Relevant bibliographies by topics / Keywords:- Humidity Prediction

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Academic literature on the topic 'Keywords:- Humidity Prediction'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Keywords:- Humidity Prediction"

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A, Nischal Alpe, P. Kulkarni Aditya, V. Ganesh, and AV Karthik. "COMPARATIVE ANALYSIS OF DATA MINING ALGORITHMS FOR HUMIDITY PREDICTION." Journal of Research and Review: Fuzzy Logic Design and Fuzzy Systems 1, no. 1 (2025): 27–33. https://doi.org/10.5281/zenodo.15193131.

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<em>Predicting humidity is critical in weather forecasting, agriculture, and HVAC systems. This study looks into the many methods used for humidity prediction, which range from basic statistical models to cutting-edge machine learning techniques. We go over their benefits and downsides, as well as methods for enhancing forecast accuracy and data processing. Finally, the research looks at future trends, such as hybrid models, which mix different approaches to better forecasting.</em>
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Liu, Tianhong, Xianzhu Qiao, Sixing Liu, and Shengli Qi. "Research on Greenhouse Environment Prediction Based on GCAKF-CNN-LSTM." Applied Engineering in Agriculture 40, no. 2 (2024): 181–87. http://dx.doi.org/10.13031/aea.15867.

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Highlights A GCAKF-CNN-LSTM model is proposed for greenhouse temperature and humidity forecasting. The grey correlation analysis is used to select the most relevant variables. Kalman filter is applied for denoising to improve the data quality. The proposed model achieves higher forecasting accuracy with the lowest forecasting errors. Abstract. Accurate prediction of temperature and humidity in the greenhouse environment is helpful to regulate the environment and promote crop growth. Aiming at the characteristics of nonlinear and strong coupling in the greenhouse environment, this article propo
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GÖKDENİZ, KADİR, and ERKAN BOSTANCI. "Indoor Air Quality Predictions For Automation." Journal of Artificial Intelligence and Human Sciences 1, no. 1 (2024): 56–66. https://doi.org/10.5281/zenodo.14670650.

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<strong>Abstract: </strong>This study examines the implementation of home automation systems to predict indoor air quality using real-time data such as temperature, humidity, pressure, occupancy status, energy consumption, and window conditions. Due to the superior pattern recognition performance of recurrent neural networks, the study employs deep learning techniques for air quality prediction. A comparative analysis of GRU, LSTM and BiGRU models highlights GRU&rsquo;s superior performance across various metrics, emphasizing its generalization capability. The study also introduces an Air-Smar
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Dhwani, N., P. Pranay, V. V. Haragopal, and T. Manjusha. "Year-wise Longitudinal Comparative Analysis of Temperature Prediction Models for Hyderabad (2017–2024)." Indian Journal Of Science And Technology 18, no. 27 (2025): 2179–84. https://doi.org/10.17485/ijst/v18i27.559.

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Objective: The study aims to compare and analyze temperature forecasting models for Hyderabad from 2017 to 2024 in the context of modeling long-term accuracy and the influence of critical meteorological conditions on temperature. Methods: Past meteorological data, including temperature, humidity, solar radiation, wind speed, and precipitation, were analyzed. Five models for predicting were applied: Multiple Linear Regression, Decision Tree Regression, Random Forest Regression, Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM) Neural Networks. A chi-squared test determined the
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Zheng, Muzi, Brian Leib, Wesley Wright, and Paul Ayers. "Neural Models to Predict Temperature and Natural Ventilation in a High Tunnel." Transactions of the ASABE 62, no. 3 (2019): 761–69. http://dx.doi.org/10.13031/trans.12781.

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Abstract. As a response to the rising demand for local food, high tunnels (HTs) can help small producers become more profitable through crop protection and extension of the growing season. Proper ventilation that responds to changes in outside weather conditions can remove excess heat and humidity inside HTs and lead to better solar energy utilization while maintaining a favorable growth environment. Rather than depending on complex mathematical models, this study investigated an artificial neural network (ANN) for predicting the inside air temperature and ventilation rate of a HT. Energy bala
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Joshy, Ms S. Agnes, and S. Karthika. "Rainfall Prediction Using Machine Learning with Web Deployment." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–7. https://doi.org/10.55041/ijsrem43869.

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Rainfall prediction plays a crucial role in agriculture, disaster management, and water resource planning. Traditional forecasting methods rely on statistical techniques, which may not effectively capture the complex patterns of weather data. This project utilizes machine learning (ML) techniques to enhance the accuracy of rainfall prediction. Various ML algorithms such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks are employed to analyse historical weather data, including temperature, humidity, wind speed, and atmospheric pressure. The proposed system preproce
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Kumar, Vikas, Vishal Kumar Yadav, and Er Sandeep Dubey. "Rainfall Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2494–97. http://dx.doi.org/10.22214/ijraset.2022.42876.

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Abstract: In India, Agriculture contributes major role to Indian economy. For agriculture, Rainfall is important but during these days’ rainfall prediction has become a major challenging problem. Good prediction of rainfall provides knowledge and know in advance to take precautions and have better strategy about theirs crops. Global warming is also having severe effect on nature as well as mankind and it accelerates the change in climatic conditions. Because of its air is getting warmer and level of ocean is rising, leads to flood and cultivated field is changing into drought. Due to adverse c
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Li, Yueh-Jung, Kuang-Wen Hsieh, Suming Chen, and Perng-Kwei Lei. "Establishing Modularized Environmental Measurements and Analyzing and Applying Environmental Information for a Windowless Broiler House in Taiwan." Transactions of the ASABE 62, no. 2 (2019): 303–13. http://dx.doi.org/10.13031/trans.12984.

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Abstract. The purpose of this study was to apply wireless sensor modules to measure the environmental conditions in a windowless broiler house. An environmental index monitoring system was established that automatically estimated the temperature-humidity index (THI) and temperature-humidity-velocity index (THVI) according to the collected database. Finally, prediction of the relationships between the THI or THVI and the feed conversion rate (FCR) was studied. Nine sets of temperature, relative humidity, and wind velocity sensors were used for measuring the inside environment. An additional set
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Mariano, Everson Batista, Enilson Palmeira Calvacanti, and Herika Pereira Rodrigues. "Avaliação do BRAMS na Previsão Numérica de Temperatura e Umidade Relativa do Ar para o Estado da Paraíba (Evaluation of BRAMS in Numerical Prediction of Temperature and Relative Humidity in State of Paraiba)." Revista Brasileira de Geografia Física 4, no. 3 (2011): 463. http://dx.doi.org/10.26848/rbgf.v4i3.232736.

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Neste trabalho foram avaliadas as previsões numéricas feitas pelo modelo numérico Brazilian Developments on the Regional Atmospheric Model System – BRAMS conforme execução em produção na UFCG. Foram analisadas as variáveis meteorológicas: temperatura do ar e umidade relativa do ar em superfície. A comparação foi feita com dados coletados em 19 estações meteorológicas automáticas, espalhadas pelos estados da Paraíba, Pernambuco e alagoas, pertencentes ao Instituto Nacional de Meteorologia - INMET. Os resultados mostram que os valores de temperatura do ar observados e estimados pelo modelo BRAMS
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Rhea Sriniwas, Dr. "Crop Harvesting using Machine Learning (ML) and Internet of Things (IOT)." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem02803.

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Accurate crop prediction is essential for optimizing agricultural productivity and ensuring food security, particularly in regions where farming decisions are heavily influenced by soil and climatic conditions. Traditional crop recommendation systems often lack real-time adaptability and the ability to integrate diverse data sources such as soil sensors and live weather feeds. This project introduces a real-time crop prediction system that leverages a Support Vector Machine (SVM) classifier for precise and reliable crop recommendations. The system integrates heterogeneous data inputs, includin
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Conference papers on the topic "Keywords:- Humidity Prediction"

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Kaur, Amanat, Yvan Gariépy, Valérie Orsat, and Vijaya Raghavan. "Microwave assisted fluidized bed drying of celery." In 21st International Drying Symposium. Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/ids2018.2018.7368.

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The drying kinetics of celery in a microwave assisted fluidized bed dryer was studied at different drying air temperatures (45°C, 55°C and 65°C) and at different initial microwave power densities (0W/g, 1W/g and 2W/g). Dried product quality, product mass, air temperature, air relative humidity, and electric power consumption were used to monitor the performance of the drying process. The results showed that the Midilli-Kucuk model was best in predicting the moisture ratio as a function of drying time. At any given temperature, the utilization of the microwave energy reduced by more than 50% th
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