Long short-term memory neural network-based multi-level model for smart irrigation

Sidhu, R.S.; Rana, Prashant Singh

doi:10.1142/s0217984920504187

Cited by 10 publications

(5 citation statements)

References 29 publications

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“…Deep learning with LSTM networks was used for irrigation management in rice by Sidhu et al (2020a), and the results were consistent with the recommendations of a human expert and a proprietary software package. Another research to present the use of deep learning was that of Wakamori et al (2020), who used an LSTM-based multimodal NN with changes in the grouping of environmental variables.…”

Section: Neural Network Usesupporting

confidence: 62%

Application of Artificial Intelligence for Irrigation Management: A Systematic Review

Alvim¹,

Guimarães

Sousa

et al. 2022

Eng. Agríc.

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A literature review on artificial intelligence in irrigation management was performed, using the Systematic Literature Review (SLR) method with explicit search criteria. More than 45,000 complete titles in 130 reference bases were consulted at once. A total of 38 primary studies were selected, which formed the basis of this review. The findings showed increasing use of Artificial Neural Networks (ANN) fed with climate and soil sensor data for irrigation management solutions. ANNs have been the most popular choice for solutions that require machine learning techniques. Fuzzy-logic-based technologies stood out in Decision Support Systems (SSD). Hybrid neuro-fuzzy approaches manage the best aspects contained in each of the two techniques (ANN and fuzzy logic). Moreover, autonomous wireless and networked sensors have been the most often used. Good chances of developing solutions for irrigation management point to the growing application of ANN-based machine learning, Support Vector Machine (SVM), and Random Forests techniques, using wireless sensor networks and computer vision with remote sensing images.

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Section: Neural Network Usesupporting

confidence: 62%

Application of Artificial Intelligence for Irrigation Management: A Systematic Review

Alvim¹,

Guimarães

Sousa

et al. 2022

Eng. Agríc.

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“…Impact: Too many nodes, the training time increases, and the network is easy to overfit. According to the optimal hidden layer node number selection formula, we determine the range of hidden layer node number selection interval (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15). The relationship between the number of hidden layer nodes and the stability prediction error rate of the BP neural network of the HTGS is shown in Fig.…”

Section: Model Training and Testingmentioning

confidence: 99%

“…As a representative type of machine learning method, artificial neural network can establish a calculation model by imitating biological neural network, and has good self-learning ability and robust performance. [11][12][13] At present, there is no research on extending the machine learning method to the stability analysis of the HTGS. Therefore, based on the nonlinear mathematical model of the HTGS, the machine learning method of neural network is used to mine the evolution law of system stability under different working conditions, which provides a new theoretical analysis approach to ensure the safe and stable operation of hydropower stations.…”

Section: Introductionmentioning

confidence: 99%

Stability analysis of hydro-turbine governing system based on machine learning

Chen¹,

Tong²

2021

Chinese Phys. B

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Hydro-turbine governing system is a time-varying complex system with strong non-linearity, and its dynamic characteristics are jointly affected by hydraulic, mechanical, electrical, and other factors. Aiming at the stability of the hydro-turbine governing system, this paper first builds a dynamic model of the hydro-turbine governing system through mechanism modeling, and introduces the transfer coefficient characteristics under different load conditions to obtain the stability category of the system. BP neural network is used to perform the machine study and the predictive analysis of the stability of the system under different working conditions is carried out by using the additional momentum method to optimize the algorithm. The test set results show that the method can accurately distinguish the stability category of the hydro-turbine governing system (HTGS), and the research results can provide a theoretical reference for the operation and management of smart hydropower stations in the future.

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“…Using a forecast of global climate models as input for crop process-based models is problematic for two reasons: the low spatial resolutions of such models and the notable fluctuations and deviations in crucial processes for crop modeling at the daily time step, such as precipitation, droughts, and others [18,19]. Some of the most commonly used approaches to tackle this limitations are stochastic weather generators [20] and statistical time series forecasting methods [21].…”

Section: Introductionmentioning

confidence: 99%

Soil Dynamics and Crop Yield Modeling Using the MONICA Crop Simulation Model and Time Series Forecasting Methods

Mirpulatov,

Gasanov,

Matveev

2023

Agronomy

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Crop simulation models are an important tool for assessing agroecosystem performance and the impact of agrotechnologies on soil cover condition. However, the high uncertainty and labor intensiveness of long-term weather forecasting limits the applicability of such models. A possible solution may be to use time series forecasting models (SARIMAX and Prophet) and artificial neural-network-based technologies (Neural Prophet). This work compares the applicability of these methods for modeling soil condition dynamics and agroecosystem performance using the MONICA simulation model for Voronic Chernozems in the Kursk region of Russia. The goal is to determine which weather indicators are most important for the yield forecast and to choose the most appropriate methods for forecasting weather scenarios for agricultural modeling. Crop rotation of soybean and sugar beet was simulated, with agricultural techniques and fertilizer usage considered as factors. We demonstrated the high sensitivity of aboveground biomass production and soil moisture dynamics to daily temperature fluctuations and precipitation during the vegetation period. The dynamics of the leaf area index and nitrate content showed less sensitivity to the daily fluctuations of temperature and precipitation. Among the proposed forecasting methods, both SARIMAX and the Neural Prophet algorithm demonstrated the ability to forecast weather to model the dynamics of crop and soil conditions with the highest degree of approximation to actual observations. For the dynamic of the crop yield of soybean, the SARIMAX model exhibited the most favorable coefficient of determination, R2, while for sugar beet, the Neural Prophet model achieved superior R2 levels of 0.99 and 0.98, respectively.

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Long short-term memory neural network-based multi-level model for smart irrigation

Cited by 10 publications

References 29 publications

Application of Artificial Intelligence for Irrigation Management: A Systematic Review

Application of Artificial Intelligence for Irrigation Management: A Systematic Review

Stability analysis of hydro-turbine governing system based on machine learning

Soil Dynamics and Crop Yield Modeling Using the MONICA Crop Simulation Model and Time Series Forecasting Methods

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