Spatiotemporal Analysis for Rainfall Prediction Using Extreme Learning Machine Cluster

Renaldy Fredyan, Muhammad Rizki Nur Majiid, Gede Putra Kusuma


Rainfall prediction is an essential study as a guideline for water resources management to manage disasters. Still, earlier research cares much about temporal information, only considering a single spatial location. The earth’s land surface has a large area of spatial location, so to manage spatial information simultaneously as temporal, we use spatiotemporal data to analyze rainfall prediction more accurately. This study uses the spatiotemporal Extreme Learning Machines (ELM) Cluster to forecast rainfall using CHIPRS data from satellites and stations. Data consists of spatial two dimensions and temporal data from 1981 to 2020. The dataset for the experiment contains 480 months. We use focal operation for data preprocessing to the nearest neighbor value. Moreover, the ELM cluster can manage every spatial location by sharing the output weight of ELM, so there is no spatial information left behind. Then, comparing the spatiotemporal Extreme Learning Machines Cluster among SVR, Linear Regression, Gaussian, Ridge, and Lasso are used to predict the data on those timescales. The results indicate that spatiotemporal ELM-Cluster can accurately forecast rainfall. Using ELM-Cluster in hydrological rainfall forecasting is encouraging, and the model can practically be used. Evaluation using MAE with a score of 66.77 and RMSE, 83.77, getting the fastest training with only 28.9 seconds compared to the other methods due to the ELM Cluster does not have backpropagation with spatial improvement.


Rainfall prediction; spatiotemporal data; CHIRPS data; spatiotemporal ELM cluster

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