scholarly journals Analysis of Landscape Ecological Planning Based on the High-Order Multiwavelet Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
ChuanDong Yu ◽  
Nan Du
Keyword(s):  
Neural Network ◽  
Landscape Ecology ◽  
Wavelet Neural Network ◽  
High Order ◽  
Average Error ◽  
Ecological Planning ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Landscape Ecological Planning ◽  
Landscape Ecological

Landscape architecture has both natural and social properties, which is the embodiment of people protecting the natural environment. Since the industrial revolution, the modern industry has developed rapidly. It has increased the living standard of people and consumed a lot of natural resources such as forest and energy. The ecological environment has been greatly damaged, and the landscape of gardens has been affected. Therefore, it is of great significance to find a method to evaluate the landscape ecology and plan the landscape ecology. This paper proposes a new high-order wavelet neural network algorithm combining wavelet analysis and artificial neural network. A model of ecological evaluation of landscape based on high-order wavelet neural network algorithm is proposed to evaluate the landscape ecology and provide reference data for the ecological planning of the landscape. The results show that the training times of the wavelet neural network to achieve the target accuracy are 3600 times less than those of the BP neural network. The MSE and MAE of the WNN are 0.0639 and 0.1501, respectively. The average error of the model to the comprehensive evaluation index of the landscape ecology is 0.005. The accuracy of the model to evaluate the sustainability of landscape land resources is 98.67%. The above results show that the model based on the wavelet neural network can effectively and accurately complete the evaluation of landscape ecology and then provide a decision-making basis for landscape ecological planning, which is of high practicability.

scholarly journals EQUIPMENT PRICE FORECAST BASED ON T-S FUZZY NEURAL NETWORK

2018 ◽  
Vol 48 (4) ◽  
pp. 305-309
Author(s):  
G. P. JIANG ◽  
L. XIE ◽  
S. X. SUN
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Test Results ◽  
Factors Affecting ◽  
Price Forecast ◽  
Network Algorithm ◽  
Auction Price ◽  
Variable Weight ◽  
Fuzzy Neural ◽  
Neural Network Algorithm

As we all know, the factors affecting the price of equipment are more complicated, but these factors still have a great correlation. How can we accurately predict the price of equipment? Based on the study of the tight support and smoothness of wavelet function, this paper proposes a correlation variable weight wavelet neural network algorithm to predict the price of 162 devices. The test results show that if the weight is not reduced, the predicted price is 0, and the error is still large. However, by arranging the data from small to large, the variable weighted wavelet neural network algorithm is used to predict the result closer to the auction price, which overcomes the incompatibility of the algorithm iteration and provides a reference for accurately predicting the price of the device.

The Study of Prediction of Agricultural Source Pollution Emissions of Ammonia Nitrogen (AN) and Chemical Oxygen Demand (COD) Based on Bionic Neural Network Algorithm

2013 ◽  
Vol 461 ◽  
pp. 544-552 ◽  
Author(s):  
Hong Peng Guo ◽  
Gan Yu Feng ◽  
Chun Xia Liu ◽  
Xiao Yi Zhang
Keyword(s):  
Neural Network ◽  
Chemical Oxygen Demand ◽  
Rapid Development ◽  
Oxygen Demand ◽  
Ammonia Nitrogen ◽  
Average Error ◽  
Research Results ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Pollution Emissions

Nearly 40% of Chinese water pollution comes from agricultural sources of pollution, and the annual emissions are difference. If we want to control pollution emissions effectively, we need to accurately predict the amount of agricultural emissions of Ammonia Nitrogen (AN) and Chemical Oxygen Demand (COD). Due to the complex mechanism of the agricultural non-point source pollution, its emissions are very difficult to measure. Currently, the Bionics Research is in a stage of rapid development, and it continues to expand into many new areas of research. So the comprehensive study of Bionics and pollutant control study will be a good choice. This research used bionic BP（Back Propagation） neural network algorithm, and used pollution census data from 2002 to 2007 and established neural network model with neural network algorithm. And we predicted the agricultural sources of emissions of AN and COD with the data from 2008 to 2010. Finally we compared the predicted value and the actual value. Research results showed that, with using the bionic BP neural network, agricultural sources emissions of AN and COD are evaluated actually and the results indicate that the average error is under 5.0%. Research results proved that the model is effective. The neural network is a scientific predict method for the agricultural sources emissions of AN and COD. It can be widely used in the prediction of agricultural sources emissions of AN and COD.

scholarly journals Improving Land Use Planning through the Evaluation of Ecosystem Services: One Case Study of Quyang County

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lin Liu ◽  
Yapeng Zhou ◽  
Haikui Yin ◽  
Ruiqiang Zhang ◽  
Ying Ma ◽  
...  
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Landscape Ecology ◽  
Land Use Planning ◽  
Landscape Fragmentation ◽  
Shannon Index ◽  
Ecological Planning ◽  
Mountain Area ◽  
Landscape Ecological Planning ◽  
Landscape Ecological

Competition for land is increasing as demand for multiple land uses and ecosystem services rises. Land regulation of the principles of landscape ecology is necessary to develop more sustainable approaches to land use planning. The research evaluated the present land patterns and determined best practices for its regulation of Dongwang Township in Quyang County, located in the Taihang Mountain area of Hebei Province, China. The research used the landscape ecology theory to construct an index system for landscape pattern analysis based on the GIS and Fragstats 3.3 software. In this study, we examined the specific reasons that landscape ecology is superior to traditional methods in land consolidation planning and design, which is conducive to the comprehensive development of land ecological benefits. Landscape ecological planning can effectively reduce landscape fragmentation and improve intensive management. The result found that the descending order of the Shannon index was current landscape, landscape ecological planning, and traditional planning. Landscape ecological planning could protect the natural diversity than traditional planning. Landscape ecological planning enables the creation of long corridors, with higher densities and connectivity and lower average corridor widths than traditional planning. Besides, it can improve ecological service function values in the study area to varying degrees, thus discouraging residents from limiting themselves to grain production. This research has great potential to improve the visibility of ecosystem services in local land use planning and, thus, to improve the ecological functioning of future landscapes.

scholarly journals Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High-Order Simulated Annealing Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lei Zhang ◽  
Qixiang Zhong ◽  
Zhenglun Yu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Simulated Annealing ◽  
Clustering Algorithm ◽  
High Order ◽  
Diagnosis And Treatment ◽  
Disease Monitoring ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Medical Big Data

With the development of medical informatization, the data related to medical field are growing at an amazing speed, and medical big data appears. The mining and analysis of these data plays an important role in the prediction, monitoring, diagnosis, and treatment of tumor diseases. Therefore, this paper proposes a clustering algorithm of the high-order simulated annealing neural network algorithm and uses this algorithm to extract tumor disease-related big data, constructs training set according to the relevant information mined, designs a kind of dimension reduction model, aiming at the problem of excessive and wrong diagnosis and treatment in the diagnosis and treatment module of tumor disease monitoring mode, and establishes the corresponding control mechanism, so as to optimize the tumor disease monitoring mode. The results show that the clustering accuracy of the high-order simulated annealing neural network algorithm on different data sets (iris, wine, and Pima India diabetes) is 97.33%, 82.11%, and 70.56% and the execution time is 0.75 s, 0.562 s, and 1.092 s, which are better than those of the fast k-medoids algorithm and improved k-medoids clustering algorithm. To sum up, the high-order simulated annealing neural network algorithm can achieve good clustering effect in medical big data mining. The establishment of model M1 can reduce the probability of excessive and wrong medical treatment and improve the effectiveness of diagnosis and treatment module monitoring in tumor disease monitoring mode.

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