IOT based prediction of rainfall forecast in coastal regions using deep reinforcement model

Nithyashri, J.; Poluru, Ravi Kumar; Balakrishnan, Subramanian; Kumar, M. Ashok; Prabu, P.; Nandhini, S.

doi:10.1016/j.measen.2023.100877

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…The evaluation value of the nonensemble algorithm model is calculated using the root mean square error (RMSE). According [55], the RMSE is the total of the squared root that are present between the predictions made by the model and the observation data. The RMSE helps in transforming the squared error into the original units of predictions by taking the square root of the squared score [56], [57].…”

Section: ) Displays the Training Dataset's Dimensionsmentioning

confidence: 99%

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Sumarsono,

Farida Afiatna,

Muflihah

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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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 to make and implement an IoT-Webserver-Android and Machine Learning-based soil PH factor monitoring tool system. The steps for making the tool system are divided into three subsystems. The first is a multiple sensors data acquisition subsystem, consisting of sensors for soil PH-Moisture, Temperature-Humidity, and Sunlight. The sensors are connected to the Arduino Uno microcontroller for serial communication with the ESP 8266 microcontroller for the Wi-Fi module. The second part is the monitoring subsystem with the local web application, which contains a MySQL database and a local web page. The third part is the monitoring subsystem with the Android application, which includes a real-time Firebase database and the application for real-time and mobile data display. The results have been implemented and display the expected outcomes. It is clear from the performance of the three subsystems. The outcomes of the tool system's data evaluation provide precise statistical values. Then, Machine Learning analysis generates accurate soil PH prediction models. It has been demonstrated that the monitoring system is applicable and has a favorable impact on data soil PH factor. The implication for the future is that this monitoring system should be added with Nitrogen-Phosphorus-Potassium sensors to measure soil nutrients. Also, the system added edge-analysis to be integrated in monitoring and analyzing soil nutrients.

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Section: ) Displays the Training Dataset's Dimensionsmentioning

confidence: 99%

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Sumarsono,

Farida Afiatna,

Muflihah

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Penelitian sebelumnya yang berkaitan dengan rainfall classification -prediction telah banyak dilakukan, baik yang menggunakan satu metode machine learning, maupun mengkombinasikan beberapa metode. [34] mengusulkan sebuah teknik berbasis IoT untuk memprediksi ramalan curah hujan di daerah pesisir India menggunakan jaringan Long Short-Term Memory (LSTM) untuk menangkap ketergantungan temporal antara data curah hujan yang dikumpulkan dari daerah pesisir dan parameter model prediksi. Hasil penelitian ini mempredikasi curah hujan dengan akurasi rata-rata 89% menggunakan model yang diusulkan menyatakan bahwa model yang diusulkan efektif untuk memprediksi curah hujan di daerah pesisir.…”

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Bibliometric Study : Rainfall Classification - Prediction using Machine Learning Methods

Riza,

Nuryadi

2023

Digitalzone

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This Research aims to review the machine learning methods used for classifying or predicting rainfall, using various features from existing data. The study used a bibliometric approach to search for metadata related to rainfall classification and prediction studies using machine learning keywords in Scopus journals. There found 94 metadata articles stored in a Comma Separated Values (CSV) file. The data in this article used published articles from 2014 to 2023 with relevant topics. The study provides information on the latest machine learning methods used for classifying or predicting rainfall. The findings of the study include an increase in published articles by 221.43% from 2018 to 2022. The article titled "An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives" by Cramer S., Kampouridis M, Freitas A.A, and Alexandridis A.K received the highest citation count of 129. The study also classified 15 keywords into 3 clusters, with common and fewer keywords. India emerged as the country with the most publications on classifying or predictiong rainfall, and the subject areas of computer science and engineering dominated the distribution of articles. Developing the use of deep learning methods and adding feature extraction algorithms in selecting features used to model data can improve the efficiency and accuracy of the rainfall classification - prediction process. The development of research data using radar images with the type of image processing research can also be maximised for research related to classification - prediction of rainfall using machine learning methods.

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Results with the Use of Artificial Intelligence with OpenAI Applied to Internet of Things Classes

Bento,

Yolanda Torres-Torres,

Camacho-Léon

2024

2024 12th International Conference on Smart Grid (icSmartGrid)

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IOT based prediction of rainfall forecast in coastal regions using deep reinforcement model

Cited by 4 publications

References 11 publications

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Bibliometric Study : Rainfall Classification - Prediction using Machine Learning Methods

Results with the Use of Artificial Intelligence with OpenAI Applied to Internet of Things Classes

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