Rainfall Prediction Using Machine Learning Algorithms for the Various Ecological Zones of Ghana

Appiah-Badu, Nana K. A.; Missah, Yaw Marfo; Amekudzi, Leonard K.; Najim, Ussiph; Twum, Frimpong; Ahene, Emmanuel

doi:10.1109/access.2021.3139312

Cited by 27 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RMSE of 2.7 mm and an MAE of 8.8 mm were obtained in their study. Rainfall prediction in Ghana's several ecological zones was shown to be better predicted using XGBoost, RF, and MLP, according to Appiah-Badu et al [60]. The study by Lawal et al [65] found XGBoost as the best model with an MAE of 0.042529 and RMSE of 0.05654 in predicting daily rainfall in the Nyando region of Kenya.…”

Section: E Comparative Analysis Of Xgboost With Existing Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts Using Uganda’s Lake Victoria Basin Weather Dataset

Gahwera,

Eyobu,

Isaac

2024

IEEE Access

View full text Add to dashboard Cite

As a result of climate change, the difficulty in the prediction of short-term rainfall amounts has become a necessary area of research. The existing numerical weather prediction models have limitations in precipitation forecasting especially due to high computation requirements and are prone to errors. Precipitation amount prediction is challenging as it requires knowledge on a variety of environmental phenomena, such as temperature, humidity, wind direction, and more over a long period of time. In this study, we first of all present our Lake Victoria Basin weather dataset and then use it to conduct a rigorous analysis of machine learning algorithms to do short term rainfall prediction. The rigorous analysis includes algorithm optimizations to improve prediction performance. In particular, we intend to validate our weather dataset using various machine learning regression models which include Random Forest regression, Support vector regression, Neural Network regression, Least Absolute Shrinkage and Selection Operator regression, Gradient boosting regression, and Extreme Gradient boosting regression. The performance of the models was assessed using Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) performance metrics. The findings demonstrate that, in comparison to other algorithms, Extreme Gradient Boost Regression had the lowest MAE values of 0.006, 0.018, 0.005 for Lake Victoria basin weather data in Uganda, Kenya, and Tanzania respectively.INDEX TERMS Precipitation Amount, Weather Prediction, Data-driven Approaches, Short-term Forecasting.WRF model results can be automated and checked with

show abstract

Section: E Comparative Analysis Of Xgboost With Existing Methodsmentioning

confidence: 99%

“…Notably, the results illustrate how the corresponding approach performs in terms of MAE and RMSE on test set performance. [54], [58], [60]. Consequently, 20% of data was drawn from Kenya weather station and another 20% from Tanzania.…”

Section: A Evaluating Xgboostmentioning

confidence: 99%

Analysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts Using Uganda’s Lake Victoria Basin Weather Dataset

Gahwera,

Eyobu,

Isaac

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Rainfall prediction is one of the widely studied areas in this context. The proposed study in [13] was used to classify the rainfall status as yes or no in different zones of Ghana considering various climatic features that were collected between the years 1980 and 2019. Well-known classification algorithms, including the decision tree (DT), multilayer perceptron (MLP), KNN, RF, and extreme gradient boosting (XGBoost) were applied for this aim.…”

Section: Introductionmentioning

confidence: 99%

Rainfall Prediction Using an Ensemble Machine Learning Model Based on K-Stars

Tuysuzoglu

Birant

2023

Sustainability

View full text Add to dashboard Cite

Predicting the rainfall status of a region has a great impact on certain factors, such as arranging agricultural activities, enabling efficient water planning, and taking precautionary measures for possible disasters (flood/drought). Due to the seriousness of the subject, the timely and accurate prediction of rainfall is highly desirable and critical for environmentally sustainable development. In this study, an ensemble of K-stars (EK-stars) approach was proposed to predict the next-day rainfall status using meteorological data, such as the temperature, humidity, pressure, and sunshine, that were collected between the years 2007 and 2017 in Australia. This study also introduced the probability-based aggregating (pagging) approach when building and combining multiple classifiers for rainfall prediction. In the implementation of the EK-stars, different experimental setups were carried out, including the change of input parameter of the algorithm, the use of different methods in the pagging step, and whether the feature selection was performed or not. The EK-stars outperformed the original K-star algorithm and the recently proposed studies in terms of the classification accuracy by making predictions that were the closest to reality. This study shows that the proposed method is promising for generating accurate predictions for the sustainable development of environmental systems.

show abstract

“…For this goal, several machine learning algorithms have been investigated, each with its own set of strengths and applications. For example, artificial neural networks (ANN) [22,23], recurrent neural networks (RNN) [24,25], random forest (RF) [26], gaussian process regression (GPR) [27], gradient-boosting [28], extreme gradient boosting [29], and long-short term memory (LSTM) [30].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities

Kumar,

Kedam,

Sharma

et al. 2023

Sustainability

View full text Add to dashboard Cite

Current research studies offer an investigation of machine learning methods used for forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their temporal complexities, are exploited using a unique data segmentation approach, providing discrete training, validation, and testing sets. Two unique models are created: Model-1, which is based on daily data, and Model-2, which is based on weekly data. A variety of performance criteria are used to rigorously analyze these models. CatBoost, XGBoost, Lasso, Ridge, Linear Regression, and LGBM are among the algorithms under consideration. This research study provides insights into their predictive abilities, revealing significant trends across the training, validation, and testing phases. The results show that ensemble-based algorithms, particularly CatBoost and XGBoost, outperform in both models. CatBoost emerged as the model of choice throughout all assessment stages, including training, validation, and testing. The MAE was 0.00077, the RMSE was 0.0010, the RMSPE was 0.49, and the R2 was 0.99, confirming CatBoost’s unrivaled ability to identify deep temporal intricacies within daily rainfall patterns. Both models had an R2 of 0.99, indicating their remarkable ability to predict weekly rainfall trends. Significant results for XGBoost included an MAE of 0.02 and an RMSE of 0.10, indicating their ability to handle longer time intervals. The predictive performance of Lasso, Ridge, and Linear Regression varies. Scatter plots demonstrate the robustness of CatBoost and XGBoost by demonstrating their capacity to sustain consistently low prediction errors across the dataset. This study emphasizes the potential to transform urban meteorology and planning, improve decision-making through precise rainfall forecasts, and contribute to disaster preparedness measures.

show abstract

Rainfall Prediction Using Machine Learning Algorithms for the Various Ecological Zones of Ghana

Cited by 27 publications

References 23 publications

Analysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts Using Uganda’s Lake Victoria Basin Weather Dataset

Analysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts Using Uganda’s Lake Victoria Basin Weather Dataset

Rainfall Prediction Using an Ensemble Machine Learning Model Based on K-Stars

A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities

Contact Info

Product

Resources

About