2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Год журнала: 2024, Номер unknown, С. 1 - 6
Опубликована: Июнь 24, 2024
Язык: Английский
International Journal on Advanced Science Engineering and Information Technology, Год журнала: 2024, Номер 14(1), С. 118 - 130
Опубликована: Фев. 14, 2024
In Indonesia, the agriculture industry has been more reluctant than other sectors to adopt IoT, IT, and AI technology. Utilizing this technology will enable precision agriculture. This research aims make implement an IoT-Webserver-Android Machine Learning-based soil PH factor monitoring tool system. The steps for making system are divided into three subsystems. first is a multiple sensors data acquisition subsystem, consisting of PH-Moisture, Temperature-Humidity, Sunlight. connected Arduino Uno microcontroller serial communication with ESP 8266 Wi-Fi module. second part subsystem local web application, which contains MySQL database page. third Android includes real-time Firebase application mobile display. results have implemented display expected outcomes. It clear from performance outcomes system's evaluation provide precise statistical values. Then, Learning analysis generates accurate prediction models. demonstrated that applicable favorable impact on factor. implication future should be added Nitrogen-Phosphorus-Potassium measure nutrients. Also, edge-analysis integrated in analyzing
Язык: Английский
Процитировано
5
Green Technologies and Sustainability, Год журнала: 2024, Номер 2(3), С. 100104 - 100104
Опубликована: Май 27, 2024
Rainfall is one of the remarkable hydrologic variables that directly connected to sustainable environment for any region over globe. The present study aims review different research papers on rainfall forecasting using artificial intelligence (AI) models including a bibliographic assessment most popular AI and comparison results based accuracy parameters. 39 journal papers, published in renowned international journals from 2000 2023, were studied extensively categorize modeling techniques, best models, characteristics input data, period variables, data division, so forth. Although certain drawbacks still exist, reviewed studies suggest may help simulate various geographic locations. In some cases, splitting mechanism was delivered model itself gets improved. recommendations will future researchers fill gaps, especially tuning hyperparameters while building training models. Hybrid advised cases minimize gap between simulated observed data. All aimed achieve resilient era climate change.
Язык: Английский
Процитировано
4
Earth Science Informatics, Год журнала: 2025, Номер 18(3)
Опубликована: Фев. 22, 2025
Язык: Английский
Процитировано
0
Smart innovation, systems and technologies, Год журнала: 2025, Номер unknown, С. 549 - 559
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Опубликована: Май 27, 2024
Язык: Английский
Процитировано
0
TecnoLógicas, Год журнала: 2024, Номер 27(60), С. e3017 - e3017
Опубликована: Июнь 27, 2024
La emisión de gases efecto invernadero, atribuida directa o indirectamente a la actividad humana, es principal causa del cambio climático nivel mundial. Entre los emitidos, el dióxido carbono (CO2) que más contribuye variación espacio temporal magnitudes físicas como humedad relativa, presión atmosférica, temperatura ambiente y, manera significativa, precipitación. El objetivo investigación fue presentar un análisis predicción precipitación mensual en departamento Boyacá mediante uso modelos basados aprendizaje reforzado (RL, por sus siglas inglés). metodología empleada consistió extraer datos desde CHIRPS 2,0 (Climate Hazards Group InfraRed Precipitation with Station data, versión 2,0) con una resolución espacial 0,05° posteriormente fueron preprocesados para implementación enfoques simulación Montecarlo y profundo (DRL, inglés) proporcionar predicciones mensual. Los resultados obtenidos demostraron DRL generan significativas Es esencial reconocer convencionales Aprendizaje profundo, Memoria Corto Plazo (LSTM) Redes Convolucionales (ConvLSTM), pueden superar términos precisión predicción. Se concluye técnicas refuerzo detecta patrones información ser usados soporte estrategias dirigidas mitigar riesgos económicos sociales derivados fenómenos climáticos.
Процитировано
0
Decision Analytics Journal, Год журнала: 2024, Номер 12, С. 100515 - 100515
Опубликована: Авг. 24, 2024
Rainfall prediction significantly impacts agriculture, water reserves, and preparations for flooding conditions. This research examines the performance interpretability of machine learning (ML) models rainfall in Republic Ireland. The study uses a brute force approach Leave One Feature Out (LOFO) methodology to evaluate model under highly correlated variables. Results reveal consistent across ML algorithms, with average Area Under Curve Precision-Recall (AUC-PR) scores ranging from 0.987 1.000, certain features such as atmospheric pressure soil moisture deficits demonstrating significant influence on outcomes.SHapley Additive exPlanations (SHAP) values provide insights into feature importance, reaffirming significance prediction. underscores importance selection enhancing accuracy usability
Язык: Английский
Процитировано
0
2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Год журнала: 2024, Номер unknown, С. 1 - 6
Опубликована: Июнь 24, 2024
Язык: Английский
Процитировано
0
2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Год журнала: 2024, Номер unknown, С. 1 - 5
Опубликована: Июнь 24, 2024
Язык: Английский
Процитировано
0
2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Год журнала: 2024, Номер unknown, С. 1 - 6
Опубликована: Июнь 24, 2024
Язык: Английский
Процитировано
0