CN108510191B - Mangrove ecological health evaluation method based on stacking noise reduction automatic coding algorithm - Google Patents
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Abstract
The invention provides a mangrove forest ecological health evaluation method based on a stacking noise reduction automatic coding algorithm, which mainly comprises the following steps: 1) and determining the mangrove forest ecological protection area. 2) And establishing a mangrove forest ecological index data set. 3) Data set X and data set Y are preprocessed. Taking the normalized data set X as the data set R1.4) The data in data set X is trained using a stacked noise reduction auto-encoding algorithm. 5) And establishing a mangrove forest ecological index prediction model. 6) And predicting the future ecological health condition of the mangrove forest in the mangrove forest ecological protection area by utilizing the mangrove forest ecological index prediction model, the linear training set S and the mangrove forest ecological health evaluation grade judgment table. The method utilizes the predicted important ecological indexes and combines the mangrove forest ecological health evaluation grade judgment table to finally realize accurate prediction of the mangrove forest ecological health condition.
Description
Technical Field
The invention relates to a data mining technology and a deep learning method, in particular to a mangrove forest ecological health evaluation method based on a stacking noise reduction automatic coding algorithm.
Background
The mangrove forest is a woody plant community which grows in the intertidal zone of the tropical and subtropical coast and is periodically immersed by seawater, is a complex ecosystem with land and sea characteristics and an important ecological key zone of the coast, and plays an irreplaceable role in improving gulfs, preventing waves and protecting dikes, purifying pollution, protecting wetland diversity and the like. The coast of Guangxi is an important mangrove forest distribution area in mainland of China, the land line length is 1490km, the area of the mangrove forest is the second nationwide, and the district with the largest mangrove forest distribution area per unit land line length is the province. The Guangxi is provided with 3 mangrove natural protection areas (the Shankou national natural protection area of Guangxi Hepu, the Beilun river national natural protection area of Guangxi City harbor and the Maotai province natural protection area of Guangxi Qinzhou). According to the current environmental status report of the ecological protection area of the river mouth in northern deluxe in the past eight years, mangrove forest ecological indexes including water environment, deposition environment, plankton, phytoplankton, intertidal zone organisms, mangrove forest communities and birds can be known. Wherein the indexes of plankton, phytoplankton, intertidal zone organisms, mangrove community and birds can most directly reflect the ecological health of mangrove. The analysis shows that the indexes are closely related, for example, the water environment has great influence on the survival of zooplankton, phytoplankton and intertidal zone organisms, and the deposition environment has great influence on the survival of zooplankton and phytoplankton. However, the sensing and image technology can only collect part of mangrove forest ecological indexes, such as water environment, deposition environment pests, pests and the like. Therefore, the sensing and image technology is difficult to accurately evaluate and predict the ecological health condition of the mangrove forest.
Disclosure of Invention
The present invention is directed to solving the problems of the prior art.
The technical scheme adopted for achieving the purpose of the invention is that the mangrove forest ecological health evaluation method based on the stacking noise reduction automatic coding algorithm mainly comprises the following steps:
1) and determining the mangrove forest ecological protection area.
2) And establishing a mangrove forest ecological index data set.
And taking mangrove forest ecological index data information I acquired by the mangrove forest ecological protection area in eta years by using a sensing and image technology as a data set X. The mangrove forest ecological index data information I mainly comprises water quality information, sediment pH, soil pH and soil granularity indexes.
The water quality information mainly comprises water temperature, salinity, pH, chlorophyll, ammonia nitrogen, nitrate, nitrite, inorganic phosphorus, petroleum and chemical oxygen demand.
The deposits comprise mainly organic carbon and sulphides.
Data set X is shown below:
in the formula (I), the compound is shown in the specification,
is mangrove forest ecological index data information I.
And taking mangrove forest ecological index data information II acquired manually in the ecological protection area within eta years as a data set Y. The mangrove forest ecological index data information II mainly comprises fish information, shrimp information, microorganism information, algae information, Chemical Oxygen Demand (COD) information, Biochemical Oxygen Demand (BOD) information, mangrove forest pest information, community type, benthonic animal information, phytoplankton information, intertidal zone biological information, lepidoptera information and coleoptera information.
Data set Y is shown below:
in the formula (I), the compound is shown in the specification,
is mangrove forest ecological index data information II.
3) Data set X and data set Y are preprocessed. The preprocessing mainly comprises denoising and normalization. CountingThe data set after X normalization is R1。
4) The data in data set X is trained using a stacked noise reduction auto-encoding algorithm.
The main steps for training the data in the data set X are as follows:
4.1) determining the network structure of the stacking noise reduction automatic coding algorithm. And setting the network to have M layers, wherein the number of input layers is 1, the number of transition layers is M-2, and the number of output layers is 1.
4.2) data set R1As an input layer to the network.
4.3) setting the initial weight of the transition layer. Training the transition layer by adopting a noise reduction automatic coding algorithm to obtain a training result H1。
4.4) training results H1As the input layer for the next layer. Repeating the step 3 to obtain a training result H2。
4.5) repeating the step 3 and the step 4M-2 times to finally obtain the output result of the M layer, namely the output layer. And recording the output result as a data set V, and finishing the training.
5) And establishing a mangrove forest ecological index prediction model.
The method mainly comprises the following steps of:
5.1) designing an inverse decision algorithm with the number of layers p. The reverse decision algorithm mainly comprises an input layer, a transition layer and an output layer. The number of the input layers is 1. The number of the transition layers is p-1. The number of the output layers is 1.
And taking the data set V and the data set Y as a training set D of the mangrove forest ecological index prediction model. Training set D is as follows:
D＝{(V1，Y1)，(V2，Y2)……(Vm，Ym)}。 (3)
in the formula, ViIs row i of data set V. Y isiIs row i of data set Y.
5.2) inputting the data set V as a characteristic.
5.3) recording the weight between the ith and jth layerHeavy is Wij. Bias of j-th layer is thetaj。
Wherein, Wij∈[-1,0]。θj∈[-1,0]。
5.4) input I of the j-th layerjAs follows:
in the formula, WijIs the weight between the ith and jth layers. ThetajIs the bias of the j-th layer. O isiIs the output of the ith layer. i is the level number. j is the level number.
Output of j-th layer OjAs follows:
in the formula IjIs the input to the j-th layer. j is the level number.
Input I through the j-th layerjAnd output of j-th layer OjForward transmission to obtain the training result of the first layer as S1。
5.5) adding S1As an input layer for the next layer. Defining an error function Err1j. Error function Err1jAs follows:
in the formula, WjkIs the weight between the j-th and k-th layers. O isjIs the output of the j-th layer. j is the level number. And k is a layer sequence number.
Weight change amount Δ WijAs follows:
ΔWij＝(l)Err1jOi。 (7)
in the formula, OiIs the output of the ith layer. i is the level number. Err1jIs an error function.
Offset change deltajAs follows:
Δθj＝(l)Err1j。 (8)
in the formula, j is a hierarchical sequence number. (l) Is a learning coefficient. Err1jIs an error function.
Repeating the step 3 and the step 4 to obtain a training result S of the layer 22。
5.6) repeating the steps 3 to 5p-1 times to finally obtain the p layer, namely the output result Y of the output layer1。
5.7) for the output layer, an error function Err2 is definedj。
Err2j＝Oj(I-Oj)(1-Oj)。 (9)
In the formula, OjIs the output of the j-th layer. j is the level number.
In the negative gradient direction, with a minimum Err2jAnd a minimum of thetajOn the basis of the values, the weight W is adjusted using the formula (10) and the formula (11)ijAnd an offset thetaj。
W′ij＝Wij+ΔWij。 (10)
In the formula, WijIs the weight between the ith and jth layers. Δ WijIs the amount of weight change. W'ijTo adjust the weight between the ith layer and the jth layer.
θj＝θj+Δθj。 (11)
In the formula, thetajIs the bias of the j-th layer. Delta thetajIs the amount of change in the bias. Theta'jTo adjust the bias of the j-th layer after the adjustment.
5.8) judging the weight W 'between the ith layer and the jth layer after adjustment'ijWhether or not it is below a threshold value epsilon1. Determining offset theta 'between the ith and jth layers after adjustment'jWhether or not it is below a threshold value epsilon2。
5.9) if W'ij≥ε1Then W'ijValue of as WijValue, repeating equation (10) to retrieve the weight W 'between the adjusted ith and jth layers'ijAnd step 5.8 is repeated.
If theta'j≥ε2Then theta 'will be'jValue of (2) is θ'jValue, repeat equation (11) to recover the offset θ 'between the ith and jth layers after adjustment'jAnd step 5.8 is repeated.
If W'ij＜ε1And θ'j＜ε2And if the result is true, finishing training to obtain the mangrove forest ecological index prediction model.
6) And predicting the future ecological health condition of the mangrove forest in the mangrove forest ecological protection area by utilizing the mangrove forest ecological index prediction model, the linear training set S and the mangrove forest ecological health evaluation grade judgment table.
The effects of the present invention are undoubted. The unsupervised deep feature learning is used for preprocessing the mangrove forest ecological big data, a deep neural network consisting of a plurality of layers of noise reduction automatic decoders is used for processing in an unsupervised mode, and the deep structure and the law in the mangrove forest ecological big data are obtained, so that the formed data representation is easier to understand by a deep learning algorithm of a prediction model, and the prediction accuracy of a reverse decision algorithm is obviously improved. Meanwhile, a reverse decision network model is established, and data information of plankton, phytoplankton, intertidal organisms and mangrove communities in the next time period is accurately predicted through water environment, deposition environment and pest data measured by current time period sensing and images. Thereby obtaining the correlation between the indexes which can be collected by the sensing and image technology and the important indexes which can not be collected. The invention utilizes the predicted important ecological indexes and combines the mangrove forest ecological health evaluation grade judgment table to finally realize the aim of predicting the mangrove forest ecological health condition.
Drawings
FIG. 1 is a general flow chart of the mangrove forest ecological health status prediction technology;
FIG. 2 is a block diagram of a reverse decision algorithm;
FIG. 3 is a comparison of predicted data and actual data;
FIG. 4 is a comparison graph of the result of ecological index prediction using the kernel technique algorithm.
Detailed Description
The present invention is further illustrated by the following examples, but it should not be construed that the scope of the above-described subject matter is limited to the following examples. Various substitutions and alterations can be made without departing from the technical idea of the invention and the scope of the invention is covered by the present invention according to the common technical knowledge and the conventional means in the field.
Example 1:
referring to fig. 1 to 4, a mangrove forest ecological health evaluation method based on a stacking noise reduction automatic coding algorithm mainly comprises the following steps:
1) and determining the mangrove forest ecological protection area. In this example, the ecological protection area of the northern river estuary is selected.
2) And establishing a mangrove forest ecological index data set.
And taking mangrove forest ecological index data information I acquired by the mangrove forest ecological protection area in the last 7 years by using a sensing and image technology as a data set X. The mangrove forest ecological index data information I mainly comprises water quality information, sediment pH, soil pH and soil granularity indexes.
The water quality information mainly comprises water temperature, salinity, pH, chlorophyll, ammonia nitrogen, nitrate, nitrite, inorganic phosphorus, petroleum and chemical oxygen demand.
The indexes which can most directly reflect the ecological health condition of the mangrove such as fish, shrimp, microorganism and algae, COD, BOD and other trace metal atoms, mangrove insect pests, community types, benthonic animals, phytoplankton and intertidal zone organisms, lepidoptera and coleoptera information are difficult to acquire through sensing and image technology, and must be manually acquired for analysis, thus being time-consuming and labor-consuming. Therefore, it is necessary to establish a model to obtain the correlation between the indexes which can be acquired by sensing and image technology and the important indexes which cannot be acquired.
The deposits comprise mainly organic carbon and sulphides.
In the formula (I), the compound is shown in the specification,
is mangrove forest ecological index data information I. Data with the same attribute is specified as a column in the training set, and different samples are specified as a row in the training set.
And taking mangrove forest ecological index data information II manually collected in the ecological protection area in the last 7 years as a data set Y. The mangrove forest ecological index data information II mainly comprises fish information, shrimp information, microorganism information, algae information, Chemical Oxygen Demand (COD) information, Biochemical Oxygen Demand (BOD) information, mangrove forest pest information, community type, benthonic animal information, phytoplankton information, intertidal zone biological information, lepidoptera information and coleoptera information.
In the formula (I), the compound is shown in the specification,
is mangrove forest ecological index data information II. Data with the same attribute is specified as a column in the training set, and different samples are specified as a row in the training set. Y isi∈RL。
3) Data set T and data set Y are preprocessed. The preprocessing mainly comprises denoising and normalization. The unit of the column information related to the data having a request for the unit is unified, and m represents the dimension after the unit is unified. Taking the normalized data set X as the data set R1。
Standardize min-max:
one column of the data set X, max and min being the maximum and minimum values in the column, XiFor the normalized data column information, each column in the data set X is normalized respectively, so that the data of each column is mapped to [0,1]Within, the normalization is finished to obtain a data set R1。
4) The data in data set X is trained using a stacked noise reduction auto-encoding algorithm.
The main steps for training the data in the data set X are as follows:
4.1) determining the network structure of the stacking noise reduction automatic coding algorithm. And setting the network to have M layers, wherein the number of input layers is 1, the number of transition layers is M-2, and the number of output layers is 1.
4.2) data set R1As an input layer to the network.
4.3) setting the initial weight H of the transition layer. Training the transition layer by adopting a noise reduction automatic coding algorithm to obtain a training result H1。
H1＝RW。 (4)
Output set H of transition layer1Reconstructing an and R by decoding1The same size specification signal F. Decoding the expression:
f and R1The reconstruction error between is as follows:
4.4) training results H1As the input layer for the next layer. Repeating the step 3 to obtain a training result H2。
4.5) repeating the step 3 and the step 4M-2 times to finally obtain the output result of the M layer, namely the output layer. And recording the output result as a data set V, and finishing the training.
5) And establishing a mangrove forest ecological index prediction model.
The method mainly comprises the following steps of:
5.1) designing an inverse decision algorithm with the number of layers p. The total number of layers of the reverse decision algorithm is the number of transition layers plus the number of output layers. The main idea of the reverse decision algorithm is to divide the learning process into two stages:
the first stage is the forward propagation process of the information flow. When input information propagates and is processed in the path of "input layer → transition layer → output layer", the actual output value of each layer is calculated.
The second stage is the back propagation process of the error. When the desired output value is not obtained at the output layer, the difference (i.e., error) between the actual output and the desired output propagates in the path "output layer → hidden layer → input layer". Specifically, the error is distributed to each layer, so as to obtain error signals of each layer, and the error signals are used as the basis for correcting each connection weight. The repeated application of these two processes ultimately minimizes the error.
The reverse decision algorithm mainly comprises an input layer, a transition layer and an output layer. The number of the input layers is 1. The number of the transition layers is p-1. The number of the output layers is 1.
And taking a linear training set S and a data set Y as a training set D of the mangrove forest ecological index prediction model. Training set D is as follows:
D＝{(V1，Y1)，(V2，Y2)……(Vm，Ym)}。 (7)
in the formula, ViIs the ith row of the linear training set V. Y isiIs row i of data set Y.
5.2) inputting the data set V as a characteristic.
5.3) note the weight between the ith and jth layers as Wij. Bias of j-th layer is thetaj。
Wherein, Wij∈[-1,0]。θj∈[-1,0]。
5.4) input I of the j-th layerjAs follows:
in the formula, WijIs the weight between the ith and jth layers. ThetajIs the bias of the j-th layer. O isiIs the output of the ith layer. i is the level number. j is the level number.
Output of j-th layer OjAs follows:
in the formula IjIs the input to the j-th layer. j is the level number.
Input I through the j-th layerjAnd output of j-th layer OjForward transmission to obtain the training result of the first layer as S1。
5.5) adding S1As an input layer for the next layer. Defining an error function Err1j. Error function Err1jAs follows:
in the formula, WjkIs the weight between the j-th and k-th layers. O isjIs the output of the j-th layer. j is the level number. And k is a layer sequence number.
Weight change amount Δ WijAs follows:
ΔWij＝(l)ErrjOi。 (11)
in the formula, OiIs the output of the ith layer. i is the level number.
Offset change deltajAs follows:
Δθj＝(l)Errj。 (12)
in the formula, j is a hierarchical sequence number. (l) Is a learning coefficient. Err1jIs an error function.
Repeating the steps 3 and 4 to obtain the layer 2Training result S of2。
5.6) repeating the steps 3 to 5p-1 times to finally obtain the p layer, namely the output result Y of the output layer1。
5.7) for the output layer, an error function Err2 is definedj。
Err2j＝Oj(I-Oj)(1-Oj)。 (13)
In the formula, OjIs the output of the j-th layer. j is the level number.
In the negative gradient direction, with a minimum Err2jAnd minimum ErriOn the basis of the values, the weight W is adjusted using the formula (14) and the formula (15)ijAnd an offset thetaj. Passing minimum Err2jCalculating to obtain a weight WijAnd then updating the weight.
W′ij＝Wij+ΔWij。 (14)
In the formula, WijIs the weight between the ith and jth layers. Δ WijIs the amount of weight change. W'ijTo adjust the weight between the ith layer and the jth layer.
θ′j＝θj+Δθj。 (15)
In the formula, thetajIs the bias of the j-th layer. Delta thetajIs the amount of change in the bias. Theta'jTo adjust the bias of the j-th layer after the adjustment.
5.8) judging the weight W 'between the ith layer and the jth layer after adjustment'ijWhether or not it is below a threshold value epsilon1. Determining offset theta 'between the ith and jth layers after adjustment'jWhether or not it is below a threshold value epsilon2。
5.9) if W'ij≥ε1Then W'ijValue of as WijValue, repeat equation (14) to recover the adjusted weight W 'between the ith and jth layers'ijAnd step 5.8 is repeated.
If theta'j≥ε2Then theta 'will be'jValue of (2) is θ'jValue, repeating equation (15) to recover the offset θ 'between the ith and jth layers after adjustment'jAnd repeating step 5.8。
If W'ij＜ε1And θ'j＜ε2And if the result is true, finishing training to obtain the mangrove forest ecological index prediction model.
Part of mangrove forest ecological index data information acquired by northern river estuary ecological protection area sensing and image technology in July 2016 is used as a test set T, the output of the model is compared with relevant parameters acquired manually in August, and the accuracy of prediction by adopting a reverse decision algorithm is higher as can be seen from the prediction result, so that the model is verified. As shown in fig. 3 and 4. Meanwhile, fig. 4 also illustrates that the prediction accuracy of the reverse decision algorithm is significantly improved by utilizing the kernel technique algorithm to perform linearization processing.
6) And predicting the future ecological health condition of the mangrove forest in the mangrove forest ecological protection area by utilizing the mangrove forest ecological index prediction model, the linear training set S and the mangrove forest ecological health evaluation grade judgment table. The mangrove forest ecological health evaluation grade judgment table grades the ecological health of the mangrove forest, for example, the health is 1 grade, the sub-health is 2 grade, the unhealthy is 3 grade, and the critical is 4 grade.
According to the invention, an accurate mangrove forest ecological index prediction model is constructed through a reverse decision algorithm, a kernel skill algorithm is added in front of the prediction model to process mangrove forest ecological big data, so that the finally constructed data representation is easier to understand by a deep learning algorithm of the ecological prediction model, and the prediction accuracy of the reverse decision prediction model is obviously improved.
Meanwhile, the mangrove forest ecological index prediction model can accurately predict the data information of plankton, phytoplankton, intertidal zone organisms and mangrove forest communities in the next time period through the water environment, deposition environment and pest data measured by sensing and image in the current time period, thereby obtaining the correlation between the indexes which can be acquired by sensing and image technology and the important indexes which can not be acquired. And finally, the predicted important ecological indexes are utilized, and the target of predicting the ecological health condition of the mangrove forest is realized by combining the mangrove forest ecological health evaluation grade judgment table.
Claims (4)
1. A mangrove forest ecological health evaluation method based on a stacking noise reduction automatic coding algorithm is characterized by mainly comprising the following steps:
1) determining an ecological protection area of the mangrove forest;
2) establishing a mangrove forest ecological index data set;
taking mangrove forest ecological index data information I acquired by the mangrove forest ecological protection area in eta years by using a sensing and image technology as a data set X;
mangrove forest ecological index data information II acquired manually in the ecological protection area within eta years is used as a data set Y;
3) preprocessing a data set X and a data set Y; the preprocessing mainly comprises denoising and normalization; recording the data set after X normalization as R1；
4) Training data in the data set X by using a stacking noise reduction automatic coding algorithm to obtain a data set V;
5) establishing a mangrove forest ecological index prediction model;
the method mainly comprises the following steps of:
5.1) designing a reverse decision algorithm with the number of layers p; the reverse decision algorithm mainly comprises an input layer, a transition layer and an output layer; the number of the input layers is 1; the number of the transition layers is p-1; the number of the output layers is 1;
taking a data set V and a data set Y as a training set D of the mangrove forest ecological index prediction model; training set D is as follows:
D＝{(V1，Y1)，(V2，Y2)……(Vm，Ym)}； (1)
in the formula, ViRow i of the data set V; y isiRow i of data set Y;
5.2) inputting the data set V as a characteristic;
5.3) note the weight between the ith and jth layers as Wij(ii) a Bias of j-th layer is thetaj；
Wherein, Wij∈[-1,0]；θj∈[-1,0]；
5.4) input I of the j-th layerjAs follows:
in the formula, WijIs the weight between the ith layer and the jth layer; thetajIs the bias of the j-th layer; o isiIs the output of the ith layer; i is a level sequence number; j is a hierarchical sequence number;
output of j-th layer OjAs follows:
in the formula IjIs the input of the j-th layer; j is a hierarchical sequence number;
input I through the j-th layerjAnd output of j-th layer OjForward transmission to obtain the training result of the first layer as S1；
5.5) adding S1An input layer as a next layer; defining an error function Err1j(ii) a Error function Err1jAs follows:
in the formula, WjkIs the weight between the j-th layer and the k-th layer; o isjIs the output of the j-th layer; j is a hierarchical sequence number; k is a hierarchical sequence number;
weight change amount Δ WijAs follows:
ΔWij＝(l)Err1jOi； (5)
in the formula, OiIs the output of the ith layer; i is a level sequence number; (l) Is a learning coefficient; err1jIs an error function;
offset change deltajAs follows:
Δθj＝(l)Err1j； (6)
in the formula, j is a level serial number; (l) Is a learning coefficient; err1jIs an error function;
repeating the step 5.3) and the step 5.4) to obtain a training result S of the layer 22；
5.6) repeating the step 5.3) to the step 5.5) for p-1 times to finally obtain the output result Y of the p-th layer, namely the output layer1；
5.7) for the output layer, an error function Err2 is definedj；
Err2j＝Oj(I-Oj)(1-Oj)； (7)
In the formula, OjIs the output of the j-th layer; j is a hierarchical sequence number;
in the negative gradient direction, with a minimum Err2jAnd a minimum of thetajOn the basis of the values, the weight W is adjusted using the formula (8) and the formula (9)ijAnd an offset thetaj；
W′ij＝Wij+ΔWij； (8)
In the formula, WijIs the weight between the ith layer and the jth layer; Δ WijIs the weight change amount; w'ijThe weight between the ith layer and the jth layer after adjustment;
θ′j＝θj+Δθj； (9)
in the formula, thetajIs the bias of the j-th layer; delta thetajIs a bias change amount; theta'jIs the bias of the adjusted j-th layer;
5.8) judging the weight W 'between the ith layer and the jth layer after adjustment'ijWhether or not it is below a threshold value epsilon1(ii) a Determining offset theta 'between the ith and jth layers after adjustment'jWhether or not it is below a threshold value epsilon2；
5.9) if W'ij≥ε1Then W'ijValue of as WijValue, repeating the formula (8), and recovering the weight W 'between the ith layer and the jth layer after adjustment'ijAnd repeating step 5.8);
if theta'j≥ε2Then theta 'will be'jValue of as θjValue, repetitionEquation (9), retrieve offset θ 'between the i-th and j-th layers after adjustment'jAnd repeating step 5.8);
if W'ij＜ε1And θ'j＜ε2If the mangrove forest ecological index is established, training is finished to obtain a mangrove forest ecological index prediction model;
6) and predicting the future ecological health condition of the mangrove forest in the mangrove forest ecological protection area by utilizing the mangrove forest ecological index prediction model, the data set V and a mangrove forest ecological health evaluation grade judgment table.
2. The mangrove forest ecological health assessment method based on the stacking noise reduction automatic coding algorithm according to claim 1, characterized in that the data set X is as follows:
in the formula (I), the compound is shown in the specification,
obtaining mangrove forest ecological index data information I;
data set X is shown below:
in the formula (I), the compound is shown in the specification,
is mangrove forest ecological index data information II.
3. The mangrove forest ecological health assessment method based on the stacking noise reduction automatic coding algorithm according to claim 1, characterized in that: the mangrove forest ecological index data information I mainly comprises water quality information, sediment pH, soil pH and soil granularity indexes;
the water quality information mainly comprises water temperature, salinity, pH, chlorophyll, ammonia nitrogen, nitrate, nitrite, inorganic phosphorus, petroleum and chemical oxygen demand;
the deposit comprises primarily organic carbon and sulfides;
the mangrove forest ecological index data information II mainly comprises fish information, shrimp information, microorganism information, algae information, Chemical Oxygen Demand (COD) information, Biochemical Oxygen Demand (BOD) information, mangrove forest pest information, community type, benthonic animal information, phytoplankton information, intertidal zone biological information, lepidoptera information and coleoptera information.
4. The mangrove forest ecological health assessment method based on the stacking noise reduction automatic coding algorithm according to claim 1, characterized in that the main steps of training the data in the data set X are as follows:
1) determining a network structure of a stacking denoising automatic coding algorithm; setting the network to have M layers, wherein the number of input layers is 1, the number of transition layers is M-2, and the number of output layers is 1;
2) data set R1As an input layer of the network;
3) setting an initial weight of the transition layer; training the transition layer by adopting a noise reduction automatic coding algorithm to obtain a training result H1；
4) Will train the result H1An input layer as a next layer; repeating the step 3) to obtain a training result H2；
5) Repeating the step 3) and the step 4) for M-2 times to finally obtain an output result of the Mth layer, namely the output layer; and recording the output result as a data set V, and finishing the training.
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