scholarly journals Multivariate Fire Risk Models Using Sample-Size Reduction and Copula Regression in Kalimantan, Indonesia

2021 ◽  
Author(s):  
Mohamad Khoirun Najib ◽  
Sri Nurdiati ◽  
Ardhasena Sopaheluwakan
Keyword(s):  
Regression Model ◽  
Sample Size ◽  
Joint Distribution ◽  
El Nino ◽  
Fire Risk ◽  
Size Reduction ◽  
Total Precipitation ◽  
Climate Conditions ◽  
Dry Spells ◽  
Sample Size Reduction

Abstract The copula-based joint distribution can construct a fire risk model to improve forest fires' early warning system, especially in Kalimantan. In this study, we model and analyze the copula-based joint distribution between climate conditions and hotspots. We used several climate conditions, such as total precipitation, dry spells, and El Nino-Southern Oscillation (ENSO). We used copula functions with sample size reduction to construct the joint distributions and the copula regression model to estimate the fire size. The results show that the probability of extreme hotspots number during normal ENSO conditions is very rare and almost near zero during La Nina. Other than that, extreme hotspot event (more severe than in 2019) during El Nino is more sensitive to total precipitation than dry spells based on the conditional survival function. However, the copula regression model found that the model used dry spells as a climate condition better than total precipitation. In this model, the 95% confidence interval of the expected hotspots can cover all actual hotspots data.

scholarly journals Multivariate Fire Risk Models Using Sample-Size Reduction and Copula Regression in Kalimantan, Indonesia

2021 ◽  
Author(s):  
Mohamad Khoirun Najib ◽  
Sri Nurdiati ◽  
Ardhasena Sopaheluwakan
Keyword(s):  
Forest Fires ◽  
El Niño ◽  
Joint Distribution ◽  
El Nino ◽  
Tail Dependence ◽  
Fire Risk ◽  
Total Precipitation ◽  
Climate Conditions ◽  
Dry Spells ◽  
The Relationship

Abstract Forest fires have become a national issue every year and get serious attention from the government and researchers, especially in Kalimantan. The copula-based joint distribution can construct a fire risk model to improve the early warning system of forest fires. This study aims to model and analyze the copula-based joint distribution between climate conditions and hotspots in Kalimantan. We constructed the bivariate joint distributions between climate conditions, either total precipitation or dry spells, and hotspots with sample size reduced by ENSO conditions, i.e., La Nina, normal, and El Nino. From the joint distribution, fire risk models are calculated using conditional probability and copula regression. The results show that the relationship between climate conditions and hotspots in La Nina and normal ENSO conditions have an upper tail dependence but no lower tail dependence. Meanwhile, the relationship has both upper and lower tail dependences during El Nino. There is an outlier in normal ENSO conditions with more hotspots than normally, i.e., in September 2019. The probability is very low during normal ENSO conditions, i.e., less than 2%. The only relatively high probability is during El Nino, i.e., more than 10%. Moreover, the copula regression models show that the model given specific dry spells is better than that given specific total precipitation as climate condition. The copula regression for hotspots given specific total precipitation and ENSO conditions has the RMSE value of 1339 hotspots and the R2 value of 60.70%. Meanwhile, the copula regression for hotspots given specific dry spells and ENSO conditions has the RMSE value of 1185 hotspots and the R2 value of 69.21%.

On biometric data sample size reduction when using Bayesian multidimensional correlation function

2017 ◽  
Vol 15 (2) ◽  
Author(s):  
A.I. Ivanov ◽  
◽  
P.S. Lozhnikov ◽  
A.E. Sulavko ◽  
Y.I. Serikova ◽  
...  
Keyword(s):  
Correlation Function ◽  
Sample Size ◽  
Size Reduction ◽  
Biometric Data ◽  
Sample Size Reduction

scholarly journals Quantifying the joint distribution of drought indicators in Borneo fire-prone area

2021 ◽  
Vol 880 (1) ◽  
pp. 012002
Author(s):  
Mohamad Khoirun Najib ◽  
Sri Nurdiati ◽  
Ardhasena Sopaheluwakan
Keyword(s):  
Return Period ◽  
Joint Distribution ◽  
Risk Model ◽  
Peat Soil ◽  
Monthly Precipitation ◽  
Total Precipitation ◽  
Dry Spells ◽  
Dry Conditions ◽  
Precipitation Anomalies ◽  
Drought Indicators

Abstract Borneo island is prone to fire due to its large peat soil area. Fire activity in Borneo is associated with regional climate conditions, such as total precipitation, precipitation anomaly, and dry spells. Thus, knowing the relationship between drought indicators can provide preliminary knowledge in developing a fire risk model. Therefore, this study aims to quantify the copula-based joint distribution and to analyze the coincidence probability between drought indicators in Borneo fire-prone areas. From dependence analysis, we found that the average of 2 months of total precipitation (TP), monthly precipitation anomalies (PA), and the total of 3 months of dry spells (DS) provides a moderate correlation to hotspots in Borneo. The results show the probability of the dry-dry period is 26.63, 17.66, and 18.54 % for TP-DS, PA-DS, and TP-PA, respectively. All of these are higher than the probability of the wet-wet period, which is 25.01, 16.12, and 17.98 % for TP-DS, PA-DS, and TP-PA, respectively. Through the probability, the return period of TP-DS in the dry-dry situation 3.2 months/year, meaning the dry situation in total precipitation and dry spells that occur simultaneously could appear about 3 months in a year on average. Furthermore, the return period of PA-DS and TP-PA in the dry-dry situation is 2.12 and 2.22 months/year, respectively. Moreover, the probability of dry spells in dry conditions when given total precipitation in dry conditions is higher than given precipitation anomalies in dry conditions.

Sign in / Sign up

Export Citation Format

Share Document