Comparison of Adaline and Multiple Linear Regression Methods for Rainfall Forecasting

Sutawinaya, I Putu; Astawa, I Nyoman Gede Arya; Hariyanti, Ni Kadek Dessy

doi:10.1088/1742-6596/953/1/012046

Cited by 6 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition, [63] explored the single and multi-layer LSTM model by adding intermediate variable signals into the LSTM memory block to build an adaptive model for predicting the weather variable such as temperature, pressure, humidity, and dew point. A comparison study was conducted between the Adaline method and the multiple linear regression method for rainfall forecasting in Kota Denpasar, Bali [64].…”

Section: Discussionmentioning

confidence: 99%

Rainfall Forecast Using Machine Learning with High Spatiotemporal Satellite Imagery Every 10 Minutes

et al. 2022

View full text Add to dashboard Cite

Increasing the accuracy of rainfall forecasts is crucial as an effort to prevent hydrometeorological disasters. Weather changes that can occur suddenly and in a local scope make fast and precise weather forecasts increasingly difficult to inform. Additionally, the results of the numerical weather model used by the Indonesia Agency for Meteorology, Climatology, and Geophysics are only able to predict the rainfall with a temporal resolution of 1–3 h and cannot yet address the need for rainfall information with high spatial and temporal resolution. Therefore, this study aims to provide the rainfall forecast in high spatiotemporal resolution using Himawari-8 and GPM IMERG (Global Precipitation Measurement: The Integrated Multi-satellite Retrievals) data. The multivariate LSTM (long short-term memory) forecasting is employed to predict the cloud brightness temperature by using the selected Himawari-8 bands as the input and training data. For the rain rate regression, we used the random forest technique to identify the rainfall and non-rainfall pixels from GPM IMERG data as the input in advance. The results of the rainfall forecast showed low values of mean error and root mean square error of 0.71 and 1.54 mm/3 h, respectively, compared to the observation data, indicating that the proposed study may help meteorological stations provide the weather information for aviation purposes.

show abstract

Section: Discussionmentioning

confidence: 99%

Rainfall Forecast Using Machine Learning with High Spatiotemporal Satellite Imagery Every 10 Minutes

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Intensitas hujan yang terlalu tinggi tentunya akan memberikan dampak yang tidak bagus, seperti bencana banjir. Pengukuran curah hujan dapat dilakukan dengan menghitung intensitas hujan yaitu jumlah curah hujan per unit dalam suatu periode tertentu [1].…”

Section: Pendahuluanunclassified

Pendekatan Z-Score Dan Fuzzy Dalam Pengujian Akurasi Peramalan Curah Hujan

Radhitya¹,

Sudipa²

2020

SINTECH Journal

View full text Add to dashboard Cite

Determination of rainfall is important to determine the intensity of rain that occurs in an area. Rain intensity that is too high will certainly have a bad impact. Forecasting or prediction techniques are used to determine the likelihood of intensity occurring in the following year. However, rainfall data are continuous numerical data. Measurement of accuracy becomes more difficult if the data type is like that. So, this study tests the accuracy of rainfall forecasting in the city of Denpasar from a different perspective. This test combines the Z-score method and the Fuzzy set theory to normalize and classify rainfall data. This combination determines the degree of rainfall membership divided into Upper, Middle, and Lower levels. Based on the results of rainfall accuracy testing starting in 2012-2016 obtained an average value of accuracy of 85% with training data that is data in 2007-2015. The normalization process greatly affects the value of the training data.

show abstract