Prediction of Rice Cultivation in India—Support Vector Regression Approach with Various Kernels for Non-Linear Patterns

Paidipati, Kiran Kumar; Chesneau, Christophe; Nayana, B. M.; Kumar, Kolla Rohith; Polisetty, Kalpana; Kurangi, Chinnarao

doi:10.3390/agriengineering3020012

Cited by 15 publications

(8 citation statements)

References 21 publications

(27 reference statements)

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“…GA-SVR Algorithm. The direct optimization method is used for intelligent identification of elastic-plastic parameters, in order to compare the effect of the improved GA-SVR algorithm proposed by the author on the three-dimensional elastic-plastic inversion; here, the GA-BP algorithm is used for the same inverse analysis, except that the regressor is changed to BP neural network [18]. The genetic algorithm uses the same real number coding and the same fitness function.…”

Section: Intelligent Identification Of Three-dimensional Elastic-plas...mentioning

confidence: 99%

Parameter Identification of 3D Elastic-Plastic Model for Tunnel Engineering Based on Improved Genetic Algorithm

Dong

Chen

2022

Mathematical Problems in Engineering

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In order to overcome the deficiencies of the existing intelligent displacement back analysis methods, the authors propose a parameter identification method for the 3D elastic-plastic model of tunnel engineering based on an improved genetic algorithm. An improved SVR algorithm is introduced, which solves this problem by transforming the multidimensional output variable regression into a multilayer standard one-dimensional output variable regression; combined with the decimal-coded genetic algorithm, an improved GA-SVR algorithm is formed, and genetic algorithm is used to search for optimal SVR model parameters, in order to establish the optimal nonlinear mapping relationship between the parameters to be identified and the displacement. The genetic algorithm is used to carry out the optimal identification of the parameters to be identified. In order to compare the effect of this improved GA-SVR algorithm, the genetic algorithm is combined with the BP neural network to form the GA-BP algorithm and compile the corresponding calculation program. The two algorithms are applied to the intelligent identification of the parameters of the 3D elastic-plastic model of the same tunnel engineering. The experimental results show that the maximum relative error of the 6 parameters to be identified is 18.51%, only 2 are over 15% and 14 are within 10%, and the average relative error of the 6 parameters is only 10.32%, the minimum is 2.63%; for the GA-BP algorithm, the maximum relative error of identification is 22.73%, 4 are more than 15% and 9 are within 10%, and the average relative error of 6 parameters’ identification is up to 13.91%, and the minimum is 4.64%. The improved GA-SVR algorithm can achieve higher identification accuracy and better calculation efficiency than the GA-BP algorithm and can be used in the identification of similar geotechnical engineering parameters.

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Section: Intelligent Identification Of Three-dimensional Elastic-plas...mentioning

confidence: 99%

Parameter Identification of 3D Elastic-Plastic Model for Tunnel Engineering Based on Improved Genetic Algorithm

Dong

Chen

2022

Mathematical Problems in Engineering

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Section: Methodsmentioning

confidence: 99%

“…In the agricultural domain, crop yield prediction is a challenging task, where many research works are performed to predict better crop yield utilizing machine learning techniques. [36][37][38] In this article, the data was collected from the Telangana Agricultural Report (2017-2018) and preprocessed before being applied to classification. The proposed model includes three phases such as data collection: real-time data, data preprocessing MMN, and classification: LSTM with Adam optimizer and Huber loss function.…”

Section: Methodsmentioning

confidence: 99%

Crop yield forecasting by long short‐term memory network with Adam optimizer and Huber loss function in Andhra Pradesh, India

Dwaram

Madapuri

2022

Concurrency and Computation

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Summary In recent times, crop yield prediction gains more attention among researchers communities to expand food production. In this article, an effort is done for crop yield prediction in Andhra Pradesh state (India), because AP highly contributes to the Indian economy. Especially, the crop yield prediction is done for Western Godavari, Prakasam, Guntur, Srikakulam, Krishna, Vizianagaram, Visakhapatnam, and Nellore districts of Andhra Pradesh (India) by estimating the rainfall based on the attributes: average temperature, minimum temperature, maximum temperature, precipitation, cloud cover, and velocity potential. After collecting the data from the Telangana agricultural report (2017–2018), the collected data are preprocessed utilizing the min‐max normalization technique, which is effective in preserving the relationship among data. The normalized data are classified using long short term memory network with Adam optimizer and Huber loss function. The proposed classifier has adaptive learning rates for dissimilar parameters by identifying the first and second‐moment gradient estimation so it has better computational effectiveness, unlike other classifiers. The proposed classifier predicts each site in the collected dataset into one of the three crops: rice, sugarcane, and onion. Finally, the agriculture experts validate the obtained results of the proposed model.

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“…In modern society, industry-education integration (IEI) has become development trend of higher education. The combination of industrial demand and teaching practice helps to improve teaching effect and cultivate students' practical ability [1][2] . At present, back propagation neural network (BP) can evaluate teaching quality.…”

Section: Introductionmentioning

confidence: 99%

Teaching quality evaluation model construction of university industry and education integration by adaptive BP and DDAE-SVR

Zhang,

Weng

2024

Preprint

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To adapt to changing teaching modes in colleges and universities, and better predict and analyze the students' learning situation, this study aims at a series of deficiencies existing in the traditional teaching quality evaluation index system. By leveraging deep learning, the evaluation model is enhanced and constructed. Firstly, the system is refined by integrating the concept of integrating industry and education. Secondly, the traditional back propagation neural network is improved, and the model is built by incorporating deep noise reduction auto-encoder and support vector regression technology. Model test showed that model used in this study had good iterative performance. When the number of iterations was 62, the model started to enter a stable state, and the optimal fitness value in the stable state was 0.25. In addition, the detection accuracy was up to 0.98, and prediction effect can be satisfied by most teachers and students. To sum up, the quality evaluation model can accurately evaluate teaching quality, provide a reliable reference for colleges and enterprises, and promote the in-depth integration development of industry and education.

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Prediction of Rice Cultivation in India—Support Vector Regression Approach with Various Kernels for Non-Linear Patterns

Cited by 15 publications

References 21 publications

Parameter Identification of 3D Elastic-Plastic Model for Tunnel Engineering Based on Improved Genetic Algorithm

Parameter Identification of 3D Elastic-Plastic Model for Tunnel Engineering Based on Improved Genetic Algorithm

Crop yield forecasting by long short‐term memory network with Adam optimizer and Huber loss function in Andhra Pradesh, India

Teaching quality evaluation model construction of university industry and education integration by adaptive BP and DDAE-SVR

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