An Efficient Agro-Meteorological Model for Evaluating and Forecasting Weather Conditions Using Support Vector Machine

Sankaralingam, Baghavathi Priya; Sarangapani, Usha; Thangavelu, Ravichandran

doi:10.1007/978-3-319-30927-9_7

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…Timely and accurate weather prediction can save us from weather disasters [2]. Weather prediction represents the scientific method of forecasting the state of atmosphere depending on time and certain locations [3]. Numerical Weather Prediction (NWP) utilizes computer algorithm for prediction based on the present weather condition by resolving a huge system of non-linear arithmetical equations on certain arithmetical model.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter Tuned Bidirectional Gated Recurrent Neural Network for Weather Forecasting

Manikandan¹,

Nagaraj²

2022

Intelligent Automation &Amp; Soft Computing

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Weather forecasting is primarily related to the prediction of weather conditions that becomes highly important in diverse applications like drought discovery, severe weather forecast, climate monitoring, agriculture, aviation, telecommunication, etc. Data-driven computer modelling with Artificial Neural Networks (ANN) can be used to solve non-linear problems. Presently, Deep Learning (DL) based weather forecasting models can be designed to accomplish reasonable predictive performance. In this aspect, this study presents a Hyper Parameter Tuned Bidirectional Gated Recurrent Neural Network (HPT-BiGRNN) technique for weather forecasting. The HPT-BiGRNN technique aims to utilize the past weather data for training the BiGRNN model and achieve the effective forecasts with minimum time duration. The BiGRNN is an enhanced version of Gated Recurrent Unit (GRU) that follows the process of passing input via forward and backward neural network and the outputs are linked to the identical output layer. The BiGRNN technique includes several hyper-parameters and hence, the hyperparameter optimization process takes place using Bird Mating Optimizer (BMO). The design of BMO algorithm for hyperparameter optimization of the BiGRNN, particularly for weather forecast shows the novelty of the work. The BMO algorithm is used to set hyperparameters such as momentum, learning rate, batch size and weight decay. The experimental result the HPT-BiGRNN approach has resulted in a lower RMSE of 0.173 whereas the Fuzzy-GP, Fuzzy-SC, MLP-ANN and RBF-ANN methods have gained an increased RMSE of 0.218, 0.216, 0.202 and 0.245 respectively.

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Section: Introductionmentioning

confidence: 99%

Hyperparameter Tuned Bidirectional Gated Recurrent Neural Network for Weather Forecasting

Manikandan¹,

Nagaraj²

2022

Intelligent Automation &Amp; Soft Computing

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“…Traditional encryption-centered methods are nevertheless restrained by means of memory size and face dilapidation of network performance and lessening its lifetime. Elliptic Curve Cryptography (ECC) is utilized for its ability to render high security with short key size [19] to trounce the conventional encryption schemes' complexity concerning storage space in WSN. Thus, an effectual trust-centered CH selection for safe DA and Modified ECC centered cryptography for safe data transmission in WSN is proposed.…”

Section: Introductionmentioning

confidence: 99%

DAMFO-Based Optimal Path Selection and Data Aggregation in WSN

Mercy¹,

Mathana²,

Jasmine³

2022

Intelligent Automation &Amp; Soft Computing

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Wireless Sensor Network (WSN) encompasses several tiny devices termed as Sensor Nodes (SN) that have restriction in resources with lower energy, memory, together with computation. Data Aggregation (DA) is required to optimize WSN for secured data transmission at Cluster Head (CH) together with Base Station (BS). With regard to the Energy Efficiency (EE) along with the privacy conservation requirements of WSN in big-data processing and aggregation, this paper proposed Diversity centered Adaptive Moth-Flame Optimization (DAM-FO) for Optimal Path Selection (OPS) and DA in WSN. In the proposed work, initially, the Trust Evaluation (TE) process is performed. The Pompeiu Distance-centered Fuzzy C-Means (PDFCM) is employed for Cluster Formation (CF) in addition to Cluster Head Selection (CHS) and then DAMFO algorithm chooses the optimal path to gather the data together with cluster centroids. The DHECC algorithm then generates keys and encrypts the aggregated data. The encrypted data is finally passed on to the BS. The experimentation outcomes exhibited that the proposed algorithm outweighs the traditional methods with respect to Energy Consumption (EC) 6.35 J, Packet Delivery Ratio (PDR) of 93%, Throughput of 0.956 bps, end-to-end delay 6.547 s, together with a lifetime of networks. Additionally, the proposed system exhibits the best Security Level (SL) of 94.2% amid the transmission.

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Ontology Based Recommendation System for Predicting Cultivation and Harvesting Timings Using Support Vector Regression

Osman

El-Bendary

Fakharany

et al. 2020

Software Engineering Perspectives in Intelligent Systems

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An Efficient Agro-Meteorological Model for Evaluating and Forecasting Weather Conditions Using Support Vector Machine

Cited by 7 publications

References 5 publications

Hyperparameter Tuned Bidirectional Gated Recurrent Neural Network for Weather Forecasting

Hyperparameter Tuned Bidirectional Gated Recurrent Neural Network for Weather Forecasting

DAMFO-Based Optimal Path Selection and Data Aggregation in WSN

Ontology Based Recommendation System for Predicting Cultivation and Harvesting Timings Using Support Vector Regression

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