[Retracted] Artificial Neural Network-Based Planting Arrangement of Smart City in Green Ecological Environment

Meng, Qingsong; Wang, Shufeng

doi:https://doi.org/10.1155/2022/3607545

Mathematical Problems in Engineering

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Volume 2022 | Article ID 3607545 | https://doi.org/10.1155/2022/3607545

[Retracted] Artificial Neural Network-Based Planting Arrangement of Smart City in Green Ecological Environment

Qingsong Meng¹and Shufeng Wang¹

Academic Editor: Dost Muhammad Khan

Received10 Jan 2022

Revised25 Feb 2022

Accepted01 Mar 2022

Published22 Apr 2022

Abstract

The traditional urban planting arrangement is largely limited by the designer’s idea and has a high repetition rate and a low reference reuse rate. Therefore, a scientific and reasonable planting arrangement of the urban environment is necessary. In this work, research on planting arrangements in a smart city is carried out under green ecology and environment. Firstly, the planting arrangement is analyzed based on the structure, characteristics, and basic principles of the artificial neural network (ANN) model. ANN is frequently applied in pattern recognition, signal processing, system identification, and optimization. In the field of control, neural networks are used to deal with the nonlinearity and uncertainty of the control system and to approximate the identification function of the system. Secondly, the output value of the planting arrangement in the smart city is calculated according to the error backpropagation algorithm. During this period, the weight is adjusted according to the Hebb criterion, and the relevant statistical model of planting arrangement in the smart city is analyzed by ANN. Finally, suggestions on planting arrangements are given. The research shows that steamed bun-shaped plants have the largest total number in smart cities, followed by spherical and bush-like plants. Planting arrangement for spherical and palm or coconut-form plants is more frequent while planting arrangements for wind-shaped plants have a lower frequency. In terms of the importance of the planting arrangement, these 18 types of plants are very important for the green ecological environment in the smart city. Finally, suggestions on planting arrangements are given according to the research.

1. Introduction

As big data, artificial intelligence (AI), cloud computing, Internet of things (IoT), and other technologies become increasingly mature, smart city, for which smart city technology emerges and begins the second evolution [1]. With the development of modern cities in recent years, urban plant arrangement has important reference significance in evaluating the intelligent development of cities [2]. The most important part of the urban landscape is its visual effect, which requires cleanliness, beauty, fluency, and coordination of style, color, and shape [3]. In modern urban construction, while following some basic principles, new design concepts should also be introduced in the plant arrangement of street green space [4, 5]. The landscape formed by successful plant arrangement can become a symbol of a city, and its function is just like the prominent architecture or sculpture in the city, which can record the history of a region and spread the culture of a city [6–8]. There are many problems in our country’s landscaping and planting arrangement. Many places have an excess pursuit of planting big trees and lawns and ignore the environmental benefits of small trees and shrubs. Worse still, there is a lack of professional management. As a result, the urban environment theme is monotonous, and the planting arrangement is short of varieties [9–12]. Almalki et al. [13] have studied green IoT for eco-friendly and sustainable smart cities and have also discussed future directions and opportunities. This research studied the tools and methods to improve the quality of life by making the cities smarter and more sustainable. The authors also highlighted the green IoT for more efficient utilization, creating a more sustainable and reducing energy consumption, pollution, and e-waste.

The traditional urban planting arrangement is largely limited by the personal concept of designers, which leads to a high repetition rate and low reference reuse rate. Artificial neural network (ANN) (neural network (NN) for short) is a simulation of the nervous system, including many features of the brain nervous system, which can process massive data and learn from it [14]. As early as 1943, psychologist w. McCulloch and mathematician w. Pitts collaborated to propose the first neural network model McCulloch-Pitts (MCP), which could perform logical operations similar to and, or, and not [15, 16]. In 1957, f. Rosenblat proposed the perceptron model, which is a feed-forward ANN composed of linear threshold neurons that can realize “and or not” logic gates for simple classification [17, 18]. In 1985, Rumelhart et al. represented the backpropagation algorithm for weight training of multilayer perceptrons [19]. Around 2010, as algorithms improved, computing speed increased, and big data emerged, deep learning made remarkable achievements in natural language processing (NLP), computer vision, speech recognition chess (AlphaGo, Master, AlphaGo Zero), automatic driving, and other application fields, and ANN has set off the third upsurge so far [20–22].

This work focuses on the planting arrangement of the smart city under the green ecology and environment. Firstly, the planting arrangement analysis is carried out based on the structure, nature, and basic principles of the ANN model. Secondly, the output value of the planting arrangement of a smart city is calculated according to the error backpropagation algorithm. During this period, the weight is adjusted according to the Hebb criterion, and the statistical model related to the planting arrangement of the smart city is analyzed by ANN. Finally, suggestions on planting arrangements are given. The research is of reference significance to the planting arrangement of smart cities under a green ecological environment.

2. Materials and Methods

2.1. ANN Theory

ANN, or NN for short, is formed by referring to the working principle of the biological neural network [23]. The model is characterized by parallel distributed processing ability, high fault tolerance, intelligence, and self-learning ability, which combines the processing and storage of information. With its unique knowledge representation and intelligent adaptive learning ability, it has attracted the attention of various disciplines [24, 25]. In fact, it is a complex network with a large number of simple elements interconnected, which is highly nonlinear and can carry out complex logical operations and deal with nonlinear relations. ANN also has the preliminary ability of self-adaptation and self-organization. It is necessary to change synaptic weight value during learning or training to adapt to the requirements of the surrounding environment. The same network can have different functions because of different learning methods and content. ANN is a system with learning ability that can develop knowledge beyond the designer’s original knowledge level [26]. Generally, its learning (or training) can be divided into two modes: one is supervised learning, which classifies or imitates using given sample standards; the other is unsupervised learning. In this case, only the learning mode or some rules are specified, and the specific learning content is different from the environment the system is in (i.e., the input signal). The system can automatically discover the characteristics and regularity of the environment and has a function more similar to the human brain [14, 27, 28]. Figure 1 shows the biological neural network model.

As shown in Figure 1, it is a large network structure composed of neurons, cells, electrocution, and other structures, which are used to help organisms think and act. Based on the basic principle of neural network in biology, after understanding and abstracting the structure of the human brain and the response mechanism of external stimuli, the ANN model simulates the processing mechanism of the human brain’s nervous system to complex information on the theoretical basis of network topology knowledge. ANN is an artificial network composed of a large number of interconnected processing units, which is used to simulate the structure and function of the brain nervous system. These processing units are called artificial neurons. ANN can be regarded as a directed graph connected by weighted arcs with artificial neurons as nodes. In this directed diagram, the artificial neuron is a simulation of the biological neuron, and the directed arc is a simulation of the axon-synapse-dendrite pair. The weight of a directed arc indicates the strength of interaction between two connected artificial neurons. Figure 2 shows the neuron model in ANN.

ANN neuron is an information processing unit with multiple inputs and a single output, which has spatial integration and threshold properties. Input is divided into excitatory input and inhibitory input. When only one layer of neurons exists in an ANN, the input layer is called layer zero because it only buffers the input. The only layer of neurons that exists forms the output layer. Each neuron in the output layer has its weight and threshold. Neurons no longer deal with discrete values, but with continuous values. The weight will change when learning, to memorize and store the knowledge learned. The activation function in neuron output is similar to binary classification, which simulates the two states of neuron excitation and inhibition in biology. The input of the neuron is similar to the linear threshold unit (LTU), as shown by (1), and the output is equation (2).

In (2), represents the threshold of neural nodes in the hidden layer, and f is the activation function. Figure 3 shows the ANN model when the bias b = 1.

By increasing the offset value, the input of ANN is changed, and the input of the ANN neuron is shown as follows:

Hebb criterion is adopted to adjust the next weight, and the equation is as follows:where is the learning rate, and it is a constant; and are outputs of two neurons; represents the status of neuron j. If and are activated simultaneously, both and are positive, and increases. If is activated and is not, then is positive and is negative, and reduces.

2.2. Neural Network Training Process

Generally, a neural network consists of one input layer, multiple hidden layers, and output layer, as shown in Figure 4.

In Figure 4, there are two hidden layers. The circle in the figure is regarded as a neuron. An ANN comprises of computational units analogous to that of the neurons of the biological nervous system known as artificial neurons. It is noted that an artificial nerve is a network model with multiple inputs and single output. At the same time, the ANN training process adjusts the free parameters of the neural network (such as the connection weight) through the stimulus effect of the environment where the neural network is located, so that the neural network responds to the external environment in a new way. To store a set of equilibrium points, the network can run and converge to this equilibrium point, and the feedback neural network model can do this [29]. Figure 5 is a recurrent network model.

As shown in Figure 5, each node represents a cell and receives both external input and feedback from other nodes, each of which also directly outputs externally. Hopfield network belongs to this type [30]. In some feedback networks, each neuron not only receives external input and feedback input from other nodes but also includes its feedback. Sometimes, the feedback neural network can also be completely undirected. In the diagram, each connection is bidirectional. Here, the synapse weight of feedback of the ith neuron to the jth neuron is equal to that of the feedback from the jth neuron to the ith neuron; that is, . Figure 6 shows the neural network learning process.

The training process is to fit the activation function f through the given mass x data and y data. The learning process can be divided into learning with mentor and learning without mentor. Learning with a mentor gives the expected output, and the actual output is close to the expected output by adjusting the weight. No mentor learning gives the measurement scale and optimizes parameters according to scales. Neural network learning adjusts the parameters of neurons so that the network can produce the desired output for a given input, expressed as follows:

As for the error, is used to calculate the error. is the error of the back layer. After the error is obtained, it is propagated back to the front neuron. Then, multiplied by the weight, the error of a neuron is obtained, as shown as follows:

Backpropagation is shown as follows:

Back to the first layer, we can start correcting the weight of the connections between the neurons. The gradient descent is used: the original weight + an error term, as shown as follows:

The input of each neuron in the hidden layer and the input layer is used to modify the connection weight. The following is the weight modification of the neuron, and the same can be obtained for neuron.

Similarly, the weight correction of the second hidden layer is obtained as follows:

This completes the modification of connection weights for each layer. Then, the next cycle can be carried out: a sample is an input into the modified model and then forward propagated to y. Next, ∆ is calculated, and backpropagation is carried out to correct weights layer by layer. The optimization objective of the neural network is shown as follows:

2.3. ANN-Based Planting Arrangement Model

In the context of green ecology and environment, the plant form distribution of smart city is shown in Figure 7.

In Figure 7, the plant form category library is the input layer, the planting arrangement scheme is the output layer, and the rest are the hidden layer. Table 1 shows the form distribution of plants.

In Table 1, the urban plants commonly include spherical, steeple-shaped, and cone-shaped trees. Round trees are mostly broad-leaved trees with neat and thick crowns. Steeple-shaped and cone-shaped trees are coniferous trees with strong upward dynamic force. The shape of the shrub mostly undergoes artificial modification, and the common shrub differs greatly from the actual shape without an obvious trunk and is in the majority of spherical clumps.

3. Results and Discussion

3.1. Analysis of Urban Planting Area

Planting arrangement is an important part of smart city planning, which not only improves the ecological environment but also beautifies living space. Figure 8 shows urban planting areas in recent years.

From Figure 8, urban green area in China has been increasing year by year in recent years. China’s urban green area was 3.312 million hectares in 2020, up 4.8 percent from 2019. The planting coverage in smart cities improves the ecological environment to the maximum extent and, accordingly, people’s mental state is more pleasant (data from National Bureau of Statistics).

3.2. Planting Arrangement Analysis in Smart City

Involving the forms of the 18 types of plants, the arrangement database of plant form is established by ANN, and supervised learning is carried out by the error backpropagation algorithm. Based on the characteristics of the ANN model, the urban plant form is used as the input node, and the logical connection of the plant number, application frequency, and arrangement area is analyzed. By adjusting the weight according to the Hebb criterion, the statistical model of plant distribution frequency in a smart city can be obtained. Figure 9 shows the frequency of planting arrangements in a smart city.

It is noted that steamed bun-shaped plants have the largest total number, followed by spherical and bush-like plants. According to their ornamental characteristics, steamed bun-shaped and spherical plants are harmonious in the green ecological environment of smart city, while bush-like plants are natural in appearance. According to the frequency of planting arrangement, spherical and palm or coconut-form have a higher frequency of planting arrangement, and the planting arrangement of a wind-shaped plant has a low frequency. According to the overall ornamental effects, the ornamental effects of the wind-shaped plant are slightly strange, while spherical and palm or coconut-form plants are harmonious, forming the diversity of green ecology of a smart city. Figure 10 shows the importance of planting arrangements of different species.

These 18 types of plants are very important in the green ecological environment of smart cities, reflecting the rationality of planting arrangements of different types of plants in the smart city.

3.3. Suggestions on Planting Arrangement in Smart City

The sustainable development and green ecological development of the smart city cannot be separated from the planting arrangement. Only by improving the planting arrangement, the ecological, functional, ornamental, and cultural smart city landscape can be realized. First, the planting arrangement in urban planning should consider the effectiveness of the plant landscape. The planting arrangement not only meets the requirements of urban functions but also beautifies the environment. Secondly, based on the theory of ecological gardens, the planting arrangement should simulate the natural ecological environment and make use of plant physiology, ecological indicators, and aesthetic principles, to create a multilayer structure and to maintain the stability and persistence of plant communities in space and time. Therefore, a reasonable and scientific arrangement of plants can make the city more vigorous, improve the aesthetic effects of the city, and effectively improve the urban ecological environment. In the arrangement process, the selection of plant species, number, and location arrangement must follow some basic artistic requirements whether with plants as the main scene or with plants and other garden elements together as the main scene. Especially the arrangement of green plants, one cannot be eager to achieve. It is not sensible to show political achievements and ignore the ecological function and social benefits. Thirdly, the diversity of urban green space is not only required for the stability and sustainable development of the ecosystem but also for the reduction of conservation costs. More plant species used in urban green space can more effectively inhibit the occurrence of diseases and insect pests, and there is a certain inverse relationship between the two.

4. Conclusion

In this work, the planting arrangement of a smart city is mainly explored according to plant forms in a green ecological environment. The planting arrangement analysis is mainly carried out by combining the structure, characteristics, and basic principles of the ANN model. The output value of planting arrangement in a smart city is calculated according to the error backpropagation algorithm, and the weight is adjusted according to the Hebb criterion. Finally, the statistical model of planting arrangement in a smart city is analyzed by ANN. 18 kinds of plants with different forms are included, and the results show that steamed bun-shaped plants have the largest total number, followed by spherical and bush-like plants. Planting arrangements for spherical and palm or coconut-form plants have a higher frequency, whereas the planting arrangement frequency of wind-shaped plants is lower. For the importance of its arrangement area, these 18 types of plants are very important for a green ecological environment in the smart city, reflecting the rationality of planting arrangement of different types of plants in the smart city. According to the overall ornamental effects, the ornamental effects of wind-shaped plants are slightly peculiar, while spherical and palm or coconut-form plants are harmonious, so are steamed bun-shaped and spherical plants under green ecology. Bush-like plants are natural in appearance, reflecting the varieties of green environments. However, only 18 species of plants are explored in this work, and the diversity is not obvious. The subsequent research on planting arrangements should include more species.

Data Availability

The simulation experiment data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

References

M. D. Lytras and A. Visvizi, “Who uses smart city services and what to make of it: toward interdisciplinary smart cities research,” Sustainability, vol. 10, no. 6, p. 1998, 2018.
View at: Publisher Site | Google Scholar
M. O. K. Azad, K. H. Kjaer, M. Adnan et al., “The evaluation of growth performance, photosynthetic capacity, and primary and secondary metabolite content of leaf lettuce grown under limited irradiation of blue and red LED light in an urban plant factory,” Agriculture, vol. 10, no. 2, p. 28, 2020.
View at: Publisher Site | Google Scholar
H. Zeayter and A. M. H. Mansour, “Heritage conservation ideologies analysis - h,” HBRC journal, vol. 14, no. 3, pp. 345–356, 2018.
View at: Publisher Site | Google Scholar
A. González-Briones, P. Chamoso, H. Yoe, and J. Corchado, “GreenVMAS: virtual organization based platform for heating greenhouses using waste energy from power plants,” Sensors, vol. 18, no. 3, p. 861, 2018.
View at: Publisher Site | Google Scholar
Y. Zhang, N. Zheng, Y. Cao et al., “Climbing-inspired twining electrodes using shape memory for peripheral nerve stimulation and recording,” Science Advances, vol. 5, no. 4, p. eaaw1066, 2019.
View at: Publisher Site | Google Scholar
Y. A. Delgado De La Flor, C. E. Burkman, T. K. Eldredge, and M. M. Gardiner, “Patch and landscape-scale variables influence the taxonomic and functional composition of beetles in urban greenspaces,” Ecosphere, vol. 8, no. 11, p. e02007, 2017.
View at: Publisher Site | Google Scholar
D. An and J.-Y. Lee, “Influence and sustainability of the concept of landscape seen in cheonggye stream and suseongdong valley restoration projects,” Sustainability, vol. 11, no. 4, p. 1126, 2019.
View at: Publisher Site | Google Scholar
A. B. Bennett and S. Lovell, “Landscape and local site variables differentially influence pollinators and pollination services in urban agricultural sites,” PLoS One, vol. 14, no. 2, p. e0212034, 2019.
View at: Publisher Site | Google Scholar
G. Bakó, “Configuration methodology for a monitoring network based on high resolution and high frequency aerial surveys,” Tájökológiai Lapok, vol. 17, no. 1, pp. 61–74, 2019.
View at: Google Scholar
E. B. Hassen and A. M. Asmare, “Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks,” Cosmetics International, vol. 5, no. 1, p. 87, 2019.
View at: Google Scholar
G. Zhang, X. Zhang, Y. Luan, J. Jiang, and H. Hu, “Scheduling algorithm for the picture configuration for secondary tasks of a digital human–computer interface in a nuclear power plant,” International Journal of Advanced Robotic Systems, vol. 17, no. 2, p. 1729881420911256, 2020.
View at: Publisher Site | Google Scholar
V. Morales, A. Noël Racine, J.-M. Garbarino, B. Massiera, L. Bailly, and C. Pradier, “Configuration sociale du réseau sport santé bien-être en région Provence-Alpes Côte d’Azur: le rôle prépondérant du médecin prescripteur,” Science & Sports, vol. 32, no. 3, pp. 129–134, 2017.
View at: Publisher Site | Google Scholar
F. Almalki, S. H. Alsamhi, R. Sahal et al., “Green IoT for eco-friendly and sustainable smart cities: future directions and opportunities,” Mobile Networks and Applications, pp. 1–25, 2021 Aug 17.
View at: Publisher Site | Google Scholar
B. Radha Krishnan, V. Vijayan, T. Parameshwaran Pillai, and T. Sathish, “Influence of surface roughness in turning process - an analysis using artificial neural network,” Transactions of the Canadian Society for Mechanical Engineering, vol. 43, no. 4, pp. 509–514, 2019.
View at: Publisher Site | Google Scholar
M. K. Georgakis, R. Malik, H. Björkbacka et al., “Circulating mcprs,” Circulation Research, vol. 125, no. 8, pp. 773–782, 2019.
View at: Publisher Site | Google Scholar
W. J. Lee, Y. C. Liao, Y. F. Wang, I. Lin, S. J. Wang, and J. L. Fuh, “Plasma MCP-1 and cognitive decline in patients with Alzheimer’s disease and mild cognitive impairment: a two-year follow-up study,” Scientific Reports, vol. 8, no. 1, pp. 1–8, 2018.
View at: Publisher Site | Google Scholar
S. P. Nyoni and T. Nyoni, “Forecasting ART coverage in Kenya using the Multilayer perceptron neural network,” International Research Journal of Innovations in Engineering and Technology, vol. 5, no. 3, p. 161, 2021.
View at: Google Scholar
B. Raheli, M. T. Aalami, A. El-Shafie, M. A. Ghorbani, and R. C. Deo, “Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River,” Environmental Earth Sciences, vol. 76, no. 14, pp. 1–16, 2017.
View at: Publisher Site | Google Scholar
S. P. Siregar and A. Wanto, “Analysis of artificial neural network accuracy using backpropagation algorithm in predicting process (forecasting),” IJISTECH (International Journal Of Information System & Technology), vol. 1, no. 1, pp. 34–42, 2017.
View at: Publisher Site | Google Scholar
A. Tealab, “Time series forecasting using artificial neural networks methodologies: a systematic review,” Future Computing and Informatics Journal, vol. 3, no. 2, pp. 334–340, 2018.
View at: Publisher Site | Google Scholar
H. Nguyen and X.-N. Bui, “Predicting blast-induced air overpressure: a robust artificial intelligence system based on artificial neural networks and random forest,” Natural Resources Research, vol. 28, no. 3, pp. 893–907, 2019.
View at: Publisher Site | Google Scholar
P. Kickingereder, F. Isensee, I. Tursunova et al., “Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study,” The Lancet Oncology, vol. 20, no. 5, pp. 728–740, 2019.
View at: Publisher Site | Google Scholar
K. Sarkar, D. Bonnerjee, R. Srivastava, and S. Bagh, “A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing,” Chemical Science, vol. 12, no. 48, pp. 15821–15832, 2021.
View at: Publisher Site | Google Scholar
R. K. Umarov and V. V. Solovyev, “Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks,” PloS one, vol. 12, no. 2, p. e0171410, 2017.
View at: Publisher Site | Google Scholar
Y. Park and J.-S. Lee, “Artificial synapses with short- and long-term memory for spiking neural networks based on renewable materials,” ACS Nano, vol. 11, no. 9, pp. 8962–8969, 2017.
View at: Publisher Site | Google Scholar
H. P. N. Nagarajan, H. Mokhtarian, H. Jafarian et al., “Knowledge-based design of artificial neural network topology for additive manufacturing process modeling: a new approach and case study for fused deposition modeling,” Journal of Mechanical Design, vol. 141, no. 2, p. 021705, 2019.
View at: Publisher Site | Google Scholar
C. Anitescu, E. Atroshchenko, and N. Alajlan, “Artificial neural network methods for the solution of second order boundary value problems,” Computers, Materials & Continua, vol. 59, no. 1, pp. 345–359, 2019.
View at: Publisher Site | Google Scholar
Ö. F. Ertuğrul and Ş. Altun, “Determining optimal artificial neural network training method in predicting the performance and emission parameters of a biodiesel-fueled diesel generator,” International Journal of Advances in Engineering & Technology, vol. 7, no. 1, pp. 7–17, 2018.
View at: Google Scholar
H. Jelodar, Y. Wang, and R. Orji, “Deep sentiment classification and topic discovery on novel coronavirus or covid-19 online discussions: nlp using lstm recurrent neural network approach,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 10, pp. 2733–2742, 2020.
View at: Publisher Site | Google Scholar
S. Yin, J. Liu, and L. Teng, “A sequential cipher algorithm based on feedback discrete hopfield neural network and logistic chaotic sequence,” International Journal on Network Security, vol. 22, no. 5, pp. 869–873, 2020.
View at: Google Scholar

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Copyright © 2022 Qingsong Meng and Shufeng Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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