Analisa Limpasan Berdasarkan Curah Hujan Menggunakan Model Artifical Neural Network (Ann) Di Sub Das Brantas Hulu

Abstract

ABSTRAK: Data debit biasanya tersedia lebih sedikit dibandingkan data curah hujan, sehingga perlu dicari suatu hubungan antara aliran sungai yang diterapkan dalam periode tersedia data curah hujan di suatu wilayah DAS. Tujuan dari studi ini adalah untuk mengetahui kesesuaian metode berdasarkan analisis validasi data antara debit pengamatan dengan debit model. Metode yang dilakukan dengan pemodelan debit berdasarkan curah hujan dengan model Artificial Neural Network (ANN) program MATLAB R2014b. Sub DAS Brantas Hulu digunakan sebagai studi kasus karena sering mengalami permasalahan limpasan. Validasi dari metode ANN diuji dengan Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Koefisien Korelasi (R) dan Kesalahan Relatif (KR). Dari hasil kalibrasi menggunakan Model ANN diperoleh data yang paling baik terdapat pada data lima tahun epoch 500. Hasil verifikasi berdasarkan nilai R mempunyai hubungan yang relatif baik antara debit pengamatan dengan debit model. Hasil validasi menunjukkan kevalidan pada data satu tahun epoch 500.Kata kunci: limpasan, model artifical neural network (ANN), uji nash sutchlife efficient (NSE), koefisien korelasi (R). ABSTRACT:Discharge data is usually less available than rainfall data, so it is necessary to find a relationship between river flows that are applied in the period available rainfall data in a watershed area. The purpose of this study is to determine the suitability of the method based on the analysis of data validation between the observed discharge and the model discharge. The method is done by modeling the discharge based on rainfall with the Artificial Neural Network (ANN) MATLAB R2014b program. The Upper Brantas Watershed is used as a case study because it often has runoff problems. Validation of the ANN method was tested with Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Correlation Coefficient (R) and Relative Error (KR). From the results of calibration using the ANN Model, the best data is found in the five years data of epoch 500. Verification results based on the value of R have a relatively good relationship between observation discharges with model discharges. The validation results show the validity in a year data of epoch 500.

Estimation of Flow Discharge Model at Temef Watershed - East Nusa Tenggara Using TRMM Satellite Data

“…The purpose of the consistency test is to determine the irregularities that occur in each rain station post with the other rain station posts in the vicinity, this test uses a double mass curve for observation and TRMM JAXA stations [3,9,18].…”

“…The homogeneity was employed to ensure the number of populations to be measured is homogeneous. The homogeneity test was conducted on both observation stations and TRMM JAXA rainfall data includes trend absence test, stationary test, and persistence test [3,9,18].…”

“…ANN (Artificial Neural Network) analysis uses a Matlab program that serves to model field debit data [3,15]. This data input consists of rainfall, amount of rainy day, and runoff coefficient data.…”

“…Hence, the quality and quantity of rainfall data become the main consideration to acquire high accuracy of estimated discharge in a river [4][5][6]. Rainfall will be divided into several processes, some will experience evaporation, others become surface runoff, and some become infiltration and percolation [3]. The stages of building an Artificial Neural Network (ANN) model are training and testing [16].…”

Perbandingan Metode Evaporasi Potensial Di Badan Meteorologi Klimatologi Dan Geofisika Sawahan Kabupaten Nganjuk, Jawa Timur

“…Uji Efisiensi Nash-Sutcliffe (ENS) adalah uji yang akan digunakan untuk mengetahui kemiripan data dari hasil analisa dengan data. Nilai Nash-Sutcliffe (ENS) diperoleh dengan menggunakan persamaan berikut (Suhartanto, Cahya, and Maknun 2019):…”