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Teknik Penggerombolan Fuzi untuk Pewilayahan Curah Hujan di Sentra Produksi Padi Aris Pramudia; Vonny Koesmaryono; Irsal Las; Tania June; I Wayan Astika
Jurnal Ilmu Pertanian Indonesia Vol. 12 No. 3 (2007): Jurnal Ilmu Pertanian Indonesia
Publisher : Institut Pertanian Bogor

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Rainfall zoning analysis with fuzzy clustering method has been performed at the centre of paddy area in the northern coast of Banten Province and West Java Province. Rainfall data recorded in the 1980-2006 period from 62 rainfall stations in the northern coast of Banten Province and from 75 rainfall stations at Karawang and Subang in the northern coast of West Java Province have been used in this analysis. For the first analysis a calculation of arithmetic mean values representing EI-Nino, La-Nina and Normal condition has been performed. Next, a fuzzy clustering analysis is applied to these mean values. The clustering analysis consists of two steps. First, a symmetric and reflective compatibility relation matrix describing a distance function between rainfall stations is calculated. Second, a fuzzy equivalency relationship i.e. a transitive approach of fuzzy compatibility matrices is determined. The results of analysis indicate a difference in the equivalency level among the stations under the EI-Nino, La-Nina and Normal conditions in the northern coast of Banten Province and West Java Province. Based on the 75°/o equivalency level, in the northern coast of Banten area can be grouped into four rainfall zones under EI-Nino condition, two zones under La-Nina condition and three zones under Normal condition. On the other hand, in the northern coast of West Java area can be grouped into three zones under EINino condition, two zones under La-Nina condition, and four zones under Normal condition.Keywords: Arithmetic means values, EI-Nino, La-Nina, Fuzzy clustering, Rainfall zoning

Rainfall Prediction Using Artificial Neural Network Resti Salmayenti; Rahmat Hidayat; Aris Pramudia
Agromet Vol. 31 No. 1 (2017): JUNE 2017
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Artificial neural network (ANN) is widely used for modelling in environmental science including climate, especially in rainfall prediction. Current knowledge has used several predictors consisting of historical rainfall data and El Niño Southern Oscillation (ENSO). However, rainfall variability of Indonesian is not only driven by ENSO, but Indian Ocean Dipole (IOD) could also influence variability of rainfall. Here, we proposed to use Dipole Mode Index (DMI) as index of IOD as complementary for ENSO. We found that rainfall variability in region with a monsoonal pattern has a strong correlation with ENSO and DMI. This strong correlation occurred during June-November, but a weak correlation was found for region with rainfall’s equatorial pattern. Based on statistical criteria, our model has R2 0.59 to 0.82, and RMSE 0.04-0.09 for monsoonal region. This finding revealed that our model is suitable to be applied in monsoonal region. In addition, ANN based model likely shows a low accuracy when it uses for long period prediction.

The research analyzed rainfall data from Subang and Karawang as the centers of rice production in West Java. The objectives of this research were to (1) develop monthly rainfall prediction model for predicting the next four months rainfall, (2) develop a next three months rice yield prediction model and (3) estimate the availability of rice in Subang and Karawang as a function of monthly rainfall. Both rainfall and rice yield prediction models were built by ANN technique. ANN rainfall predi Magfira Syarifuddin; Yonny Koesmaryono; Aris Pramudia
Forum Pasca Sarjana Vol. 32 No. 3 (2009): Forum Pascasarjana
Publisher : Forum Pasca Sarjana

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The research analyzed rainfall data from Subang and Karawang as the centers of rice production in West Java. The objectives of this research were to (1) develop monthly rainfall prediction model for predicting the next four months rainfall, (2) develop a next three months rice yield prediction model and (3) estimate the availability of rice in Subang and Karawang as a function of monthly rainfall. Both rainfall and rice yield prediction models were built by ANN technique. ANN rainfall prediction model was applied at six rainfall stations in Subang and Karawang which are Cigadung, Karawang, Rawamerta, Subang, Sindanglaya and Ciseuti. It was developed by including 7-8 variables (X) at input layer and 6-10 nodes at a single hidden layer. Variables at input layer are month code (t) as X1, monthly rainfall values at t, t+1, t+2, and t+3 as X2, X3, X4, and X5 respectively, SOI at t as X6 and SST anomalies at t and t+3 as X7 and X8. Rice yield model was built to estimate the rice production at t+3 by using four variables at input layer which are t, t+1, t +2 and t+3 as X1, X2, X3 and X4 and also included 6-8 nodes at hidden layer. The results of this research found that the ANN model could accurately predict the monthly rainfall in all stations with the R2 values ranged from 64-96%, and maximum errors of each month rainfall ranged from 0.4-3.4 mm/month. Rainfall model predicted that there were trends of Above Normal (AN) rainfall at Karawang and Rawamerta stations in dry season, while at four stations in Subang region would experience Below Normal (BN) rainfall in dry season. Based on 2009 rainfall prediction, the rice yield model predicted highest rice production to happen during February and March 2009 at values of 299.294 ton and 329.082 ton. Key words: artificial neural network, rainfall prediction, rice production

The paper describes about rainfall zoning and rainfall prediction modeling and its use for rice availability and vulnerability analysis. The study used rainfall data from Station Baros (Banten region), Station Karawang and Station Kasomalang Subang (Northern Coastal of West-Java), and Station Tarogong (Garut). Fuzzy clustering methods, that was applied for rainfall zoning, used the representative data for El-Nino, La-Nina and normal means condition during 1980-2006 periods. Neural network ana Aris Pramudia; Yonny Koesmaryono; Irsal Las; Tania June; I Wayan Astika; Eleonora Runtunuwu
Forum Pasca Sarjana Vol. 31 No. 2 (2008): Forum Pascasarjana
Publisher : Forum Pasca Sarjana

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The paper describes about rainfall zoning and rainfall prediction modeling and its use for rice availability and vulnerability analysis. The study used rainfall data from Station Baros (Banten region), Station Karawang and Station Kasomalang Subang (Northern Coastal of West-Java), and Station Tarogong (Garut). Fuzzy clustering methods, that was applied for rainfall zoning, used the representative data for El-Nino, La-Nina and normal means condition during 1980-2006 periods. Neural network analysis technique was applied for rainfall prediction modeling. Training set of the model based on the rainfall data of 1990-2002 periods, and validation model based on data of 2003-2006 periods. The model were used to predict the rainfall of 2007-2008 periods. The distibution of equivalence value between rainfall stations was very variative under El-Nino, La-Nina and Normal condition. On the certain of equivalence level it could be derivated some different rainfall zone under El-Nino, La-Nina and normal condition. Model training set could explain 88% of Baros rainfall variability, 89% of Karawang rainfall variability, and 72% of Kasomalang rainfall variability. At Baros, Karawang and Subang, rainfall was predicted to be increased on November 2007-February 2008 period, and to be decreased on the March-June 2008, and to be increased on July-November 2008. The rainfall decreasing on the March-June would carry a losses of rice production up to 25%. But, applying the well irrigation management and suitable growing periods could decrease and mitigate the decreasing of paddy production. Key words: rainfall prediction model, fuzzy clustering, neural network analysis, rice vulnerability

Validation of Evapotranspiration Prediction Model: An Effort to Complete the National Climate Database System ELEONORA RUNTUNUWU; HARIS SYAHBUDDIN; ARIS PRAMUDIA
Jurnal Tanah dan Iklim (Indonesian Soil and Climate Journal) No 27 (2008): Juli 2008
Publisher : Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21082/jti.v0n27.2008.%p

To cope with limited evapotranspiration data, recently, there are many evapotranspiration estimation methods have been developed. Those methods were generally developed in sub tropic region when climate is not similar with Indonesia and the methods may not be applied directly. Validation of several estimation methods including Blaney Criddle, Radiation, Penman, and Pan Evaporation have been done in Cikarawang (Bogor) and Ciledug (Tangerang). The average correction factor andcorrelation coefficient (r) were respectively 1.83 for Blaney Criddle method (r = 0.97); 1.90 for Radiation method (r=0.97); 1.10 for Penman method (r=0.96), and 1.81 for Pan Evaporation method (r=0.98). Penman is the best method with regard on the smallest correction factor especially for station with complete climatic data. Since all methods have correlationcoefficient of more than 0.95, those methods can be used to estimate evapotranspiration based on the available climatic data. The present study used the Penman and Pan Evaporation methods to estimate evapotranspiration in Bogor for period of 1995-2005. The study provides insight into alternative to estimate the evapotranspiration for the area with no lysimeter. The method is selected by considering the available climatic data.

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