Comparative Analysis of Temperature Prediction Using Regression Methods and Back Propagation Neural Network

Sharaff, Aakanksha; Roy, Samuel Robin

doi:10.1109/icoei.2018.8553803

Cited by 24 publications

(13 citation statements)

References 10 publications

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“…In 2018, temperature prediction models were investigated by comparing different machine learning methods such as linear regression, regression trees, and BPN (Sharaff and Roy 2018). The results show that the BPN with proper configuration produces a significantly better prediction.…”

Section: Related Workmentioning

confidence: 99%

Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station

et al. 2020

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Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10-20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.Soft Computing https://doi.org/10.1007/s00500-020-04954-0( 0123456789().,-volV) (0123456789(). ,-volV)Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. P. Hewage et al.

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Section: Related Workmentioning

confidence: 99%

Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station

et al. 2020

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“…In [9], a nonlinear method as Artificial Neural Network (ANN) network's compared with regression methods for daily temperature prediction. The dataset used for this work is collected in Mumbai.…”

Section: Literature Reviewmentioning

confidence: 99%

Air Temperature Prediction Using Different Datamining Approaches In Sulaymaniyah City In Iraq

Salih

Murad

Tahir

2021

passer

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Climate change has a historical impact at universal and local levels over the past era. Climate change is one of the greatest challenge issues in the globe for meteorological research. Air temperature estimation, in particular, has been measured as a significant feature in weather impression studies on industrial sectors, environmental, ecological, and agricultural. Accurately predicting air temperature guides to measure lifestyle, perform a key character for the government, industries, and public in development activities. In this paper, we investigate the use of various data mining approaches such as Support Vector Machine (SVM), Decision tree (DT), and Naïve Bayes for air temperature prediction within Sulaymaniyah City in Kurdistan, IRAQ. The metrological data is collected from the local Weather Forecast Department in the city within the range 2013 to 2018 inclusive. A dataset for the metrological data was developed and used to train the data mining algorithms. The proposed data mining algorithms were tested on the dataset to predict the air temperature and the performance of these algorithms were compared using standard performance metrics. Support vector machine has accomplished promising performance among using algorithms

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“…As a rule, regression and autoregressive methods are mainly used to predict the minimum, maximum, or average parameters of a time series, for example, air temperature [34]. These methods do not have sufficient accuracy in comparison with the other two groups of methods [35,36]. Therefore, in many cases, applying methods based on neural networks and support vector machines is preferable for modeling and predicting.…”

Section: Technique and Tools For Air Temperature Predictionmentioning

confidence: 99%

Integration of Web Processing Services with Workflow-Based Scientific Applications for Solving Environmental Monitoring Problems

Feoktistov¹,

Gorsky²,

Kostromin³

et al. 2021

IJGI

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Nowadays, developing and applying advanced digital technologies for monitoring protected natural territories are critical problems. Collecting, digitalizing, storing, and analyzing spatiotemporal data on various aspects of the life cycle of such territories play a significant role in monitoring. Often, data processing requires the utilization of high-performance computing. To this end, the paper addresses a new approach to automation of implementing resource-intensive computational operations of web processing services in a heterogeneous distributed computing environment. To implement such an operation, we develop a workflow-based scientific application executed under the control of a multi-agent system. Agents represent heterogeneous resources of the environment and distribute the computational load among themselves. Software development is realized in the Orlando Tools framework, which we apply to creating and operating problem-oriented applications. The advantages of the proposed approach are in integrating geographic information services and high-performance computing tools, as well as in increasing computation speedup, balancing computational load, and improving the efficiency of resource use in the heterogeneous distributed computing environment. These advantages are shown in analyzing multidimensional time series.

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Comparative Analysis of Temperature Prediction Using Regression Methods and Back Propagation Neural Network

Cited by 24 publications

References 10 publications

Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station

Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station

Air Temperature Prediction Using Different Datamining Approaches In Sulaymaniyah City In Iraq

Integration of Web Processing Services with Workflow-Based Scientific Applications for Solving Environmental Monitoring Problems

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