The Dee Investigation Simulation Program for Regulating Network (DISPRIN) model consists of eight tanks that are mutually interconnected. It contains 25 parameters involved in the process of transforming rainfall into runoff data. This complexity factor is the appeal to be explored in order to more efficiently. Parameterization process in this research is done by using Differential Evolution (DE) algorithm while parameters sensitivity analysis is done by using Monte Carlo simulation method. Software application models of merging the two concepts are called DISPRIN25-DE model and compiled using code program M-FILE from MATLAB. Results of research on Lesti watershed at the control point Tawangrejeni automatic water level recorder (AWLR) station (319.14 km2) in East Java Indonesia indicate that the model can work effectively for transforming rainfall into runoff data series. Model performance at the calibration stage provide value of NSE = 0.871 and PME = 0.343 while in the validation stage provide value of NSE = 0.823 and PME = 0.180. Good performance in the calibration process indicates that DE algorithm is able to solve problems of global optimization of the equations system with a large number of variables. The results of the sensitivity analysis of 25 parameters showed that 3 parameters have a strong sensitivity level, 7 parameters with a medium level and 15 other parameters showed weak sensitivity level to performance of DISPRIN model.
Markov Chain Model is a stochastic model for forecasting the river flow which in his analysis always involves a long series of historical data. In most studies the method is still highly theoretical and not fully applicable significantly due to the limited data in the field.This study is an attempt to optimize the application of Markov Chain Model for its functionality extensively to extrapolate data streams. The scope of this research is basically conducted a study on the relationship between the length of the historical flow data series with data quality prediction results. By knowing these characteristics, the error correction of analysis results can be expected due to data limitations, so that the Markov Chain Model can be widely applied to optimization of waterworks operations.Results for the Konto River and River showed that the prediction of flow Kwayangan next year with Markov chain models tend to give better results than the results of forecasting by conventional methods are widely applied. Markov model is good enough to predict the river flow has low flow fluctuations, but for a river flow fluctuated sharply less than satisfactory results. The length of data series ranges from 15 to 20 of the optimal inputs to produce a minimum error rate prediction. Accuracy of prediction result is not determined by the length of the input data series, but is determined by the nature of statistical data. Value of lag-1 correlation coefficient are large and small skewness coefficient of the historical data tends to give a satisfactory prediction results.Key words: river flow, data, prediktion, markov model.
Fundamental weakness of the tank model application is so much value parameters must firstbe defined simultaneously before the model was applied. This condition causes tank models areconsidered not efficient to solve practical problems. This research is an attempt to improve theperformance of Standard Tank Model that can be applied more effectively, especially for thetransformation of climate data into the stream data. The discussion focused on efforts to completethe system of equations in standard tank model using genetic algorithms for optimization parameters,so that the resulting equation system can determine the appropriate model parameters automaticallyat a watershed in the study. Standard tank model is a system composed tank 4 series and has 17parameters. Results of research on the Konto Watershed and the Lekso Watershed show thatStandard Tank Model-based Genetic Algorithm can present relationships very well climate data andstreams data. At the maximum generation value of 500 obtained root mean square error (RMSE) of0.241 m3/sec for the Konto Watershed and the Lekso Watershed of 0.30 m3/sec.Keywords: genetic algorithm, a standard tank model, optimization, parameters
Programasi Linear untuk Pencarian Diameter Pipa Optimal pada Sistem Jaringan Pipa Distribusi Air BersihLinear Programming for Search Optimum Diameter Pipe on Network Pipe Distribution Water SupplySuliantoJurusan Teknik Sipil-Fakultas Teknik Univeristas Muhammadiyah MalangAlamat korespondensi : Jalan Raya Tlogomas 246 Malang 65144AbstractIn determining the optimum pipe diameter in the activities of the water distribution system design is highly dependent on the objective function and design criteria expressed as a function of its boundary. On gravity flow conditions, the performance of a network can be assessed from the minimum cost of investment pipeline and high minimum pressure difference relative to each service node. Therefore, the purpose of optimization in designing pipelines with gravity flow directed towards minimizing both. Linear Programming (LP) is a method that is very popular and reliable in solving optimization problems containing linear functions. Besides the simplicity of its functions, the ease in completing the equation makes the LP system more attractive for application in solving various cases optimization. This work aims to determine the level of enforcement in solving optimization problems LP diameter pipe in the open pipe network in the water distribution system. Testing the model is done by using the data that is considered to represent a hypothetical pipeline on a flat service area (case-1) and a service area on the undulating topography (case 2). Results of the analysis in both cases shows that the LP models is quite consistent and can provide optimum solutions appropriate objective function and barrier function set.Keyword :Linear Program, diameter, pipelines, optimization.AbstrakUpaya menemukan diameter pipa optimum dalam kegiatan perancangan sistim distribusi air bersih sangat tergantung dari fungsi tujuan dan kriteria desain yang dinyatakan sebagai fungsi pembatasnya. Pada kondisi aliran gravitasi maka kinerja suatu jaringan dapat dinilai dari biaya minimum investasi pipa dan perbedaan minimum tinggi tekanan relatif pada setiap simpul layanan. Oleh sebab itu tujuan optimasi dalam merancang jaringan pipa dengan aliran gravitasi diarahkan pada upaya minimalisasi kedua hal tersebut. Linier Programming (LP) merupakan sebuah metode yang sangat populer dan handal dalam memecahkan masalah optimasi yang mengandung fungsi-fungsi linier. Disamping kesederhanaan fungsinya, faktor kemudahan dalam menyelesaikan sistim persamaan menjadikan LP semakin menarik untuk diterapkan dalam memecahkan berbagai kasus optimasi. karya ini bertujuan untuk mengetahui tingkat pemberlakuan LP dalam menyelesaikan masalah optimasi diameter pipa pada jaringan pipa terbuka dalam sistim distribusi air bersih. Pengujian model dilakukan dengan menggunakan data hipotetik yang dianggap mewakili jaringan pipa pada daerah layanan datar (kasus-1) dan daerah layanan pada topografi bergelombang (kasus-2). Hasil analisis pada kedua kasus tersebut menunjukkan bahwa model LP cukup konsisten dan dapat memberikan solusi optimum sesuai fungsi tujuan dan fungsi pembatas yang ditetapkan.Kata kunci : Program Linier, diameter, jaringan pipa, optimasi.
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