connection weights of each layer, till error is less than designated precision.Utilize this model just can predict the amount of chlorophyll a in the river accurately and rapidly, significantly reduce the consumption of material resources, manpower, the energy.
Above presentation of results, the neural network of being set up all has good prediction effect to training group and test group, thereby has stronger popularization ability.Present embodiment shows that the present invention can predict chlorophyll in the river accurately and rapidly, and Forecasting Methodology has stronger popularization ability, has broad application prospects.
Claims (10)
1. one kind based on chlorophyll a concentration prediction method in the water body of BP neural network, it is characterized in that, comprises the steps:
(1) obtains in the tested water body chlorophyll a and its influential other correlation water are referred to that target value is as detecting data;
(2) set up the error back propagation neural network;
(3) neural network is carried out training and testing;
(4) utilization is predicted chlorophyll-a concentration in the water body by the neural network of test.
2. according to claim 1 based on chlorophyll a concentration prediction method in the water body of BP neural network, it is characterized in that, described influential other water quality of chlorophyll a are meant: ammonia nitrogen, total nitrogen, total phosphorus, orthophosphate, permanganate index, temperature, dissolved oxygen DO, pH, suspension, five-day BOD.
3. according to claim 1 and 2 based on chlorophyll a concentration prediction method in the water body of BP neural network, it is characterized in that, described step (1) also comprise to the data normalization of chlorophyll a and other 10 water-quality guideline to-1 and+normalization process between 1.
4. according to claim 1ly it is characterized in that based on chlorophyll a concentration prediction method in the water body of BP neural network described neural network comprises an input layer, a middle layer and an output layer.
5. according to claim 4ly it is characterized in that based on chlorophyll a concentration prediction method in the water body of BP neural network described input layer has 11 neurons, there are 17 neurons in the middle layer, and output layer has 1 neuron.
6. according to claim 5ly it is characterized in that based on chlorophyll a concentration prediction method in the water body of BP neural network that the neuronic transport function in described middle layer adopts S type tangent activation function, output layer is a S type logarithmic function.
7. according to claim 1 based on chlorophyll a concentration prediction method in the water body of BP neural network, it is characterized in that, describedly neural network is carried out training and testing be, Monitoring Data is divided into two parts, preceding 70% is used for training network, be designated as training sample, back 30% is used for supervising network, is designated as test samples; To the network repetition training, when error reaches 0.001 between predicted value and Monitoring Data, stop training, begin prediction.
8. according to claim 1 or 7 described based on chlorophyll a concentration prediction method in the water body of BP neural network, it is characterized in that: described training to neural network is to adopt error backpropagation algorithm to train.
9. according to claim 1 or 7 described based on chlorophyll a concentration prediction method in the water body of BP neural network, it is characterized in that, in step (3), when neural network to the predicated error of each group test samples when all being lower than prescribed level by test, carry out the prediction work of step (4) then.
10. according to claim 1 based on chlorophyll a concentration prediction method in the water body of BP neural network, it is characterized in that, in step (4), when utilization is predicted by the neural network of test, earlier Monitoring Data is normalized to-1 and+1 between, import again, and the output valve after the network operations is carried out anti-normalization, promptly obtain the chlorophyll a predicted value.
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