Neurocomputing fundamental climate analysis

Abstract

Rainfall is a natural phenomenon that needs to be studied more deeply and interesting to be analyzed. It involves numbers of human activities such as aviation, agriculture, fisheries, and also disaster risk reduction. Moreover, the characteristics of rainfall data follows seasonality, fluctuation, not normally distributed and it makes traditional time series challenging to use. Therefore, neurocomputing model can be used as an alternative to extraction information from rainfall data and give high performance also accuracy. In this paper, we give short preview about SST Anomalies in Manado, Northern Sulawesi and at the same time comparing the performance of rainfall forecasting by using three types of neurocomputing methods such as Generalized Regression Neural Network (GRNN), Feed forward Neural Network (FFNN), and Localized Multi Kernel Support Vector Regression (LMKSVR). In a nutshell, all of neurocomputing methods give highly accurate forecasting as well as reach low MAPE FFNN 1.65%, GRNN 2.65% and LMKSVR 0.28%, respectively.

Selecting critical features for data classification based on machine learning methods

“…The analysis of noisy ecological data [25], variables in ecology modelling [26], number of counts termites [27], community ecology and integrating species, traits, environmental, space [28], parameter in rainfall forecasting [29,30], global climate zone [31], local climate zone [32], environmental noise pollution [33], urban pollution [34,35], rainfall spatial temporal [36], flash flood hazard [37,38], landslide [39], earthquake damage detection using curvilinear features [40], earthquake classifiers using stochastic reconstruction [41] and tsunami [42] Health Future genetic association studies of colorectal cancer [11], Aortic Anatomy on Endovascular Aneurysm Repair (EVAR) [43], colorectal cancer cases phenotype [10], identify a wide range of predictors that could enhance prediabetes prediction and clinical decision-making [44], the classification of diabetes mellitus [45], type 2 diabetes within 2 years in an obese, hypertensive population [46], the principal purpose of coronary illness [47], heart disease [48], cardiovascular disease [49], ovarian cancer patients [50], gene expression RNA-Seq data [51,52], adjuvant chemotherapy effectiveness assessment in non-small cell lung cancer [53], and Alzheimer's disease [54] Finance Mineral prospect [12], Industrial recommendation system [13], financial crisis [55], industrial coal mine [56], poverty classification [57], spatiote...…”

Cluster Around Latent Variable for Vulnerability Towards Natural Hazards, Non-Natural Hazards, Social Hazards in West Papua

“…In order to respond, in the last stage of the recovery process, the primary objective is to rebuild as well as stabilize the system so that it recovers immediately, as it would before the disaster recovers or improves for better transformation. Successful adaptation strategies will greatly reduce the likelihood of disasters [36]. There are three major stages of disaster risk mitigation adaptation, including the predisaster, disaster and post-disaster stages [37], [38]…”

Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO

“…The probability vector of steady-state π is an Eigenvector determined by Eigenvalue λ so that π must fulfil in equation (14).…”

“…Neural Network [13] is a nonparametric model that can be used for modelling time series data that does not require various residual assumptions. Several studies have found that this model produces better predictive accuracy than parametric models [14]. Beside of time dimension, data can also have a space dimension known as space-time data [15].…”