Weather Parameters Forecasting as Variables for Rainfall Prediction using Adaptive Neuro Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR)

Abstract

The weather anomaly phenomenon that occurs can have some negative impact such as flooding, floods will paralyze the economic activities of the community, transportation activities, damage public infrastructure. In this research forecasting weather parameters as a variable for predicting the amount of rainfall using the ANFIS method and Support Vector Regression (SVR) with the aim to provide information on future weather conditions quickly and accurately. The people can prepare themselves and prepare the equipment needed to deal with it. Rainfall predicted based on synop data such us relative humidity, wind, and temperature. Each parameters must forcasted by using ANFIS and the result used for predict rainfall. Accurate prediction calculated using MSE and RMSE. Predictions of parameters that affect rainfall using the ANFIS method shown that for wind speed predictions having RMSE of 1.975004, temperature predictions have RMSE of 0.742332, and predictions of relative humidity have RMSE of 3.871590. Predicted rainfall based on the data results of the nearest method pre-processing using the Support Vector Regression (SVR) method produces an MSE error value of 0.0928.

Adaptive Decomposition and Multitimescale Analysis of Long Time Series of Climatic Factors and Vegetation Index Based on ICEEMDAN-SVM

“…SRM seeks to minimize an upper bound on the generalization error consisting of the sum of the training error and the confidence level based on the Vapnik-Chernoverkis dimension, which is different from the commonly used ERM principle that only minimizes the training error. This method has proven to be very effective for solving generic classification and regression problems [73]- [75]. The basic idea of SVM for regression is to introduce kernel function, map the input space into a high dimensional feature space via a nonlinear mapping, and perform a linear regression in this feature space [72].…”

Monthly Average Rainfall Forecasting Based On An Adaptive Neuro-Fuzzy Inference System in Upper Brahmani Basin, Odisha, India

“…Brahmani River Basin is located in the Indian peninsula's states of Chhattisgarh (Novitasari et al 2020), Jharkhand, and Orissa, from latitudes 20° 28' to 23° 35' N and longitudes 83° 52' to 87° 30' E(Figure -1). It lies between the Mahanadi Basin on the right and the Baitarani Basin on the left.…”

Deteksi Tingkat Keparahan Cedera Panggul Menggunakan ANFIS

“…Algoritma ANFIS seperti penurunan gradien dan perambatan mundur yang digunakan untuk melatih data berdasarkan artificial neural network (Adyanti et al, 2017). Tahap pertama yang digunakan pada ANFIS adalah "if then" rules dari fuzzy inference system (Novitasari et al, 2020).…”