Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia
Climate change; Decision trees; Machine learning; Reservoirs (water); Water levels; Weather forecasting; Autocorrelation functions; Boosted decision trees; Coefficient of determination; Comparative studies; Forecasting modeling; Hyper-parameter; Machine learning methods; Rainfall forecasting; Rain
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2023
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my.uniten.dspace-261882023-05-29T17:07:33Z Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia Ridwan W.M. Sapitang M. Aziz A. Kushiar K.F. Ahmed A.N. El-Shafie A. 57218502036 57215211508 57205233815 57212462702 57214837520 16068189400 Climate change; Decision trees; Machine learning; Reservoirs (water); Water levels; Weather forecasting; Autocorrelation functions; Boosted decision trees; Coefficient of determination; Comparative studies; Forecasting modeling; Hyper-parameter; Machine learning methods; Rainfall forecasting; Rain Rainfall plays a main role in managing the water level in the reservoir. The unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the reservoir. In this study, several models and methods were applied to predict the rainfall data in Tasik Kenyir, Terengganu. The comparative study was conducted focusing on developing and comparing several Machine Learning (ML) models, evaluating different scenarios and time horizon, and forecasting rainfall using two types of methods. Data involved for this research consist of taking the average rainfall from 10 stations around the study area using Thiessen polygon to weight the station area and projected rainfall. The forecasting model uses four different ML algorithms, which are Bayesian Linear Regression (BLR), Boosted Decision Tree Regression (BDTR), Decision Forest Regression (DFR) and Neural Network Regression (NNR). On the other hand, the rainfall was predicted on different time horizon by using different ML's algorithms which is method 1 (M1): Forecasting Rainfall Using Autocorrelation Function (ACF) and method 2 (M2): Forecasting Rainfall Using Projected Error. In M1, the best regression developed for ACF is BDTR since it has the highest coefficient of determination, R2, after tuning the hyperparameter. The results show coefficient between 0.5 and 0.9 with the highest of each scenarios for daily (0.9739693), weekly (0.989461), 10-days (0.9894429) and monthly (0.9998085). In M2, overall model performances show that normalization using LogNormal is preferably giving a good result of each categories except for 10-days with BDTR and DFR are the most acceptable result than NNR and BLR. It is concluded that, two different methods have been applied with different scenarios and different time horizons, and M1 shows a rather high accuracy than M2 using BDTR modeling. � 2020 THE AUTHORS Final 2023-05-29T09:07:33Z 2023-05-29T09:07:33Z 2021 Article 10.1016/j.asej.2020.09.011 2-s2.0-85095868138 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095868138&doi=10.1016%2fj.asej.2020.09.011&partnerID=40&md5=4c3cb64699a7f5b87cc8f0dd7ffee727 https://irepository.uniten.edu.my/handle/123456789/26188 12 2 1651 1663 All Open Access, Gold Ain Shams University Scopus |
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Climate change; Decision trees; Machine learning; Reservoirs (water); Water levels; Weather forecasting; Autocorrelation functions; Boosted decision trees; Coefficient of determination; Comparative studies; Forecasting modeling; Hyper-parameter; Machine learning methods; Rainfall forecasting; Rain |
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57218502036 |
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57218502036 Ridwan W.M. Sapitang M. Aziz A. Kushiar K.F. Ahmed A.N. El-Shafie A. |
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Article |
| author |
Ridwan W.M. Sapitang M. Aziz A. Kushiar K.F. Ahmed A.N. El-Shafie A. |
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Ridwan W.M. Sapitang M. Aziz A. Kushiar K.F. Ahmed A.N. El-Shafie A. Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia |
| author_sort |
Ridwan W.M. |
| title |
Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia |
| title_short |
Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia |
| title_full |
Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia |
| title_fullStr |
Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia |
| title_full_unstemmed |
Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia |
| title_sort |
rainfall forecasting model using machine learning methods: case study terengganu, malaysia |
| publisher |
Ain Shams University |
| publishDate |
2023 |
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1806428503594237952 |
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13.222552 |