Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches

Moosavi, Vahid; Talebi, Ali; Mokhtari, Mohammad Hossein; Hadian, Mohammad Reza

doi:10.1080/01431161.2016.1244366

Cited by 22 publications

(7 citation statements)

References 58 publications

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“…Not the same as the public blockchain, the consortium blockchain ordinarily use an agreement system like the 2PBFT calculation, the Raft calculation and so forth., instead of Proof of Work (PoW), Proof of Stake (PoS), and so forth. Among the agreement systems, the PBFT calculation beats others on account of its incredible execution in Byzantine adaptation to non-critical failure (Moosavi et al 2016). Nonetheless, it likewise faces a few issues, e.g., high correspondence overhead.…”

Section: Wireless Monitoring Architecture Incorporated With Artificial Intelligencementioning

confidence: 99%

Wireless network upgraded with artificial intelligence on the data aggregation towards the smart internet applications

Priyanka

Thangavel

Sagayam

et al. 2021

Int J Syst Assur Eng Manag

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In the modern evolution, WSN (Wireless Sensor Network) incorporated with data aggregation platform which involves stimulating research area with various modern upgradation of AI (Artificial Intelligence). Many types of research are carried out by undertaking variety of Deep Learning Network and Fuzzy based data aggregation techniques in the interpretation of Wireless Sensor Circumstance. The focal theme of the proposed research paper is to analyze the present concentrated work on Artificial Intelligence-accompanied data aggregation paradigm in wireless communication by elaborating the integration framework. By this proposed AI with data aggregation wireless sensor system has upgraded the innovation with high empowering pillars in analyzing the data with more processing and interpretations. Since it also improves the data transmission rate by providing more security and encryption schemes to the preprocessed data storing and streaming ton the bandwidth channels. This paper enumerates the AI contribution on the computing platform with the various advancement of blockchain technology schematic framework and further its case study experimentation on the aero-engine applications. Further experimental results are provided with numerical analysis by showing the traditional and advanced-Long term Memory technique prediction results by taking aero-engine wireless sensor network environment through MATLAB simulation.

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Section: Wireless Monitoring Architecture Incorporated With Artificial Intelligencementioning

confidence: 99%

Wireless network upgraded with artificial intelligence on the data aggregation towards the smart internet applications

Priyanka

Thangavel

Sagayam

et al. 2021

Int J Syst Assur Eng Manag

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show abstract

“…It should be particularly stressed that the models developed in this study have been successfully optimized by various heuristic algorithms in diverse research fields (e.g., hydrological prediction, wind speed and solar radiation forecasting). For instance, Baghban et al [63] used genetic algorithm to tune, optimize, and determine the respective key parameters of ANFIS and SVM methods for predicting the dew point temperature of moist air and obtained excellent results; Moosavi et al [64] utilized particle swarm optimization algorithm in conjunction with ANFIS and SVM approaches to estimate the surface soil moisture and respectively improved the predictive performance of original ANFIS and SVM models without using optimization algorithm. Consequently, to improve the modeling accuracy of carbon fluxes in the present work, further studies can be devoted to optimizing the machine learning models by the aid of different meta-heuristic algorithms.…”

Section: Capability Of Machine Learning Models and Their Comparisonmentioning

confidence: 99%

Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

2018

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Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the scientific community aiming to gain new insights into the temporal and spatial variation of different carbon fluxes in terrestrial ecosystems. In this study, adaptive neuro-fuzzy inference system (ANFIS) and generalized regression neural network (GRNN) models were developed to predict the daily carbon fluxes in three boreal forest ecosystems based on eddy covariance (EC) measurements. Moreover, a comparison was made between the modeled values derived from these models and those of traditional artificial neural network (ANN) and support vector machine (SVM) models. These models were also compared with multiple linear regression (MLR). Several statistical indicators, including coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), bias error (Bias) and root mean square error (RMSE) were utilized to evaluate the performance of the applied models. The results showed that the developed machine learning models were able to account for the most variance in the carbon fluxes at both daily and hourly time scales in the three stands and they consistently and substantially outperformed the MLR model for both daily and hourly carbon flux estimates. It was demonstrated that the ANFIS and ANN models provided similar estimates in the testing period with an approximate value of R2 = 0.93, NSE = 0.91, Bias = 0.11 g C m−2 day−1 and RMSE = 1.04 g C m−2 day−1 for daily gross primary productivity, 0.94, 0.82, 0.24 g C m−2 day−1 and 0.72 g C m−2 day−1 for daily ecosystem respiration, and 0.79, 0.75, 0.14 g C m−2 day−1 and 0.89 g C m−2 day−1 for daily net ecosystem exchange, and slightly outperformed the GRNN and SVM models. In practical terms, however, the newly developed models (ANFIS and GRNN) are more robust and flexible, and have less parameters needed for selection and optimization in comparison with traditional ANN and SVM models. Consequently, they can be used as valuable tools to estimate forest carbon fluxes and fill the missing carbon flux data during the long-term EC measurements.

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“…Due to the application of equality constraints, the solution process is transformed from a quadratic programming problem into a system of linear equations obtained by the Karush-Kuhn-Tucker (KKT) condition, which greatly reduces the solution difficulty. The LS-SVR model has been improved for different application fields by dividing spatiotemporal factors into different groups [18], allocating various weights to different data [19] and combining the model with optimization algorithms such as the genetic algorithm (GA) [5,7,20], particle swarm optimization (PSO) [6,[21][22][23][24] and artificial bee colony (ABC) [25] to achieve automatic optimal parameter selection. This improvement of the LS-SVR model adopts new ways in variable integration or parameter selection, but no improvement is made according to the essential characteristics of spatial lattice data.…”

Section: Introductionmentioning

confidence: 99%

Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix

Wang

Che³

et al. 2021

IJGI

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Due to the increasingly complex objects and massive information involved in spatial statistics analysis, least squares support vector regression (LS-SVR) with a good stability and high calculation speed is widely applied in regression problems of geospatial objects. According to Tobler’s First Law of Geography, near things are more related than distant things. However, very few studies have focused on the spatial dependence between geospatial objects via SVR. To comprehensively consider the spatial and attribute characteristics of geospatial objects, a geospatial LS-SVR model for geospatial data regression prediction is proposed in this paper. The 0–1 type and numeric-type spatial weight matrices are introduced as dependence measures between geospatial objects and fused into a single regression function of the LS-SVR model. Comparisons of the results obtained with the proposed and conventional models and other traditional models indicate that fusion of the spatial weight matrix can improve the prediction accuracy. The proposed model is more suitable for geospatial data regression prediction and enhances the ability of geospatial phenomena to explain geospatial data.

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Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches

Cited by 22 publications

References 58 publications

Wireless network upgraded with artificial intelligence on the data aggregation towards the smart internet applications

Wireless network upgraded with artificial intelligence on the data aggregation towards the smart internet applications

Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix

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