Prediction of Water Discharge and Sediment in Teak Forested Areausing Artificial Neural Network Model
Downloads
Information on the relationship of rainfall with discharge and sediment are required in watershed management.This relationship is known to be highly nonlinear and complex. Although discharge and sediment has been monitored continuously, but sometimes the information is not or less complete. In this condition, modeling is indispensable. The research objective is to create a model to predict the monthly direct runoff and sediment using Artificial Neural Network (ANN).The model was tested using rainfall data at t-3 and t-4 as input, and discharge and sediment at t+3 and t+4 as output. The data used is the data from 2001 to 2014. The results showed that of some models tested there are two models for the prediction of discharge and two models for sediment.The model was chosen because it has the smallest MSE, the largest R2and satisfying K (0.5 to 0.65).Thus,these models can be used to predict discharge andsediment for a period of t+3 and t+4.Prediction of discharge of t+3 and t+4 may use Q t+3= 0,64 Q t-3+ 0,05 and Q t+4= 0,65 Q t-4+ 0,074 res pectively, while for predicting sediment of t+3 and t+4 may use equations QS t+3= 0,45 QS t-3+ 0,052 and QS t+4= 0,45 QS t-4+ 0,052. This ANN modeling can be applied to predict the flow and sediment in other locations with an architecture adapted to the conditions of available data.
Adamala, S., Raghuwanshi, N. S., Mishra, A. dan Tiwari, M. K. (2014). Evapotranspiration Modeling Using Second-Order Neural Networks. Journal Hydrology Engineering 2014, 19(6), 1131-1140.
Agarwal, A., Rai, R. K. dan Upadyay, A. (2009). Forecasting of Runoff and Sediment Yield Using Artificial Neural Networks J. Water Resource and Protection, 1, 368-375.
Albaradeyia, I., Hani, A. dan Shahrour, I. (2011). WEPP and ANN Models for Simulating Soil Loss and Runoff in a Semi-Arid Mediterranean Region. Environ Monit Assess, 180, 537-556.
Peraturan Pemerintah Republik Indonesia tentang Pengelolaan Daerah Aliran Sungai Nomor 37 Tahun 2012 (2012).
Arif, F. M., Gernowo, R., Setyawan, A. dan Febrianty, D. (2012). Analisa data curah hujan stasiun klimatologi Semarang dengan model jaringan syaraf tiruan. Berkala Fisika, 15(1), 21-26.
Asadollahfardi, G., Taklify, A. dan Ghanbari, A. (2012). Application of Artificial Neural Network to Predict TDS in Talkheh Rud River. Journal of Irrigation and Drainage Engineering, 138(4), 363-370.
Aziz, K., Rahman, A., Fang, G. dan Shrestha, S. (2014). Application of artificial neural networks in regional flood frequency analysis: a case study for Australia. Stoch Environ Res Risk Assess 28, 541-554.
Chow, M. F., Yusop, Z. dan Toriman, M. E. (2012). Modelling Runoff Quantity and Quality in Tropical Urban Catchments Using Storm Water Management Model. Int. J. Environ. Sci. Technol., 9, 737-748.
Dariah, A., Subagyo, H., Tafakresnanto, C. dan Marwanto, S. (2004). Kepekaan Tanah Terhadap Erosi Konservasi Tanah pada Lahan Kering Berlereng. Bogor: Pusat Penelitian dan Pengembangan Tanah dan Agroklimat.
Demuth, H. dan Beale, M. (2002). Neural Network Toolbox For Use with MATLAB. Massachusetts: The Mathworks Inc.
El-shafie, A., Mukhlisin, M., Najah, A. A. dan Taha, M. R. (2011). Performance of Artificial Neural Network and Regression Techniques for Rainfall-Runoff Prediction.International Journal of the Physical Sciences 6(8),1997-2003.
Ghalkhani, H., Golian, S., Saghafian, B., Farokhnia, A. dan Shamseldin, A. (2013). Application of surrogate artificial intelligent models for real-time flood routing. Water and Environment Journal, 27, 535-548.
Huang, Y. dan Liu, L. (2010). Multiobjective Water Quality Model Calibration Using a Hybrid Genetic Algorithm and Neural Network–Based Approach. Journal of Environment Engineering,136(10), 1020-1031.
Jahanbani, H. dan El-Shafie, A. H. (2011). Application of artificial neural network in estimating monthlytime series reference evapotranspiration with minimum and maximum temperatures. Paddy Water Environ, 9, 207-220.
López-Vicente, M. dan Navas, A. (2010). Relating Soil Erosion and Sediment Yield to Geomorphic Features and Erosion Processes at the CatchmentScale in the Spanish Pre-Pyrenees. Environmental Earth Sciences, 61, 143-158.
Mehr, A. D., Nazemosadat, M. J., Kahya, E. dan Sahin, A. (2015). Successive-Station Monthly Streamflow Prediction Using Different Artificial Neural Network Algorithms. International Journal Environment Science Technology, 12, 2191-2200.
Mittal, P., Chowdhury, S., Roy, S., Bhatia, N. dan Srivastav, R. (2012). Dual Artificial Neural Network for Rainfall-Runoff Forecasting. Journal of Water Resource and Protection, 4, 1024-1028.
Moriasi, D. N., Arnold, J. G., Liew, M. W. V., Bingner, R. L., Harmel, R. D. dan Veith, T. L. (2007). Model Evaluation on Guedelines fo Systematic Quantification of Accuracy in Watershed Simulation. Transactions of the ASABE, 50(3), 885-900.
Nash, J. E. dan Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I -A discussion of principles. Journal of Hydrology, 10(3), 282-290.
Nourani, V. dan Kalantari, O. (2010). Integrated Artificial Neural Network for Spatiotemporal Modeling of Rainfall Runoff SedimentProcesses. Environtmental Engineering Science, 27(6), 411-422.
Nunes, A. N., de Almeida, A. C. dan Coelho, C. O. A. (2011). Impacts of land use and cover type on runoff and soil erosion in a marginal area of Portugal. Applied Geography,31, 637-699.
Partal, T. (2009). River flow forecasting using different artificial neural network algorithms and wavelet transform. Can. J. Civ. Eng. , 36, 26-39.
Peraturan Pemerintah Republik Indonesia tentang Pengelolaan Daerah Aliran Sungai Nomor 37 Tahun 2012 (2012).
Phukoetphim, P., Shamseldin, A. Y. dan Melville, B. W. (2014). Knowledge Extraction from Artificial Neural Networks for Rainfall-Runoff Model Combination Systems. Journal Hydrology Engineering, 19(7), 1422-1429.
Pramono, I. B., Murtiono, U. H., Supangat, A. B. dan Mastur. (2000). Petunjuk Teknis Analisis Data Hujan dan Aliran Sungai. Info DAS, 9, 1-39.
Pramono, I. B. dan Wahyuningrum, N. (2010). Luas optimal hutan jati sebagai pengatur tata air di Daerah Aliran Sungai (DAS) berbahan induk kapur.Jurnal Penelitian Hutan dan Konservasi Alam, 7(5).459-467.
Prasetyo, B. H. (2007). Perbedaan sifat-sifat tanah vertisol dari berbagai bahan induk. Jurnal Ilmu-ilmu Pertanian Indonesia, 9(1), 20-31.
SDA, D. (2013). Kebutuhan air baku nasional akan meningkat lima kali lipat dalam lima belas tahun ke depan.Retrieved 25 April, 2015, from http://sda.pu.go.id/index.php/berita-sda/datin-sda/item/346-kebutuhan-air-baku-nasional-akan-meningkat-lima-kali-lipat-dalam-lima-belas-tahun-ke-depan
Shamsuddin, S. A., Yusup, Z. dan Noguchi, S. (2014). Influence of plantation establishment on discharge characteristics in a small catchment of tropical forest. International Journal of Forestry Research.2014, 1-10. http://dx.doi.org/10.1155/2014/408409
Sikorska, A. E., Scheidegger, A., Banasik, K. dan Rieckermann, J. (2012). Bayesian Uncertainty Assessment of Flood Predictions in Ungauged Urban Basins for Conceptual Rainfall-Runoff Model. Hydrology Earth System Science, 16, 1221-1236.
Tiwari, M. K., Song, K.-Y., Chatterjee, C. dan Gupta, M. M. (2012). River-Flow Forecasting Using Higher-Order Neural Networks. Journal Hydrology Engineering, 17(5), 655-666.
Vafakhah, M. (2012). Application of artificial neural networks and adaptive neuro-fuzzy inference system models to short-term streamflow forecasting.Can. J. Civ. Eng. , 39, 402-414.
Voinov, A. (2008). System science and modelling for ecologycal economics. Amsterdam, Boston, Heidelberg, London, New York, Oxford: Academic Press, Elsevier.
Widodo, P. P. dan Handayanto, R. T. (2012). Penerapan Soft Computing dengan Mathlab (Revisi ed. Vol. I). Bandung: Rekayasa Sains.
Woznicki, S. dan Nejadhashemi, A. (2013). Spatial and Temporal Variabilities of Sediment Delivery Ratio. Water Resources Management, 27(7), 2483-2499.
Yulianto, F. E., Hadiani, R. R. dan Setiono. (2014). Pemodelan Hujan Debit untuk Analisis Kekeringan pada DAS Temon. Matriks Teknik Sipil, 2(1), 100-107.
Zanetti, S. S., Sousa, E. F., Oliveira, V. P. S., Almeida, F. T. dan Bernardo, S. (2007). Estimating Evapotranspiration Using Artificial Neural Network and Minimum Climatological Data. Journal Irrigation and Drainage Engineering, 133(2), 83-89.
