Estimating the common agricultural policy milestones and targets by neural networks

Evaluation and Program Planning, Год журнала: 2023, Номер 99, С. 102296 - 102296

Опубликована: Май 3, 2023

Язык: Английский

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An Improved Sparrow Search Algorithm for Global Optimization with Customization-Based Mechanism

Axioms, Год журнала: 2023, Номер 12(8), С. 767 - 767

Опубликована: Авг. 7, 2023

To solve the problems of original sparrow search algorithm’s poor ability to jump out local extremes and its insufficient achieve global optimization, this paper simulates different learning forms students in each ranking segment class proposes a customized method (CLSSA) based on multi-role thinking. Firstly, cube chaos mapping is introduced initialization stage increase inherent randomness rationality distribution. Then, an improved spiral predation mechanism proposed for acquiring better exploitation. Moreover, strategy designed after follower phase balance exploration A boundary processing full utilization important location information used improve processing. The CLSSA tested 21 benchmark optimization problems, robustness verified 12 high-dimensional functions. In addition, comprehensive capability further proven CEC2017 test functions, intuitive given by Friedman's statistical results. Finally, three engineering are utilized verify effectiveness solving practical problems. comparative analysis shows that can significantly quality solution be considered excellent SSA variant.

Язык: Английский

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An Integrated Framework for Remote Sensing Assessment of the Trophic State of Large Lakes

Remote Sensing, Год журнала: 2023, Номер 15(17), С. 4238 - 4238

Опубликована: Авг. 29, 2023

The trophic state is an important factor reflecting the health of lake ecosystems. To accurately assess large lakes, integrated framework was developed by combining remote sensing data, field monitoring machine learning algorithms, and optimization algorithms. First, key meteorological environmental factors from in situ were combined with remotely sensed reflectance data statistical analysis used to determine main influencing state. Second, a index (TSI) inversion model constructed using algorithm, this then optimized sparrow search algorithm (SSA) based on backpropagation neural network (BP-NN) establish SSA-BP-NN model. Third, typical China (Hongze Lake) chosen as case study. application results show that, when (pH, temperature, average wind speed, sediment content) band combination Sentinel-2/MSI input variables, performance improved (R2 = 0.936, RMSE 1.133, MAPE 1.660%, MAD 0.604). Compared prior 0.834, 1.790, 2.679%, 1.030), accuracy 12.2%. It worth noting that could identify water bodies different states. Finally, framework, we mapped spatial distribution TSI Hongze Lake seasons 2019 2020 analyzed its variation characteristics. can combine regional special feature influenced complex environment S-2/MSI achieve assessment over 90% for sensitive waters has strong applicability robustness.

Язык: Английский

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Enhanced Neural Network for Rapid Identification of Crop Water and Nitrogen Content Using Multispectral Imaging

Agronomy, Год журнала: 2023, Номер 13(10), С. 2464 - 2464

Опубликована: Сен. 23, 2023

Precision irrigation and fertilization in agriculture are vital for sustainable crop production, relying on accurate determination of the crop’s nutritional status. However, there challenges optimizing traditional neural networks to achieve this accurately. This paper aims propose a rapid identification method water nitrogen content using optimized networks. addresses difficulty backpropagation network (BPNN) structure. It uses 179 multi−spectral images crops (such as maize) samples model. Particle swarm optimization (PSO) is applied optimize hidden layer nodes. Additionally, proposes double−hidden−layer structure improve model’s prediction accuracy. The proposed PSO−BPNN model showed 9.87% improvement accuracy compared with BPNN correlation coefficient R2 predicted was 0.9045 0.8734, respectively. experimental results demonstrate high training efficiency lays strong foundation developing precision plans modern holds promising prospects.

Язык: Английский

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Hyperparameter Optimization for Deep NeuralNetwork Models: A Comprehensive Study onMethods and Techniques

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Июнь 23, 2023

Abstract Advancements in computing and storage technologies have significantly contributed to the adoption of deep learning (DL)-based models among machinelearning (ML) experts. Although a generic model can be used search fora near-optimal solution any problem domain, what makes these DL modelscontext-sensitive is combination training data hyperparameters. Due lack inherent explainability HyperparameterOptimization (HPO) or tuning specific each art,science, experience. In this article, we explored various existing methods ways identify optimal set values for hyperparameters specificto along with techniques realize those real-lifesituations. The article also includes detailed comparative study variousstate-of-the-art HPO using Keras Tuner toolkit highlights observations describing how performance improvedby applying techniques.

Язык: Английский

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