Comparison of Decision Tree Based Rainfall Prediction Model with Data Driven Model Considering Climatic Variables

Ramsundram, N; Sathya, S.; Karthikeyan, S.

doi:10.4172/2168-9768.1000175

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…This procedure disrupts the integrity of the data by causing data leakage, whereby information from the testing set is introduced into the training set. Other papers make no attempt to account for seasonality [18,22,23,46,48,61,68,69]. Evidently, there is no consistent procedure for dealing with the seasonality aspect of the data; this is one point that we address in this paper.…”

Section: Literature Review and Scope Of The Researchmentioning

confidence: 97%

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

et al. 2021

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Machine learning (ML) has been utilized to predict climatic parameters, and many successes have been reported in the literature. In this paper, we scrutinize the effectiveness of five widely used ML algorithms in the monthly prediction of seasonal climatic parameters using monthly image data. Specifically, we quantify the predictive performance of these algorithms applied to five climatic parameters using various combinations of features. We compare the predictive accuracy of the resulting trained ML models to that of basic statistical estimators that are computed directly from the training data. Our results show that ML never significantly outperforms the statistical baseline, and underperforms for most feature sets. Unlike previous similar studies, we provide error bars for the relative performance of different predictors based on jackknife estimates applied to differences in predictive error magnitudes. We also show that the practice of shuffling data sequences which was employed in some previous references leads to data leakage, resulting in over-estimated performance. Ultimately, the paper demonstrates the importance of using well-grounded statistical techniques when producing and analyzing the results of ML predictive models.

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Section: Literature Review and Scope Of The Researchmentioning

confidence: 97%

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

et al. 2021

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Experimental Analysis of Precipitation Forecasting Using Machine Learning and Distributed Machine Learning Approach

Balaji,

Sivagami,

Mohan

2024

Advances in Geographical and Environmental Sciences

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A Study on Distributed Machine Learning Techniques for Large-Scale Weather Forecasting

Balaji V.,

Sivagami M.

2023

Scalable and Distributed Machine Learning and Deep Learning Patterns

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The weather data generated, processed, and collected by the sensor or shared by IoT devices and mobile devices has significantly increased weather data collection in daily life. The data generation speed also accelerated, and a vast amount of data has been collected and stored in distributed databases, improving weather forecasting. Still, the conventional processing method for massive data is distributed and centralized computing, and this chapter looks into how distributed machine learning techniques help as to increase the processing speed. Some distributed frameworks that play a significant role in massive data, like MapReduce, have been trained and tested to resolve various machine learning problems in a distributed environment. The aim of this chapter will provide different information about datasets, issues, platforms, and optimized approaches in a distributed environment. So, researchers can use and deploy new techniques in machine learning algorithms. It helps the researchers develop new strategies in distributed computing environments.

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Comparison of Decision Tree Based Rainfall Prediction Model with Data Driven Model Considering Climatic Variables

Cited by 3 publications

References 13 publications

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

Experimental Analysis of Precipitation Forecasting Using Machine Learning and Distributed Machine Learning Approach

A Study on Distributed Machine Learning Techniques for Large-Scale Weather Forecasting

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