Introducing a Hybrid Model SAE-BP for Regression Analysis of Soil Temperature With Hyperspectral Data

[1]  Roozbeh Moazenzadeh,et al.  Assessment of bio-inspired metaheuristic optimisation algorithms for estimating soil temperature , 2019, Geoderma.

[2]  Ali Ouni,et al.  Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting , 2019, Energies.

[3]  Yu Liu,et al.  Autoencoder-based deep belief regression network for air particulate matter concentration forecasting , 2018, Journal of Intelligent & Fuzzy Systems.

[4]  Sina Keller,et al.  Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[5]  Alain Abran,et al.  Software Development Effort Estimation Using Regression Fuzzy Models , 2019, Comput. Intell. Neurosci..

[6]  Zichen Zhang,et al.  Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm , 2019, Nonlinear Dynamics.

[7]  Ningbo Cui,et al.  Estimation of soil temperature from meteorological data using different machine learning models , 2019, Geoderma.

[8]  Ruqiang Yan,et al.  A sparse auto-encoder-based deep neural network approach for induction motor faults classification , 2016 .

[9]  Feng Xu,et al.  A Flood Forecasting Model Based on Deep Learning Algorithm via Integrating Stacked Autoencoders with BP Neural Network , 2017, 2017 IEEE Third International Conference on Multimedia Big Data (BigMM).

[10]  Peter Zeilhofer,et al.  Identification of Ramularia Leaf Blight Cotton Disease Infection Levels by Multispectral, Multiscale UAV Imagery , 2019, Drones.

[11]  Wen Zhuo,et al.  Soil temperature estimation at different depths, using remotely-sensed data , 2020, Journal of Integrative Agriculture.

[12]  Wei Tian,et al.  A Model Combining Stacked Auto Encoder and Back Propagation Algorithm for Short-Term Wind Power Forecasting , 2018, IEEE Access.

[13]  Qin Lin,et al.  Classification of Epileptic EEG Signals with Stacked Sparse Autoencoder Based on Deep Learning , 2016, ICIC.

[14]  Asifullah Khan,et al.  Wind power prediction using deep neural network based meta regression and transfer learning , 2017, Appl. Soft Comput..

[15]  B. Wotton,et al.  Summer Moisture and Wildfire Risks across Canada , 2009 .

[16]  Sina Keller,et al.  Fusion of Hyper Spectral and Ground Penetrating Radar Data to Estimate Soil Moisture , 2018, 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[17]  Angshul Majumdar,et al.  Machine Load Estimation Via Stacked Autoencoder Regression , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[18]  Xiaojian Wang,et al.  Electric Load Data Compression and Classification Based on Deep Stacked Auto-Encoders , 2019, Energies.

[19]  Qiufeng Wu,et al.  Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks , 2020 .

[20]  Tianqi Wang,et al.  An Android Malware Detection Method Based on Deep AutoEncoder , 2018, AICCC '18.

[21]  Ozgur Kisi,et al.  Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths , 2018 .

[22]  Saeid Mehdizadeh,et al.  Evaluating the performance of artificial intelligence methods for estimation of monthly mean soil temperature without using meteorological data , 2017, Environmental Earth Sciences.

[23]  Yang Zhang,et al.  Novel chaotic bat algorithm for forecasting complex motion of floating platforms , 2019, Applied Mathematical Modelling.

[24]  Guoliang Ye,et al.  Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization , 2016 .

[25]  Lin Zhao,et al.  Soil moisture and texture primarily control the soil nutrient stoichiometry across the Tibetan grassland. , 2018, The Science of the total environment.

[26]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[27]  Ying Qu,et al.  Hybrid Spectral Unmixing in Land-Cover Classification , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.

[28]  Stephan J. Maas,et al.  A Three-Dimensional Index for Characterizing Crop Water Stress , 2014, Remote. Sens..

[29]  Chao Han,et al.  Examination of errors of table integration in flamelet/progress variable modeling of a turbulent non-premixed jet flame , 2019, Applied Mathematical Modelling.

[30]  Nilanjan Dey,et al.  Soil moisture quantity prediction using optimized neural supported model for sustainable agricultural applications , 2020, Sustain. Comput. Informatics Syst..

[31]  Nilanjan Dey,et al.  A Survey of Data Mining and Deep Learning in Bioinformatics , 2018, Journal of Medical Systems.

[32]  Daniel V. Smith,et al.  A comparison of autoencoder and statistical features for cattle behaviour classification , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).