Prediction of the livestock carrying capacity using neural network in the meadow steppe

2019 ◽  
Vol 41 (1) ◽  
pp. 65 ◽  
Author(s):  
T. S. Wu ◽  
H. P. Fu ◽  
G. Jin ◽  
H. F. Wu ◽  
H. M. Bai
Keyword(s):  
Neural Network ◽  
Carrying Capacity ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Back Propagation ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Back Propagation Neural Network ◽  
Meadow Steppe ◽  
Normalised Difference Vegetation Index

In order to predict the livestock carrying capacity in meadow steppe, a method using back propagation neural network is proposed based on the meteorological data and the remote-sensing data of Normalised Difference Vegetation Index. In the proposed method, back propagation neural network was first utilised to build a behavioural model to forecast precipitation during the grass-growing season (June–July–August) from 1961 to 2015. Second, the relationship between precipitation and Normalised Difference Vegetation Index during the grass-growing season from 2000 to 2015 was modelled with the help of back propagation neural network. The prediction results demonstrate that the proposed back propagation neural network-based model is effective in the forecast of precipitation and Normalised Difference Vegetation Index. Thus, an accurate prediction of livestock carrying capacity is achieved based on the proposed back propagation neural network-based model. In short, this work can be used to improve the utilisation of grassland and prevent the occurrence of vegetation degradation by overgrazing in drought years for arid and semiarid grasslands.

scholarly journals A Neural Network Model for Wildfire Scale Prediction using Meteorological Factors

2021 ◽  
Vol 9 (VI) ◽  
pp. 2646-2650
Author(s):  
Lavanya I
Keyword(s):  
Neural Network ◽  
Forest Fire ◽  
Forest Fires ◽  
Meteorological Data ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fire Incident ◽  
Forested Areas ◽  
Long Term Impact

Forest fires are natural hazards defined as movements of fire through unregulated and uncontrolled forested areas. They pose a permanent risk of loss of forest and forest land. The ability to reliably forecast the region that could be involved in a forest fire incident will help to optimize fire prevention efforts. It appears that Portugal may theoretically make better use of the wildfire risk assessment. More than any other region in Europe, it is a country overrun by wildfires. It has a large amount of forest. Forest fires have a long-term impact on the climate because they contribute to deforestation and global warming, which is one of the main causes of the phenomenon. This research employs Back Propagation Neural Network (BPNN) and Recurrent Neural Network (RNN) models with meteorological parameters as inputs to anticipate forest fires as a means of safeguarding forest biodiversity. The results indicate that using meteorological data, it is possible to anticipate the severity of a forest fire at the beginning.

scholarly journals KLASIFIKASI PENUTUP/PENGGUNAAN LAHAN DENGAN DATA SATELIT PENGINDERAAN JAUH HIPERSPEKTRAL (HYPERION) MENGGUNAKAN METODE NEURAL NETWORK TIRUAN

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Dony Kushardono
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Near Infrared ◽  
Back Propagation ◽  
Remote Sensing Data ◽  
Back Propagation Neural Network ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data ◽  
Test Input ◽  
Long Time

Hyperspectral remote sensing data has numerous spectral information for the land-use/land-cover (LULC) classification, but a large number of hyperspectral band data is becoming a problem in the LULC classification. This research proposes the use of the back propagation neural network for LULC classification with hyperspectral remote sensing data. Neural network used in this study is three layers, in which to test input layer has a number of neurons as many as 242 to process all band data, 163 neurons, and 50 neurons to process the data band has a high average digital number, and data bands at wavelengths of visible to near infrared. The results showed the use of all the data band hyperspectral on classification with the neural network has the highest classification accuracy of up to 98% for 18 LULC class, but it takes a very long time. Selecting a number of bands of precise data for classification with a neural network, in addition to speeding up data processing time, can also provide sufficient accuracy classification results.ABSTRAKData penginderaan jauh hiperspektral memiliki informasi spektral yang sangat banyak untuk klasifikasi penutup/penggunaan lahan (LULC), akan tetapi banyaknya jumlah band data hiperspektral menjadi masalah dalam klasifikasi LULC. Penelitian ini mengusulkan penggunaan back propagation neural network untuk klasifikasi LULC dengan data penginderaan jauh hiperspektral. Neural network yang dipergunakan 3 lapis, dimana untuk uji coba lapis masukan memiliki jumlah neuron sebanyak 242 untuk mengolah seluruh band, 163 neuron, dan 50 neuron untuk mengolah data band yang memiliki nilai digital rataan yang tinggi, dan data band pada panjang gelombang cahaya tampak hingga infra merah dekat. Hasil penelitian menunjukkan penggunaan seluruh band data hiperspektral pada klasifikasi dengan neural network memiliki akurasi hasil klasifikasi tertinggi hingga 98% untuk 18 kelas LULC, akan tetapi waktu yang diperlukan sangat lama. Pemilihan sejumlah band data yang tepat untuk klasifikasi dengan neural network, selain mempercepat waktu pengolahan data, juga bisa memberikan akurasi hasil klasifikasi yang mencukupi.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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