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Near Future Prediction of Rainfall Distribution Pattern by Adaptive Neuro Fuzzy Interference System in Central Java, Indonesia

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Near Future Prediction of Rainfall Distribution Pattern by Adaptive Neuro Fuzzy Interference System in Central Java, Indonesia 30 June, 2021 AgricEngInt: CIGR Journal Open access at http://www.cigrjournal.org Vol. 23, No.2 Near future prediction of rainfall distribution pattern by adaptive neuro fuzzy interference system in Central Java, Indonesia Reza Kusuma Nurrohman1,2*, Bayu Dwi Apri Nugroho3, Putu Sudira3, Ngadisih3, Murtiningrum3 (1. Department of Agricultural Engineering, University of Mataram, Mataram 83126, West Nusa Tenggara, Indonesia 2. Graduate School of Agriculture, Hokkaido University, Hokkaido 060-0809, Sapporo, Japan 3. Department of Agricultural and Biosystems Engineering, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia) Abstract: Rainfall distribution pattern is very important for sustainable production in the agricultural sector in Tropical region. The near future rainfall amount under changing climate was predicted from 2009 to 2028 by Adaptive Neuro Fuzzy Inference System (ANFIS), which was trained with the rainfall observation data from 1979 to 2013, spatiotemporally in the Central Java Province, Indonesia. Our analysis showed that the predicted rainfall data using ANFIS could represent actual rainfall conditions. Rainfall predicted from 2009 – 2028 in Central Java will experience a decrease in high rainfall in an area of 1,615,125.2 hectare, which can cause drought. The area that predicted to experience drought in the future are Kebumen, Jepara, Pati, Rembang, Kudus, Grobogan, Blora, Sragen, and parts of the regions of Purworejo, Cilacap, Banyumas, Brebes, Wonogiri, Karanganyar, and Demak. Keywords: climate change, rainfall pattern, ANFIS, Central Java Citation: Nurrohman, R. Kusuma., B. D. A. Nugroho, P. Sudira, Ngadisih, and Murtiningrum. 2021. Near future prediction of rainfall distribution pattern by Adaptive Neuro Fuzzy Interference System in Central Java, Indonesia. Agricultural Engineering International: CIGR Journal, 23 (2):30-42. 1 Introduction threaten several countries, especially islands such as Indonesia as predicted by the FAO (Sekhar, 2018). Climate change occurs globally, both in tropical, sub- Based on research conducted by Nugroho (2015), it is tropical, temperate and polar regions. According to known that climate change is causing changes in rainfall Mawardi (2016), climate change, a direct result of global patterns in Central Java Province. Based on identification warming, already, is and will continue occur on earth. from Regional Disaster Management Agency and related The direct result of climate change is a change in the agencies in 2019, there are 1,319 villages in 287 sub- hydrological cycle which results in changes in water districts in 31 districts / cities in Central Java prone to availability (quantity and quality) of water that can be experiencing drought. BMKG (2019a) and Yuwono utilized by plants, animals and also humans. This change (2019) also stated that Central Java Province had the most in water availability will directly affect the environmental drought areas in Indonesia, then East Nusa Tenggara and system of plants, including agricultural crops, both in the East Java Provinces. Based on data from the Central Java, regional and global regions. The water crisis is likely to Indonesian Red Cross (PMI) data, in October 2019, as many as 12,088,000 liters of water have been distributed Received date: 2020-03-20 Accepted date: 2020-08-07 to 473 villages in 26 districts/cities to overcome the *Corresponding author: Reza Kusuma Nurrohman, Department of Agricultural Engineering, Universitas Mataram, Indonesia. Tel : drought that occurred. The distributed water is used for +6285291249967, Fax: +62370636041, Email: daily needs and irrigated the rainfed lowland rice fields. [email protected]. Meteorology, Climatology, and Geophysical Agency June, 2021 Near future prediction of rainfall distribution pattern by adaptive neuro fuzzy interference system Vol. 23, No.2 31 (BMKG) also stated that the drought in Central Java was in this study can be used by the government to develop the worst since 2015 (Kencana, 2019). strategies for increasing agricultural production in the Head of the Horticultural and Plantation Food Crop future in an effort to tackle the impact of climate change Protection Center, Central Java Agriculture and in the agricultural sector. Plantation Office, Herawati (2019) said that a total of 2 Materials and methods around 70,000 hectares of agricultural land in Central 2.1 Study area Java had experienced drought. However, from that Central Java is one of the provinces on the island of amount, only around 17,902 ha experienced crop failure. Java, located between two large provinces, namely West The most affected area is in Cilacap, which is around Java and East Java. It is located between 5°40' and 8°30' 4,000 ha, then Kebumen and followed by Grobogan South Latitude and between 108°30' and 111°30' East (Saputra, 2019). This is certainly a serious problem that Longitude (including Karimunjawa Island). A map of must be handled properly by the government, as one of Central Java Province can be seen at Figure 1. the efforts to mitigate drought. By predicting rainfall and land mapping, the government can be used as a reference to determine good actions to reduce or anticipate the effects of drought. Nugroho et al. (2020), in his research stated that the global climate index, such as Southern Oscillation Index (SOI), Sea Surface Temperature Nino 3.4, Sea Surface Temperature Nino West, and Indian Ocean Basin-wide Warming (IOBW) affected rainfall patterns in the Special Region of Yogyakarta. The global climate index has Figure 1 Research Target area in central Java Province in Indonesia different effects on rainfall patterns, such as SOI and SST Central Java Province is divided into 29 districts and Nino 3.4 having a high negative correlation to rainfall 6 cities. The total area of Central Java is 3.25 million ha, distribution, which means that the increase in the value of which is around 25.04% of the area of Java and 1.70% of those indexs are causing drought in several regions in the total area of Indonesia (BPS, 2017a). Yogyakarta and Central Java Province, Indonesia. According to Class I Climatology Station Semarang, Globally, climate change has an impact on the average air temperature in Central Java in 2015 temperature and rainfall fluctuations, which resulted in ranged from 23°C to 28°C. Places that are located close the drying out of several reservoirs and changing to the beach have relatively high average temperatures. cropping patterns (Umar et al., 2019). Strauch et al. For the average humidity varies, from 69% to 83%. The (2015) stated that climate change was not only causes highest rainfall was recorded at the Banjarnegara drought, but can also cause an increase in rainfall due to meteorological station with 3624 mm and the most rainy extreme climatic conditions. Based on the research period was recorded at the Banjarnegara station during (Oktaviani et al., 2011), global climate change has a major impact on the agricultural sector, especially in 179 days (BPS, 2016). Indonesia. In 2030, it is predicted that an increase in 2.2 Rainfall data acquisition temperature of 0.8oC will result in a significant decrease The rainfall data was obtained from the National in rice, corn and soybean production. Center for Atmospheric Research (NCAR, 2019). Future climate prediction research is very meaningful Rainfall data was obtained from 87 data locations (Figure for developing strategies for protecting freshwater 2), having daily data for 35 years (1979-2013). ecosystems, and also to maintain and increase food Based on research that has been done, climatology production. Rainfall distribution prediction data obtained data from the National Center for Atmospheric Research 32 June, 2021 AgricEngInt: CIGR Journal Open access at http://www.cigrjournal.org Vol. 23, No. 2 is better than the nearest weather station (Fuka et al., 35-years rainfall yearly data and was divided into seven 2014) since CFSR climate data has advantages over data groups, namely (1979 – 1983), (1984 – 1888), (1989 conventional data because it provides a complete set of – 1993), (1994 – 1998), (1999 – 2003), (2004 – 2008), for climate data (Dile and Srinivasan, 2014). model training and (2009 – 2013) for validation of the model. ANFIS models were made as many as 87 models, the same as the number of station data in Central Java Province, Indonesia. Figure 4 ANFIS modeling scheme Data were grouped due to meet the requirements of ANFIS model in recognizing patterns of large amounts of Figure 2 Rainfall data location historical data, and in order not to lose the value of 2.3 ANFIS model generation and rainfall data historical rainfall data from 1979 – 2013. Models are prediction evaluated using Root Mean Square Error (RMSE) Adaptive Neuro Fuzzy Inference System (ANFIS) equation as follows: belongs to hybrid system and class of adaptive networks that incorporate both neural networks and fuzzy logic ( , , ) = (1) principles (Jang, 1993). 푛 2 ∑푡=1 푥1 푡 − 푥2 푡 ANFIS model generation used the membership 푅푀푆퐸 � After getting the model with푛 the smallest RMSE function type of gbellmf/ generalized bell-shaped value, the model is tested to predict rainfall data and membership function and using Sugeno fuzzy inference compared with the actual data. The parameter used as a system. The gbellmf type was the best (with the smallest reference that the model is good and can be used to RMSE value) compared to using other function types. predict the future rainfall data is the value of the The ANFIS model was generated using five inputs and coefficient of determination (R2), which is the square of one output, and has 243 rules (hidden layer), the structure the correlation equation (R): of the model can be seen in Figure 3. ) = , , 푛 2 푡=1 1 푡 2 푡 1 2 (2) ∑ , 푥 푥 − 푛푥 푥 , 푅 푛 2 2 푛 2 2 2.4 Rainfall�∑푡 =data1 푥1 tra푡 −nsformation푛푥1 �∑푡= 1 푥2 푡 − 푛푥2 Predicted rainfall data that has been obtained for each data station, are changed into spatial data using Inverse Distance Weighting (IDW) interpolation in ArcGIS software.
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