Riverway side slope ecological restoration method

Abstract

The application discloses a river channel side slope ecological restoration method, comprising the following steps: obtaining a river channel side slope ecological restoration scheme, encoding the river channel side slope ecological restoration scheme to obtain a side slope matrix, training a deep learning model through the side slope matrix to obtain a trained decoder, evaluating the river channel side slope ecological restoration scheme based on a current river channel side slope through an expert scoring method to obtain an evaluation score, optimizing an evaluation model according to the side slope matrix and the evaluation score to obtain an optimized evaluation model, iteratively generating an initial side slope matrix through the trained decoder, evaluating the iteratively generated initial side slope matrix through the optimized evaluation model, obtaining an optimal side slope matrix according to an evaluation result, decoding the optimal side slope matrix to obtain an optimal ecological restoration scheme of the current river channel side slope, and setting a scheme of the current river channel side slope according to the optimal river channel side slope ecological restoration scheme to realize ecological restoration.

Description

Technical Field

[0001] This invention belongs to the field of slope restoration technology, and in particular relates to a method for ecological restoration of riverbank slopes. Background Technology

[0002] Riverbank ecological restoration is a comprehensive technical approach aimed at restoring and improving the ecological environment of riverbanks, enhancing their stability and erosion resistance. However, scientific riverbank ecological restoration requires specialized personnel to conduct statistical calculations and design relevant plans tailored to local conditions. Existing methods often involve simply copying fixed ecological restoration schemes or directly employing single solutions for slope reinforcement, failing to provide a scientific approach to ecological restoration and neglecting to consider multiple aspects of the riverbank's condition. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes a method for ecological restoration of riverbank slopes, thereby resolving the issues present in the existing technologies.

[0004] To achieve the above objectives, the present invention provides a method for ecological restoration of riverbank slopes, comprising:

[0005] Obtain a riverbank slope ecological restoration plan, encode the riverbank slope ecological restoration plan, and obtain a slope matrix;

[0006] A deep learning model is constructed and trained using a slope matrix to obtain a trained decoder. The deep learning model is a generative network with an encoder-decoder structure.

[0007] An evaluation model was constructed, and an expert scoring method was used to evaluate the ecological restoration plan of the river slope based on the current river slope to obtain an evaluation score. The evaluation model was then optimized based on the slope matrix and the evaluation score to obtain the optimized evaluation model.

[0008] The initial slope matrix is ​​generated iteratively by a trained decoder. The initial slope matrix is ​​evaluated by an optimized evaluation model. Based on the evaluation results, the optimal slope matrix is ​​obtained.

[0009] The optimal slope matrix is ​​decoded to obtain the optimal ecological restoration scheme for the current river slope. Based on the optimal ecological restoration scheme, the current river slope is set up to achieve ecological restoration.

[0010] Optionally, the process of coding the riverbank ecological restoration scheme includes:

[0011] Obtain the materials and installation locations used in the aforementioned riverbank slope ecological restoration plan;

[0012] The initial coding sequence and the numbering relationship between materials and labels are constructed. The initial coding sequence has corresponding coding positions according to different material types. The initial coding sequence is filled in according to the material type and corresponding label to generate coding results. An initial slope matrix is ​​constructed. The coding results are filled into the initial slope matrix according to the setting position of the materials in the river slope scheme to obtain the slope matrix.

[0013] Optionally, the deep learning model employs a variational autoencoder, which uses an encoder-decoder structure. The slope matrix serves as the input to the encoder, and the output of the decoder is the slope matrix. There is a latent space between the encoder and the decoder, which contains latent variables. The input space, latent space, and output space all belong to the integer space.

[0014] Optionally, the encoder structure includes, in sequence: four sequentially connected convolutional modules, a Flatten layer, a Linear layer, an LReLU layer, and another Linear layer, wherein the convolutional module includes a Conv layer, a BN layer, and an LReLU layer connected in sequence.

[0015] Optionally, the decoding structure includes an Unflatten layer, a deconvolution layer, a BN layer, an LReLU layer, two sequentially connected upsampling modules, a convolution module, a convolutional layer, and an output layer, wherein the upsampling module includes a sequentially connected Upsample layer, a Conv layer, a BN layer, and an LReLU layer.

[0016] Optionally, the evaluation model employs a convolutional neural network.

[0017] Optionally, the structure of a convolutional neural network includes: an input layer, three convolutional layers, two fully connected layers, and an output layer.

[0018] Optionally, the evaluation process for riverbank ecological restoration schemes includes:

[0019]

[0020] Among them, J p J represents the final score given by experts for the repair plan in the p-th segment. pq J represents the score given by the qth expert for the p-th riverbank slope ecological restoration scheme, where p represents the label of different indicators, q represents the label of different experts, n represents the total number of experts, and J represents the score of the qth expert. o,pq w represents the score of the o-th indicator among the q-th experts for the p-th type of riverbank slope ecological restoration scheme. oThe weight of indicator o is set based on human experience. o represents the indicator number and m represents the number of indicators. The indicators include ecological restoration capacity, defense capacity, appearance score, cost score, construction difficulty, and scheme risk. Ecological restoration capacity includes the ability to restore soil erosion, vegetation destruction, and soil erosion in the current riverbank ecology.

[0021] Optionally, the process of obtaining the optimal slope matrix includes:

[0022] A population is constructed and initialized based on the input of the trained decoder, wherein the population includes several individuals, and the individuals include different latent parameters; an objective function, fitness, and population update method are set, wherein the objective function is to maximize the evaluation score of the slope matrix generated by the trained decoder; based on the objective function, fitness, and population update method, an optimization algorithm is used to find the optimal slope matrix.

[0023] Optionally, the optimization algorithm employs an improved moth-to-a-flame algorithm.

[0024] Compared with the prior art, the present invention has the following advantages and technical effects:

[0025] Through the above technical solution, this invention statistically analyzes existing riverbank slope ecological restoration schemes and trains a deep learning model based on the statistical results. This enables the deep learning model to learn and regenerate new riverbank slope ecological restoration schemes. Furthermore, by scoring these new schemes and iteratively optimizing them, the optimal riverbank slope ecological restoration scheme, which is not a fixed solution, can be effectively found. This invention can find or predict the optimal riverbank slope ecological restoration scheme, thereby improving its effectiveness. The scoring process considers multiple indicators to ensure the scheme is optimal under each indicator. Based on this, it can effectively guide the actual work of riverbank slope ecological restoration and has strong practicality. Attached Figure Description

[0026] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0027] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention. Detailed Implementation

[0028] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0029] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0030] like Figure 1 As shown, this embodiment provides a method for ecological restoration of riverbank slopes, including:

[0031] A riverbank ecological restoration plan is obtained, and the plan is encoded into a matrix to obtain a slope matrix. The slope matrix is ​​continuously transformed using a deep learning model, and the transformed plan matrix is ​​evaluated by score. Based on the evaluation results, the optimal plan matrix is ​​obtained. The optimal plan matrix is ​​then decoded to obtain the optimal riverbank ecological restoration plan. The riverbank is then configured according to the optimal plan to achieve ecological restoration.

[0032] As some embodiments, the riverbank ecological restoration scheme selects as many existing riverbank ecological restoration schemes under different environments as possible. The riverbank ecological restoration scheme is a restoration system setting scheme that includes functional layers of different materials and the location of material placement. For example, it includes different riverbank ecological restoration system setting schemes such as clay layer, sand and gravel layer, soil layer, organic matter layer and vegetation planting layer. In the above content, different functional layers are coded. First, the main material types used in the functional layers are determined. The material types include three types: vegetation materials, natural materials and prefabricated materials. Vegetation materials include different types and varieties of vegetation. Natural materials include the above-mentioned clay, sand and gravel, soil, fertilizer, organic matter, etc. of different textures. Prefabricated materials include different types of concrete blocks, cells and other industrial products used for slope ecological restoration or slope fixation. During the coding process, an initial coding sequence is constructed, based on a six-digit number. Each two-digit number corresponds to a type of vegetation material, natural material, or precast material. Materials within each type (those with the largest proportion within that type) are numbered. For example, turf in vegetation materials is numbered 01, a certain type of soil in natural materials is numbered 20, and a certain type of concrete block in precast materials is numbered 09, resulting in a final number of 012009. The slope is divided into different grids according to region, and the main materials within each grid are statistically analyzed. An initial matrix is ​​constructed based on the grid's location, where the number of rows and columns in the initial matrix corresponds to the number of rows and columns in the grid. The elements of the slope matrix are then assigned values ​​based on the final codes corresponding to the main materials in each grid to generate the slope matrix. Each slope matrix corresponds to a specific riverbank slope ecological restoration scheme.

[0033] As some embodiments, existing ecological restoration schemes for riverbank slopes are collected from existing ecological restoration schemes or data from relevant databases or institutions. Based on the above-mentioned initial matrix method, different ecological restoration schemes are encoded to generate a historical slope matrix. After encoding, the encoded matrices of the above-mentioned ecological restoration schemes are integrated into a dataset to generate a historical slope dataset. The historical slope dataset is used as a training set and a test set for training and testing subsequent deep learning models.

[0034] As one embodiment, the deep learning model used in this embodiment is a variational autoencoder, which adopts an encoder-decoder structure. To facilitate the use of the deep learning model, a two-dimensional deep learning model is used. The aforementioned slope matrix is ​​used as the input to the encoder. A latent space exists between the encoder and decoder, containing latent variables. The input space, latent space, and output space are all integer spaces. The encoder outputs latent variables and their distribution parameters. Simultaneously, the latent variables are used as input to the decoder, enabling the decoder to output the corresponding slope matrix. The encoder represents the mapping relationship between the slope matrix and the latent variables, mapping the slope matrix to latent variables. The decoder represents the correlation mapping from latent variables to the slope matrix. Through training, the decoder learns this mapping relationship and, when used alone, can generate different and more slope matrices using latent variables different from those corresponding to existing slope matrices. The latent distribution of latent variables in the latent space is common knowledge in the art. The structure of the variational autoencoder can be set using conventional structures in the art, and will not be elaborated here.

[0035] As some embodiments, the variational autoencoder used in this invention differs from the conventional convolutional neural network architecture. The encoder structure sequentially includes: four convolutional modules connected in sequence, a Flatten layer, a Linear layer, an LReLU layer, and another Linear layer. Each convolutional module includes a Conv layer, a BN layer, and an LReLU layer connected in sequence. The decoding structure includes an Unflatten layer, a deconvolutional (Deconv) layer, a BN layer, an LReLU layer, two upsampling modules connected in sequence, a convolutional module, a convolutional layer, and an output layer. Each upsampling module includes an Upsample layer, a Conv layer, a BN layer, and an LReLU layer connected in sequence. In the above, the Conv layer is a two-dimensional convolutional layer; the Deconv layer is a two-dimensional deconvolutional layer; the Linear layer is a fully connected layer; the BN layer is a two-dimensional batch normalization layer; the LReLU layer indicates the use of the LeakyReLU activation function; Upsample indicates a two-dimensional bilinear interpolation upsampling operation; Flatten indicates unfolding tensor data into a vector; Unflatten is the reverse process of the above operation; and the output layer uses the tanh function. The model parameters mentioned above can be determined according to actual needs, and will not be elaborated here.

[0036] As one embodiment, the aforementioned deep learning model is trained using a historical slope dataset. The training set is used to train the variational autoencoder, i.e., the deep learning model, which is trained using the Adam optimizer. The loss function for training the VAE model is expressed as follows:

[0037] min L=λ1NMSE(x)+λ2KL(x)

[0038]

[0039] In the formula, NMSE is the normalized root mean square error, used to measure the difference between the cross-section matrix generated by the decoder (i.e., the reconstructed sample x) and the original cross-section matrix (i.e., the original sample x0); KL represents the Kullback-Leibler divergence, which serves as a regularization term in the loss function to ensure the generative capability of the VAE model. λ1 and λ2 represent the corresponding term weight coefficients, and μ and σ represent the mean and standard deviation of each component of the latent variable in the latent space, respectively. This represents the expected result of processing the encoder input data. This represents the expected result of the operation on the encoder output data.

[0040] As one embodiment, after training, the deep learning model uses a decoder to generate a slope matrix. This involves randomly selecting latent variables in the latent space and using these variables to generate the corresponding slope matrix through the decoder. The generated slope matrix conforms to the patterns of existing schemes, but does not represent a specific solution provided by those schemes. The latent variables are optimized by maximizing the score of the slope matrix generated by the decoder, resulting in a final slope matrix that serves as a redesigned or currently designed slope solution. In this embodiment, the decoder can store the relationship between latent variables and the slope matrix. The slope matrix ultimately generated by the decoder can simulate the ability to generate existing slope matrices, representing a non-implementable riverbank slope ecological restoration scheme that conforms to actual setting patterns, providing a relevant data foundation for subsequent optimization.

[0041] The expert scoring method is used to evaluate the current riverbank slope environment. Based on this environment, existing riverbank slope ecological restoration plans corresponding to the current slope matrix are scored. In other words, the scores are assessed on the impact of these plans on the current riverbank slope environment. The final score is determined through a weighted sum.

[0042]

[0043] Among them, J p J represents the final score given by experts for the repair plan in the p-th segment. pq J represents the score given by the qth expert for the p-th riverbank slope ecological restoration scheme, where p represents the label of different indicators, q represents the label of different experts, n represents the total number of experts, and J represents the score of the qth expert. o,pq w represents the score of the o-th indicator among the q-th experts for the p-th type of riverbank slope ecological restoration scheme. o The weight of indicator o is set based on human experience. o represents the indicator number and m represents the number of indicators. The indicators include ecological restoration capacity (such as the ability to restore soil erosion, vegetation damage, and soil erosion in the current riverbank ecology), defense and reinforcement capacity, appearance score, cost score, construction difficulty, and scheme risk. The scoring is set according to experts.

[0044] It should be noted that this evaluation model is constructed for a single environment, not a specific area. For environments similar to the current riverbank slope (such as slope, height, soil type, and ecological issues), the pre-trained evaluation model can be used directly. As an extension, corresponding evaluation models can be pre-built and stored for different environmental categories as preparation for the technical solution, rather than a real-time process. This allows different riverbank slope environments to be categorized into different environmental types, and the evaluation models can be directly extracted and used later.

[0045] A simple convolutional neural network (CNN) is used to fit the correlation between the above-mentioned solutions and scores. The existing repair solutions and corresponding expert scores are used as the fitting or training data for the CNN. This trains the CNN to construct a scoring model, i.e., an evaluation model. The input of the CNN is the slope matrix, and the output is the corresponding score. The CNN structure is exemplarily an input layer, three convolutional layers, two fully connected layers, and an output layer. The functions used in each layer are common knowledge in the field and will not be elaborated here. Using the scores from the CNN as the evaluation basis, the main optimization objective during the optimization process is to maximize the score of the repair solution generated by the encoder. This is the basis for optimization.

[0046] As some embodiments, in the process of finding the optimal solution, an optimization algorithm is combined with the aforementioned deep learning model and evaluation model. In this embodiment, an improved moth-to-a-flame algorithm is used as the optimization algorithm to implement the above content. This embodiment mainly focuses on finding the slope matrix, or the ecological restoration scheme for riverbank slopes. The relevant content of the optimization algorithm includes:

[0047] An initial population is generated using latent parameters. This population contains different individuals, each with different latent parameters. The data encoded by the slope matrix represented by these latent parameters can be used to generate different slope matrices by adjusting and decoding the latent parameters. The objective is to maximize the score of the ecological restoration scheme corresponding to the slope matrix. The objective function is:

[0048] f(x) = maxJ(B(x)) 2 =maxJ 2

[0049] Where J represents the score of the slope matrix corresponding to the scheme. J() represents the output score of the convolutional neural network, and B(x) represents the slope matrix generated by processing the implicit function x through the decoder. The population update method and iteration stopping condition (satisfying the maximum number of iterations) are set, and after setting the above, the pollution source is optimized.

[0050] As some embodiments, the specific steps of the optimization algorithm are as follows:

[0051] 1) Initialize the population, which contains several different individuals or moth locations. Each individual contains a value of a hidden parameter, and the location is selected as the initial individual based on human experience or by random selection.

[0052] Set the relevant objective function, constraints, and corresponding fitness, where the objective function is:

[0053] f(x) = maxJ(B(x)) 2 =maxJ 22

[0054] Where J represents the score of the slope matrix corresponding to the proposed solution. J() represents the output score of the convolutional neural network, and B(x) represents the slope matrix generated by processing the implicit function x through the decoder. The fitness function is J(B(x)). 2 .

[0055] 2) In the improved moth-to-flame algorithm, the moth needs to update and iterate around the flame, where the flame represents a local optimum. The number of flames needs to be determined, and during the iteration process, the number of flames needs to be adaptively reduced. The formula for the adaptive reduction mechanism is as follows:

[0056] FN = round(Nt*(N-1) / T)

[0057] In the formula: N is the initial number of flames; t is the current iteration number; T is the maximum number of iterations; and FN represents the number of flames in the t-th iteration.

[0058] 3) Calculate the fitness of different individuals based on the objective function. The higher the fitness, the better the quality of the individual. During the calculation, the data corresponding to the individual is used as a latent variable and decoded by the decoder to generate the slope matrix. Based on the slope matrix, the corresponding score is generated by the evaluation model.

[0059] The fitness values ​​are sorted from largest to smallest. After sorting, the corresponding number of flames is selected according to the sorting result. Individuals in the fitness ranking are used as flames. The number of flames is calculated according to the above-mentioned fitness reduction mechanism formula. The current best flame is stored. The best flame is the individual with the highest number of flames in the fitness ranking.

[0060] 4) Update according to the individual update method, or the moth position update method. In the update process, first use the initial position update method, in which the above moth position update method is adaptively updated using inertia weights:

[0061] Mi = Di·e bθ ·cos(2πθ)+ω·Fj

[0062] Where b is a constant used to define the shape of the logarithmic spiral; θ is a random number between [-1, 1]; Mi represents the i-th moth (individual); Fj represents the j-th flame; Di represents the distance between the i-th moth and the j-th flame, which is a constant representing the shape of the logarithmic spiral; the formula is: Di = |Fj - Mi|, and ω is the inertia weight. Borrowing the inertia weight idea from the particle swarm optimization algorithm, the larger ω is, the greater the search strength of the algorithm in the global scope; the smaller ω is, the higher the search accuracy of the algorithm in the local scope.

[0063]

[0064] Where t is the current iteration number; T is the maximum iteration number; and a and d are constants.

[0065] 5) After the update, the moth locations will be updated again:

[0066]

[0067] Where MI represents the updated moth individual's label, rand() represents a random number, and Levy represents the corresponding Levy random step size.

[0068]

[0069] Where β is a constant, set to 1.5, and the distribution number μ ~ N(0, σ 2 ), σ 2 Indicates the upper limit; the random distribution number v ~ N(0, 1);

[0070]

[0071] Where Г() represents the standard gamma function.

[0072] 6) Determine if there is only one flame data remaining. If there is only one, output the final result directly.

[0073] 8) Determine if the maximum number of iterations has been reached. If not, repeat steps 2-6 above. If it has been reached, output the optimal moth individual, which is the slope matrix corresponding to the hidden parameter finally found.

[0074] Based on the above encoding rules, the optimal slope matrix is ​​decoded to generate the corresponding optimal riverbank slope ecological restoration scheme.

[0075] Through the above technical solution, this invention statistically analyzes existing riverbank slope ecological restoration schemes and trains a deep learning model based on the statistical results. This enables the deep learning model to learn and regenerate new riverbank slope ecological restoration schemes. Furthermore, by scoring these new schemes and iteratively optimizing them, the optimal riverbank slope ecological restoration scheme can be effectively found. This invention can identify or predict the optimal riverbank slope ecological restoration scheme, thereby improving its effectiveness. The scoring process considers multiple indicators to ensure the scheme is optimal under each indicator. Based on this, it can effectively guide the actual work of riverbank slope ecological restoration and has strong practicality.

[0076] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for ecological restoration of riverbank slopes, characterized in that, include: Obtain a riverbank slope ecological restoration plan, encode the riverbank slope ecological restoration plan, and obtain a slope matrix; The process of coding the riverbank ecological restoration plan includes: Obtain the materials and installation locations used in the aforementioned riverbank slope ecological restoration plan; Construct an initial coding sequence and the numbering relationship between materials and labels. The initial coding sequence has corresponding coding positions set according to different material types. Fill in the initial coding sequence according to the material type and corresponding label to generate coding results and construct an initial slope matrix. Fill in the coding results into the initial slope matrix according to the setting position of the materials in the river slope scheme to obtain the slope matrix. Construct a deep learning model, train the deep learning model using the slope matrix, and obtain a trained decoder; The deep learning model described herein employs a variational autoencoder, which uses an encoder-decoder structure. The slope matrix serves as the input to the encoder, and the output of the decoder is the slope matrix. There is a latent space between the encoder and the decoder, which contains latent variables. The input space, latent space, and output space are all integer spaces. In this process, latent variables are randomly selected and adjusted in the latent space, and the latent variables are used to generate the corresponding slope matrix through the decoder. The generated slope matrix conforms to the pattern of the existing scheme, but the existing scheme does not provide relevant schemes. The latent variables are optimized by maximizing the score of the slope matrix generated by the decoder as the objective, and the final slope matrix is ​​generated as the slope scheme for redesign or the current design. An evaluation model was constructed, and an expert scoring method was used to evaluate the ecological restoration plan for the riverbank slope based on the current riverbank slope, and an evaluation score was obtained. The evaluation model uses a convolutional neural network, and the structure of the convolutional neural network includes: an input layer, three convolutional layers, two fully connected layers, and an output layer. The evaluation model is optimized based on the slope matrix and evaluation scores to obtain the optimized evaluation model. The initial slope matrix is ​​generated iteratively by a trained decoder. The initial slope matrix is ​​evaluated by an optimized evaluation model. Based on the evaluation results, the optimal slope matrix is ​​obtained. The process of obtaining the optimal slope matrix includes: A population is constructed and initialized based on the input of the trained decoder, wherein the population includes several individuals, and the individuals include different latent parameters; an objective function, fitness, and population update method are set, wherein the objective function is to maximize the evaluation score of the slope matrix generated by the trained decoder; based on the objective function, fitness, and population update method, an optimization algorithm is used to find the optimal slope matrix. The optimization algorithm adopts an improved moth-to-flame algorithm; The optimal slope matrix is ​​decoded to obtain the optimal ecological restoration scheme for the current river slope. Based on the optimal ecological restoration scheme, the current river slope is set up to achieve ecological restoration.

2. The method for ecological restoration of riverbank slopes according to claim 1, characterized in that, The encoder structure consists of four sequentially connected convolutional modules, a Flatten layer, a Linear layer, an LReLU layer, and a Linear layer. The convolutional modules include a Conv layer, a BN layer, and an LReLU layer connected in sequence.

3. The method for ecological restoration of riverbank slopes according to claim 1, characterized in that, The decoding structure includes an Unflatten layer, a deconvolution layer, a BN layer, an LReLU layer, two upsampling modules connected in sequence, a convolution module, a convolutional layer, and an output layer. The upsampling module includes an Upsample layer, a Conv layer, a BN layer, and an LReLU layer connected in sequence.

4. The method for ecological restoration of riverbank slopes according to claim 1, characterized in that, The evaluation process for riverbank ecological restoration plans includes: , , where J P J represents the final score given by experts for the p-th repair scheme. pq J represents the score given by the qth expert for the p-th riverbank slope ecological restoration scheme, where p represents the label of different indicators, q represents the label of different experts, n represents the total number of experts, and J represents the score of the qth expert. o , pq w represents the score of the o-th indicator among the q-th experts for the p-th type of riverbank slope ecological restoration scheme. o The weight of indicator o is set based on human experience. o represents the indicator number and m represents the number of indicators. The indicators include ecological restoration capacity, defense capacity, appearance score, cost score, construction difficulty, and scheme risk. Ecological restoration capacity includes the ability to restore soil erosion, vegetation destruction, and soil erosion in the current riverbank ecology.

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