ANALYSIS OF RAINY DAYS AND RAINFALL TO LANDSLIDE OCCURRENCE USING LOGISTIC REGRESSION IN PONOROGO EAST JAVA

Referred to data of Badan Nasional Penanggulangan Bencana (BNPB) and Kementerian Kesehatan Republik Indonesia (Kemenkes RI), almost landslide occurrence in Ponorogo always starts with high-intensity rain. This research aimed to determine simultaneously correlation and partial assessment impact of rainy days every month and monthly rainfall toward landslide occurrence in Ponorogo using logistic regression. The data collection was conducted through Badan Pusat Statistik (BPS) in the book of Ponorogo Regency in Figure on 2012 to 2016. The existing data shows that in sixty months have been twenty-six times landslides occurrence in Ponorogo districts. The data statistically analyzed in simultaneous proves that contribution of rainy days and rainfall to landslide were included adequate correlation (Nagelkerke R Square = 25.4 % and Cox & Snell R Square = 36.9 %) and in partial test proves that rainy days have significant impact (sig. = 0.024) and rainfall does not significant impact (sig. = 0.291) (α = 0.05) to landslide occurrence in Ponorogo regency. The rainy days per month were abled applied to predict for possible landslide elsewhere.


Introduction
Ponorogo Regency is an area in East Java Province who are in a position 200 km northwest province capital, and 800 km to the capital city of Indonesia.The area of 1.371.78km 2 is divided in 21 district that consists of 307 villages.Ponorogo Regency topography varies from lowlands to mountains.Based on existing data, a large district that is 79% Ponorogo situated at an altitude of fewer than 500 m above sea level, 14.4% are between 500 and 700 m above sea level and the remaining 5.9%is at the height of the above 700 m(Badan Perencanaan Pembangunan Daerah Ponorogo, 2013).
Based on the location of topography, climate, and rainfall, Ponorogo regency including areas that are often categorized landslides, especially in the hills and mountains (Yuniarta, Saido, & Purwana, 2015)of which there are in five districts that is Ngrayun, Slahung, Pudhak, Pulung, and Ngebel.According to data from BNPB and Kemenkes RI from 2012 to 2016, Ngrayun district was ranked the highest with nine times the landslide, Slahung with five times, Ngebel four times and the last place is Pudhak and Pulung with twice the landslide occurred.Almost all landslide events always start with rainfall in high intensity or rain for more than a day(Badan Nasional Penanggulangan Bencana, 2018;Kementerian Kesehatan Republik Indonesia, 2018).
Factors of rainy days and rainfall are also the ultimate set to be the cause of the occurrence of the landslide by Ubeku and Okeke (Ubechu & Okeke, 2017), as well as Paimin a Geoscientist who gave a statement that 25% of landslide factors are caused by rainy days for three days.(Paimin, Sukresno, & Pramono, 2009), the Department of Public Works also makes one of the fourteen factors that cause landslides is rainfall (Departemen Pekerjaan Umum, 2007), the book of Disaster Risks Indonesia also makes one of the four factors that cause landslides is rainfall (Amri et al., 2016).This study examines the extent to which the simultaneous correlation of rainy days every month and monthly rainfall on the occurrence of landslides in Ponorogo and the impact of rainy days every month and monthly rainfall on landslides in Ponorogo using logistic regression.
These are some studies related to landslides and logistic regression i.e, the first is the results perform that landslides terrace dramatically from 1946 to 2012 inthe capture area.The nearness and overlapping of human development with landslides terraced.However,the logistic regression results prove that variation in sensitivity to landslides was due to natural causes,with the exclusion of historical deforestation and recentlyestablished road systems.
Accordingly, well-recoveredhistoricalwoodland sites might presently be landslide-prone areas(Y.C. Chuang & Shiu, 2018), second in Ambon Indonesia, eight landslide causative factors were respected in the landslide sensitivityevaluation.The causative factors were height, slope angle, slope aspect, closeness to stream network, lithology, the solidity of geological boundaries, closenessto faults, and closenessto the road network.The output sensitivitymaps were reclassified into five categorize ranging from very low to very high sensitivity using Jenks natural breaksmethod.Twenty percent of allmapped landslideswere used as the legalization of the sensitivitymodels.The legalization and the accuracy of each modelwere examined by calculating areas under recipient operating characteristic curves (ROCs),andthe areasnether the curve (AUC) for the success rate curves of FR, LR, and ANN were 0.688, 0.687, and 0.734, severally.The AUC for the prediction rate curve of FR, LR, and ANN were 0.668, 0.667, and 0.717, respectively (Aditian, Kubota, & Shinohara, 2018), the third is twelve landslide causative factors (namely, slope, slope aspect, highness, curvature, profile arch, plan arch, slope length, topographic dampness index, gap to river, gap to road, gap to fault and yearly maximum 24-and 48-h rainfalls) were used in this landslide sensitivity analysis.These models were applied to the Kaoping River basin in Southwestern Taiwan to rate its show.Landslide inventory maps from 2008 to 2011 were congregated.The results prove that the RBF-SVM model makes better the logistic regression in the study area (Lin, Chang, Huang, & Ho, 2017).

The Methods
Logistic regression is a method of statistical analysis to describe the relationship between response variables (dependent variable) which has two or more categories with one or more explanatory variables (independent variable) scale or interval (Hosmer & Lemeshow, 2005).Logistic regression is a nonlinear regression, used to explain the relationship between X and Y which is nonlinear, Y-dislocated abnormality, diversity of non-constant response unexplained by ordinary linear regression model (Agresti, 1996).

Variable Identification
Independent variable is landslide occurrence and the dependent variable is rainy days every month and rainfall every month

Output Result
Data Analysis with SPSS Data Collection

Research Hypothesis
Variable Identification 1.Any simulant correlation between rainy days every month and rainfall every month to landslide occurrence 2. Impact between rainy days every month to landslide occurrence 3. Impact between rainfall every month to landslide occurrence

Data collection
This data was taken on five years from Ponorogo in Figure since  (Goodness of fit), the significant test is used to know the correlation value of dependent variables to independent variable, the accuracy classification test is used for measure precision prediction in this study, and the last partial test is used to prove the significance value of dependent variables to independent variable.The statistic of logistic regression was produced the chi-square value that is used to check the correlation of rainy days and rainfall toward landslide occurrence.The accepted criteria of rainy days and rainfall to landslide occurrence can be seen if the chi-square value is lower than chi-square table.

Output Result
The output details referred to Figure 2 were shown in the result and discussion section.

Results and Discussion
Here are the results of calculations using logistic regression using SPSS tools (Reed & Wu, 2013)  Referred to table 2, the sixty data is valid and no missing cases.The probability of 0.645> 0.05, meaning that the binary regression model is suitable for further analysis since there is no significant difference between the predicted classification and the observed classification.(Pourghasemi & Rahmati, 2018).

Conclusion
The data statistically from Ponorogo regency that analyzed using logistic regression method in simultaneous proves that contribution of rainy days and rainfall to landslide were included adequate correlation (Nagelkerke R Square = 25.4 % and Cox & Snell R Square = 36.9%) and in partial test proves that rainy days have significant impact (sig.= 0.024) and rainfall does not significant impact (sig.= 0.291) (α = 0.05) to landslide occurrencein Ponorogo regency.For the next research, the rainy days per month were abled applied to predict for possible landslide elsewhere and landslide analized can used others algoriltm beside linear regression such as artificial neural network, support vector machine, boosted regression tree, generalized linear regression, etc.
model has sufficiently explained the data (Goodness of fit) H1 = Model is not enough to explain data Test criteria: H0 accepted If the p-value or significance > 0.05 Referred to Table 5. has a significance value of 0.645 > 0.05, it means that the significance value is greater than 0.05.Decision: H0 accepted Conclusion: the model has sufficiently explained the data (goodness of fit).

Table 1 .
2012 to 2016 by Ponorogo Regional Development Planning Agencyand Central Bureau of Statistics (Badan Perencanaan Pembangunan Daerah Ponorogo, 2013, 2014, Badan Pusat Statistik Kabupaten Ponorogo, 2015a, 2015b, 2016), Ministry of Health of the Republic of Indonesia (Kementerian Kesehatan Republik Indonesia, 2018) and BPBD (Badan Nasional Penanggulangan Bencana, 2018).Data of rainy days every month, rainfall every month and landslide occurred status in 2012-2016 at Ponorogo regency Figure 2 describes the logistic regression stage i.e. data validation test is used to know that data is valid or not, variable category naming is used to know the code of the landslide occurs or not, the properness model test is used for sufficiently explained the data

Table 2 .
Case Processing Summary

Table 3 .
Dependent Variable Encoding

Table 4 .
Omnibus Tests of Model CoefficientsTest criteria: H0 is rejected if sig value <0.05, or chi-square value > chi-square table (5.99)Referred to Table4.The output can be seen that significant = 0.000, it means less than 0.05 and chi-square value is 17.552, it means chi-square value is higher than chi-square table.

Table 5 .
Hosmer and Lemeshow Test