Supervised Rainfall Learning Model Using Machine Learning Algorithms

Sharma, Amit; Chaurasia, Sandeep; Srivastava, Devesh Kumar

doi:10.1007/978-3-319-74690-6_27

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…The daily accuracy value is 67% and the monthly accuracy value is 88% with mapping results that are easy to read. Compared to previous studies [6], [7], [8], and [9] it only show prediction results, and does not display mapping. While research [10] and [11] only shows mapping that does not adjust the shape of the map.…”

Section: Mapping Resultscontrasting

confidence: 76%

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Prediction and Mapping Rainfall Classification Using Naive Bayes and Simple Kriging

Yoga

Prasetyowati

Sibaroni

2022

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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This study discusses the development of a prediction model for the classification of rainfall based on time in Java. The method used in this research is naive Bayes and simple kriging. Naive Bayes is used for classification prediction, while simple kriging is an interpolation method used for mapping. There are two scenarios used, that is building a prediction model for daily and monthly rainfall classification, with data taken from 27 weather stations on the island of Java from 2010 to 2021. The results obtained in the classification process are an accuracy value of 67% for the daily model and 88% for the monthly model. The daily model data uses a spherical semivariogram with an average RMSE of 1,021. For the monthly model data using a Gaussian semivariogram with an average RMSE of 0,34. Then interpolation using simple kriging for mapping rainfall. The results of this study are predictions for the classification and mapping of daily rainfall models from April 1 to April 7 2022 and monthly models from April to September 2022. The contribution of this research is to provide predictive information and mapping of future rainfall so that public people can anticipate more.

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Section: Mapping Resultscontrasting

confidence: 76%

“…However, in this study, only 5 parameters were used. In 2018, A. K. Sharma et al compared 4 methods for the prediction of rainfall in India [8]. One of the methods used is nave Bayes which obtains accuracy above 96% for each different area.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Mapping Rainfall Classification Using Naive Bayes and Simple Kriging

Yoga

Prasetyowati

Sibaroni

2022

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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show abstract

“…The detailed algorithms for tree-based ensemble models are provided in Appendix B. Generally speaking, ensemble learning methods are much more accurate and robust than individual decision tree models [71,72].…”

Section: Ensemble Learning Methodsmentioning

confidence: 99%

Relationship between Early-Stage Features and Lifetime Maximum Intensity of Tropical Cyclones over the Western North Pacific

Ren

Tang

2021

Atmosphere

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The relationship between early-stage features and lifetime maximum intensity (LMI) of tropical cyclones (TCs) over the Western North Pacific (WNP) was investigated by ensemble machine learning methods and composite analysis in this study. By selecting key features of TCs’ vortex attributes and environmental conditions, a two-step AdaBoost model demonstrated accuracy of about 75% in distinguishing weak and strong TCs at genesis and a coefficient of determination (R2) of 0.30 for LMI estimation from the early stage of strong TCs, suggesting an underlying relationship between LMI and early-stage features. The composite analysis reveals that TCs with higher LMI are characterized by lower latitude embedded in a continuous band of high low-troposphere vorticity, more compact circulation at both the upper and lower levels of the troposphere, stronger circulation at the mid-troposphere, a higher outflow layer with stronger convection, a more symmetrical structure of high-level moisture distribution, a slower translation speed, and a greater intensification rate around genesis. Specifically, TCs with greater “tightness” at genesis may have a better chance of strengthening to major TCs (LMI ≥ 96 kt), since it represents a combination of the inner and outer-core wind structure related to TCs’ rapid intensification and eyewall replacement cycle.

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“…Specifically, the domain of health care is receiving considerable attention in research. Sharma and Rani, 2020 indicated that patients' IoT solutions are connected devices to a human's body to monitor health parameters such as body temperature, oxygen level, blood pressure, heart rate, breathing rate, etc.…”

Section: Background Literature Analysismentioning

confidence: 99%

Wearable IoT intelligent recommender framework for a smarter healthcare approach

Nia

Kazemi

Valmohammadi

et al. 2021

LHT

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PurposeThe increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.Design/methodology/approachThis data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.FindingsThe proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.Research limitations/implicationsThe research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.Practical implicationsThe emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.Originality/valueIn this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

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Supervised Rainfall Learning Model Using Machine Learning Algorithms

Cited by 10 publications

References 7 publications

Prediction and Mapping Rainfall Classification Using Naive Bayes and Simple Kriging

Prediction and Mapping Rainfall Classification Using Naive Bayes and Simple Kriging

Relationship between Early-Stage Features and Lifetime Maximum Intensity of Tropical Cyclones over the Western North Pacific

Wearable IoT intelligent recommender framework for a smarter healthcare approach

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