Weather prediction using random forest machine learning model

Meenal, R.; Michael, Prawin Angel; Pamela, D.; Ekambaram, Rajasekaran

doi:10.11591/ijeecs.v22.i2.pp1208-1215

Cited by 35 publications

(16 citation statements)

References 17 publications

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“…To date, more than hundreds of machine learning algorithms can be utilized for various domains of problem such as Decision Tree [23], Random Forest [24], Support Vector Machine [24] and Gradient Decision Trees [23]. Decision Tree, Random Forest and Gradient Decision Trees are categorized as tree-based machine learning.…”

Section: Literature Reviewmentioning

confidence: 99%

Tree-based Machine Learning and Deep Learning in Predicting Investor Intention to Public Private Partnership

Amin¹,

Rahmawaty²,

Lautania³

et al. 2023

IJACSA

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Public private partnership (PPP) is the government initiate in accelerating public infrastructure development growth. However, the scheme exposes private sector to various risks including political risk which in turn affect financial performance and reporting of participating firms. Given that one of the issues facing the government is the lack of participation from the private sector in such arrangements. Thus, the main objective of this study is to observe the machine learning prediction models on private investor intention in participating the PPP program. Tree-based machine learning and deep learning are two different types of promising algorithms, which proven to be useful in widely domain of prediction problems but never been tested on the concerned problem of this study. Based on real data of investors for Indonesian listed firms, this paper presents the ability of the selected machine learning algorithms by means of different assessments point of view. First assessment is on the algorithms' performances in producing accurate prediction. Second assessment is to identify the variance of PPP attributes in each of the prediction model with the machine learning algorithms. The performance results show that all the prediction models with the machine learning algorithms and the PPP attributes were well-fitted at R squared above 80%. The findings contribute a significant knowledge to various fields of scholars to implement a more in-depth analysis on the machine learning methods and investors' prediction.

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Section: Literature Reviewmentioning

confidence: 99%

Tree-based Machine Learning and Deep Learning in Predicting Investor Intention to Public Private Partnership

Amin¹,

Rahmawaty²,

Lautania³

et al. 2023

IJACSA

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“…The central idea of MDA is to develop models, first of analysis then of design, until the code, by successive transformations, derivations, and enrichments. In this approach, the model is used to represent all layers of an application, including data exchange with source systems, application objects and their methods, artificial intelligence (AI) machine learning algorithms [10]- [13], and the application's user interface. Each of these layers can also be accessed as microservices.…”

Section: Introductionmentioning

confidence: 99%

Approach by modeling to generate an e-commerce web code from laravel model

Rahmouni

Bouzaidi

Mbarki

2023

IJEECS

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The world of web is constantly evolving. Today, we no longer speak of a website but of a web application. The growing difficulty of designing web applications has given rise to solutions and tools. The framework is one of them. Providing a serious framework for development by offering strict development rules, as well as generic and out-of-the-box components, PHP laravel framework is one of them. This paper aims to present the different stages of modeling and development of an enterprise resource planning (ERP) system that will be implemented by PHP laravel by defining the main concepts involved in this information system modeling and development process. To lead and implement the end-to-end development of the said system, we apply the model-driven architecture (MDA) approach. This approach is based on atlas transformation language (ATL). The result of this paper is an Ecore file, a reliable MVC2 web model of e-commerce ERP, which will be the input file to generate the aforementioned system code. To validate this approach, we implemented a case study. The result of this work is very satisfying. Thus, we arrived to generate all necessary elements for this ERP code generation by respecting the MVC2 model and the PHP coding.

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“…Artificial neural network (ANN) based multilayer perceptron model [4], [5] with Levenberg-Marquardt algorithm was proposed to forecast 24 hours ahead solar Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752 ❒ 901 irradiance and found that the usage of meterological parameters as input variables gives more accuracy in forecast. Input variables with higher dimension [6]- [9] (up to 900 inputs) are used with ANN models of different architecture to predict short term global solar irradiance of 20% reduction in errors. Deep learning models are the subset of machine learning and these models on solar irradiance forecast results with higher accuracy comapared to machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Day-ahead solar irradiance forecast using sequence-to-sequence model with attention mechanism

Subramanian¹,

Gounder²,

Lingana³

2022

IJEECS

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<p>The increasing integration of distributed energy resources (DERs) into power grid makes it significant to forecast solar irradiance for power system planning. With the advent of deep learning techniques, it is possible to forecast solar irradiance accurately for a longer time. In this paper, day-ahead solar irradiance is forecasted using encoder-decoder sequence-to-sequence models with attention mechanism. This study formulates the problem as structured multivariate forecasting and comprehensive experiments are made with the data collected from National Solar Radiation Database (NSRDB). Two error metrics are adopted to measure the errors of encoder-decoder sequence-to-sequence model and compared with smart persistence (SP), back propagation neural network (BPNN), recurrent neural network (RNN), long short term memory (LSTM) and encoder-decoder sequence-to-sequence LSTM with attention mechanism (Enc-Dec-LSTM). Compared with SP, BPNN and RNN, Enc-Dec-LSTM is more accurate and has reduced forecast error of 31.1%, 19.3% and 8.5% respectively for day-ahead solar irradiance forecast with 31.07% as forecast skill.</p>

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Weather prediction using random forest machine learning model

Cited by 35 publications

References 17 publications

Tree-based Machine Learning and Deep Learning in Predicting Investor Intention to Public Private Partnership

Tree-based Machine Learning and Deep Learning in Predicting Investor Intention to Public Private Partnership

Approach by modeling to generate an e-commerce web code from laravel model

Day-ahead solar irradiance forecast using sequence-to-sequence model with attention mechanism

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