Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia

Jumin, Ellysia; Basaruddin, Faridah; Yusoff, Yuzainee Bte. Md; Latif, Sarmad Dashti; Ahmed, Ali Najah

doi:10.1007/s11356-021-12435-6

Cited by 72 publications

(26 citation statements)

References 34 publications

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“…DT indicates bene ts of randomization approaches, alternate analysis, and classifying and grouping techniques. The main major uses of DT include discovering data anomalies, discovering data patterns, and providing accurate results (Jumin et al, 2021). Due to its reliability and diversity, DT is one of the most employed ML models for forecasting and modelling.…”

Section: Decision Treementioning

confidence: 99%

Air pollution forecasting and modelling using hybrid machine learning for Craiova City in Romania

Udriștioiu

MGHOUGHI

Yıldızhan

2022

Preprint

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Poor air quality affects human health and exacerbates climate change, inducing temperature variations. Therefore, it is important to understand at local level the interactions between climate variability and air pollution to mitigate the effects of air pollution on human health. The aim of this paper is to explore the correlations between meteorological parameters, temperature (T), humidity (H) and air pressure (P) and particulate matter concentrations (PM1, PM2.5, PM10). Also, it is investigated the correlation between PM 2.5 and PM10, between noise, CO2, and PM concentrations. For this purpose, five hybrid Machine Learning models were employed to forecast the PMs concentrations and then the Air Quality Index (AQI). The data set was provided by an independent network of sensors for the period of September 22, 2021 – February 17, 2022. In the results we found, R² exceeds, in general, 0.96 and, in most cases, they can reach 0.99. The humidity was found to be the less significant variable on the PMs concentrations, and the best accuracy was found by combining the pressure with the temperature. In addition, PM10 concentrations were found effectively related to the PM2.5 concentrations and with a bit strongly to the PM1. The PM10 concentration is not strongly correlated to the Noise and CO2 data. Finally, several new relationships have been built to forecast the PMs concentrations and the AQI based on the best combinations of predictor variables found.

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Section: Decision Treementioning

confidence: 99%

Air pollution forecasting and modelling using hybrid machine learning for Craiova City in Romania

Udriștioiu

MGHOUGHI

Yıldızhan

2022

Preprint

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“…The DT method is usually applied to construct a nonlinear model which is sensitive to intermediate missing values with stronger explanatory than linear regression (Pekel, 2020). According to the training input dataset, each region is recursively divided into two subregions originally, in which the output value is determined to construct a binary decision tree (Jumin et al, 2021). The process can be described as four steps in details:…”

Section: Multi-model Ensemble Methodsmentioning

confidence: 99%

Machine-learning ensembled CMIP6 projection reveals socio-economic pathways will aggravate global warming and precipitation extreme

Zhang

Chen

2022

Preprint

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Abstract. The climate change plays a key role in ecosystem evolution and has been proved to be affected by comprehensive factors including anthropogenic activities. The application of GCMs (General Circulation Models) launched by CMIP6 (Coupled Model Intercomparison Project Phase 6) has become a primary implement to catch future climate characteristics under different future socio-economic pathways. However, quantitative future climate change records with high credibility generated by robust GCMs merged dataset from CMIP6 is scare. The majority of former conclusions depend on traditional GCMs ensemble datasets (e.g., single, mean and medium) which have proved to be highly instable. In this study, 3 machine learning methods (Ordinary Least Squares regression, Decision Tree, and Deep Neural Networks) were applied to ensemble temperature and precipitation from 16 CMIP6 GCMs simultaneously. Monthly optimal estimation of precipitation and temperature from the three datasets were selected to generate a new ensemble dataset under three Socio-Economic Pathways (SSP1-2.6, SSP2-4.5 and SSP5-8.5). The new precipitation (temperature) ensemble dataset with the R=0.81 (0.99) is more accurate than all the single GCM. High credible analyses demonstrate that Europe and North America contribute more to global warming than Oceania, Africa and South America. The global continent break through 1.5 °C, 2 °C and 3 °C rising threshold in 2024, 2031 and 2048 under SSP5-8.5 scenarios, of which the driving capacity for global warming ranks first. Most precipitation aggregates in July and August, while dry months fall in April and September to next February till the end of 21st century. Global precipitation will be accelerated polarization with the decreasing trends of Africa and Asia (p < 0.05) under the scenario of SSP5-8.5. The proposed analysis provides credible opportunities and quantitative fundamental to understand future climate characteristics for ecology and meteorology.

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“…decision tree regression (dtr) models [29] are used in modelling and prediction approaches in statistics and machine learning very frequently. often referred to as dtr induction, these learning processes assume the construction of a particular graph structure, which is called a tree.…”

Section: Self Prediction Modelsmentioning

confidence: 99%

Estimation of atmospheric boundary layer values in the context of the daily prediction of PM10 air pollution

Kowalski¹,

Kusy²,

Szwagrzyk³

et al. 2022

Int. J. EI

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air pollution is one of the most dynamically developing problems of the contemporary world. due to constantly present threat of air pollution, it is essential for the society to be aware of this issue and to be able to trace the individual factors influencing the existence of smog, as well as to predict the state of air quality in the following hours and days. this paper aims to determine the feasibility of cascading prediction of atmospheric boundary layer (abl) values for several consecutive days, and then use this information to synthesize a prediction procedure for harmful smog particulate matter (pm10) for several days as well. various prediction methods are used in the current study, among which the linear regression algorithm proves to be the most effective. herein, the simulations concerning the investigated prediction algorithms are based on real data provided by the airly company network of pollution measurement stations as well as abl from the copernicus climate data store. Evaluation of the obtained results is carried out using such measures as mean squared error, mean absolute error, pearson correlation coefficient r, and index of agreement. as a result of the simulation, abl and then pm10 predictors are synthesized for three consecutive days. the latter is characterized by an average daily mean absolute error in the range of 8-10 µg/m3, and index of agreement 0.88-0.89 depending on the day of the prediction and the variants of the prediction algorithm selected.

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Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia

Cited by 72 publications

References 34 publications

Air pollution forecasting and modelling using hybrid machine learning for Craiova City in Romania

Air pollution forecasting and modelling using hybrid machine learning for Craiova City in Romania

Machine-learning ensembled CMIP6 projection reveals socio-economic pathways will aggravate global warming and precipitation extreme

Estimation of atmospheric boundary layer values in the context of the daily prediction of PM10 air pollution

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