Machine learning models for prediction of rainfall over Nigeria

Ojo, Olusola Samuel; Ogunjo, Samuel

doi:10.1016/j.sciaf.2022.e01246

Cited by 26 publications

(6 citation statements)

References 54 publications

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“…SVM holds an advantage over other machine learning techniques in its capability to reduce the intricacies and noise within data structures. It achieves this by employing kernels to transform the data [15].…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches

Bouziane,

Aghoutane,

Moumen

et al. 2024

E3S Web Conf.

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Accurate precipitation forecasting is paramount for various sectors. Traditional methods for rainfall prediction involve understanding physical processes, historical weather data, and statistical models. These methods utilize observations from ground-based weather stations, satellites, and weather radars to assess current conditions and predict future precipitation. However, accurate precipitation prediction remains challenging due to the intricate and non-linear characteristics of rainfall. Over the past few years, machine learning (ML) algorithms have shown promise in improving precipitation prediction accuracy. This research provides an overview of both traditional methods and advanced ML models applicable to rainfall prediction, including regression, classification, and time series models. We conducted a comprehensive review of related works that explore the impact of using ML algorithms for rainfall estimation. Through this analysis, we identified the strengths and limitations of ML models in this context and highlighted advancements in rainfall prediction using these algorithms. We possess a comprehensive dataset, spanning data from 1996 to 2015, comprising historical weather data from the Ziz basin, our designated study area. This dataset contains five key meteorological features: precipitation, humidity, wind, temperature, and evaporation. In terms of perspective, we plan to utilize this dataset and conduct a comprehensive comparative study to evaluate the performance of different ML models. Our objective is to demonstrate the effectiveness and potential of these algorithms in improving weather forecasting capabilities and enhancing the accuracy of rainfall estimation methods in the specific study area.

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Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches

Bouziane,

Aghoutane,

Moumen

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…Rainfall prediction involves forecasting the amount of rainfall in a specific area. Accurate rainfall predictions offer valuable observations to decision-makers, enabling them to assign resources more efficiently, mitigate risks, and increase socio-economic outcomes [1], [2], [3]. Researchers face significant challenges in attaining effective and precise time series analysis and modelling, primarily due to the complex and volatile nature of time series data, especially when it involves natural processes like rainfall.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimized Deep Learning Models for Rainfall Prediction: A Case Study in Aizawl, Mizoram

Zoremsanga,

Hussain

2024

IEEE Access

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Rainfall is vital to all life on Earth, and rainfall prediction is essential for various sectors and aspects of human society. Hilly areas such as the state of Mizoram in India have suffered from landslides during the rainy season. This study compares twelve hybrid deep learning and machine learning models to predict daily rainfall using meteorological variables such as maximum humidity, minimum temperature, maximum temperature, and rainfall.

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“…High rainfall or heavy rainfall is very dangerous for people's lives because it will have adverse effects such as floods and landslides. Floods can cause economic losses and overflow of water, which is the main factor transporting sediments, nitrates, phosphorus and other chemical compounds in watersheds, can also be harmful (Ojo & Ogunjo, 2022). Low rainfall can cause drought and will impact several water-related sectors such as agriculture, irrigation and energy (Ginting & Kartiasih, 2019;Kartiasih et al, 2022;Maulana & Kartiasih, 2017;Rachma Safitri & Kartiasih, 2019;Ojo & Ogunjo, 2022).…”

Section: Introductionmentioning

confidence: 99%

Utilizing the Sarima Model and Support Vector Regression to Forecast Monthly Rainfall in Bandung City

Innayah,

Sulistiana,

Febrian

et al. 2024

jitter

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As one of the largest cities in Indonesia, Bandung has varying monthly rainfall intensity. High rainfall is very dangerous for people's lives and will have an impact on various sectors such as agriculture, fisheries, tourism, and transportation. For this reason, rainfall prediction is needed as an effort for the government to make policies and the community can anticipate the possibility of high rainfall that occurs. This study compares the effectiveness of SARIMA and Support Vector Regression (SVR) models in predicting monthly rainfall objectively, with the aim of improving decision making for stakeholders. Forecasting rainfall data is carried out based on the best method of the two methods that have been compared. The results showed that the SARIMA method outperformed the SVR method in forecasting precision, as seen from the lower RMSE value of 93.2045. The results provide valuable insights into weather prediction methodologies, benefiting authorities and the public.

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Machine learning models for prediction of rainfall over Nigeria

Cited by 26 publications

References 54 publications

Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches

Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches

Particle Swarm Optimized Deep Learning Models for Rainfall Prediction: A Case Study in Aizawl, Mizoram

Utilizing the Sarima Model and Support Vector Regression to Forecast Monthly Rainfall in Bandung City

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