Читать книгу Handbook on Intelligent Healthcare Analytics - Группа авторов - Страница 52

As observed from the Markov chain model that is developed, a framework for identifying random effects can create a disaster. Kaggle datasets are used to find the state transition matrix and occurrence that are predicting the natural changes in climate. Computation on the basis of frequency and the latent state changes are monitored based on the probability. By considering the likelihood from the part of observation that are identified from the matrix according to rows and column for assumption. Using the sequence numbering and entries on hidden state, the current sequence follows the Markov model and fixes the likelihood. Using the proposed framework, the tasks such as important performance based on weather forecast are achieved. The proposed algorithm IBHMF that produced a better performance and also independent variables that are factors of time series shows the exact transition matrix analysis to predict the forecast based on climatic change.

Table 2.2 Sample dataset for predicting weather forecasting.

	Year	Total economic damage from natural disasters (US$)
count	561.000000	5.610000e+02
mean	1977.217469	1.146966e+10
std	30.399233	3.199525e+10
min	1900.000000	0.000000e+00
25%	1959.000000	6.50000e+07
50%	1984.000000	8.400000e+07
75%	2001.000000	5.444777e+09
max	2018.000000	3.640932e+11

Figure 2.4 Changes from various impacts from natural disaster.

Figure 2.5 Economic damage changes a prediction analysis.

Figure 2.6 Boxplot view of natural disaster on various entity.