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  • Open Access

    ARTICLE

    Improvements in Weather Forecasting Technique Using Cognitive Internet of Things

    Kaushlendra Yadav*, Anuj Singh, Arvind Kumar Tiwari

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3767-3782, 2023, DOI:10.32604/csse.2023.033991 - 03 April 2023

    Abstract Forecasting the weather is a challenging task for human beings because of the unpredictable nature of the climate. However, effective forecasting is vital for the general growth of a country due to the significance of weather forecasting in science and technology. The primary motivation behind this work is to achieve a higher level of forecasting accuracy to avoid any damage. Currently, most weather forecasting work is based on initially observed numerical weather data that cannot fully cover the changing essence of the atmosphere. In this work, sensors are used to collect real-time data for a… More >

  • Open Access

    ARTICLE

    Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

    Hadil Shaiba1, Radwa Marzouk2, Mohamed K Nour3, Noha Negm4,5, Anwer Mustafa Hilal6,*, Abdullah Mohamed7, Abdelwahed Motwakel6, Ishfaq Yaseen6, Abu Sarwar Zamani6, Mohammed Rizwanullah6

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3367-3382, 2022, DOI:10.32604/cmc.2022.030067 - 16 June 2022

    Abstract The agricultural sector’s day-to-day operations, such as irrigation and sowing, are impacted by the weather. Therefore, weather constitutes a key role in all regular human activities. Weather forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from disasters. Rainfall, wind speed, humidity, wind direction, cloud, temperature, and other weather forecasting variables are used in this work for weather prediction. Many research works have been conducted on weather forecasting. The drawbacks of existing approaches are that they are less effective, inaccurate, and time-consuming. To overcome these issues,… More >

  • Open Access

    ARTICLE

    Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning

    Jianbing Ma1,*, Xianghao Cui1, Nan Jiang2

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1939-1949, 2022, DOI:10.32604/cmc.2022.025206 - 24 February 2022

    Abstract Sudden precipitations may bring troubles or even huge harm to people's daily lives. Hence a timely and accurate precipitation nowcasting is expected to be an indispensable part of our modern life. Traditionally, the rainfall intensity estimation from weather radar is based on the relationship between radar reflectivity factor (Z) and rainfall rate (R), which is typically estimated by location-dependent experiential formula and arguably uncertain. Therefore, in this paper, we propose a deep learning-based method to model the ZR relation. To evaluate, we conducted our experiment with the Shenzhen precipitation dataset. We proposed a combined method More >

  • Open Access

    ARTICLE

    Hyperparameter Tuned Bidirectional Gated Recurrent Neural Network for Weather Forecasting

    S. Manikandan1,*, B. Nagaraj2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 761-775, 2022, DOI:10.32604/iasc.2022.023398 - 08 February 2022

    Abstract 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… More >

  • Open Access

    ARTICLE

    Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

    Sathish Babu Pandu1,*, A. Sagai Francis Britto2, Pudi Sekhar3, P. Vijayarajan4, Amani Abdulrahman Albraikan5, Fahd N. Al-Wesabi6, Mesfer Al Duhayyim7

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 109-124, 2022, DOI:10.32604/cmc.2022.021015 - 03 November 2021

    Abstract Solar energy has gained attention in the past two decades, since it is an effective renewable energy source that causes no harm to the environment. Solar Irradiation Prediction (SIP) is essential to plan, schedule, and manage photovoltaic power plants and grid-based power generation systems. Numerous models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of time. In this scenario, commonly available Artificial Intelligence (AI) technique can be trained over past values of irradiance as well as weather-related parameters… More >

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