The aims of this study were to evaluate five infiltration models for mineral soils in the tropics with different land use types, such as settlements, plantations, rice fields, and forests. The infiltration models evaluated were Green–Ampt, Kostiakov, Kostiakov–Lewis, Philip, and Horton. The research was conducted at the Amprong watershed, Malang, Indonesia. The infiltration rate of the thirteen soil samples was analysed. The infiltration was tested using Turf-Tech infiltrometer. Moreover, each soil sample was tested in terms of the bulk density, specific gravity, porosity, soil moisture, and soil texture. The results of the study indicate that there is no significant difference (α = 5%) in the infiltration rate among the five models of infiltration. The infiltration rate in the study site was considered fast. Three models exhibiting the best performance are Kostiakov, Kostiakov–Lewis, and Horton model, respectively. The highest infiltration rate occurred in the forest land use while the lowest occurred in the rice field land use. The results of this study suggest that the infiltration model parameters correlate closely with the initial infiltration rate (fo) and the final infiltration rate (fc). In other words there is a correlation between the soil's ability to absorb water (representing the capillary force or horizontal flow) at the beginning of the infiltration (fo) and the gravity or the vertical flow upon reaching the final infiltration rate (fc).
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.
Pos penakar hujan di Indonesia lokasinya masih kurang tersebar merata, padahal data hujan yang dihasilkan sangat penting. Maka diperlukan analisis validasi dengan data satelit TRMM karena dapat mencakup wilayah luas, tersedia secara near real-time dan aksesnya yang cepat. Penelitian ini bertujuan untuk memvalidasi data satelit dengan data observasi di DAS Grindulu yang datanya dianggap lengkap dan dapat diandalkan. Nantinya digunakan untuk mengantisipasi data curah hujan observasi yang mungkin error atau tidak tersedia. Metode validasi yang digunakan berupa Root Mean Squared Error (RMSE), Uji Kesalahan Relatif (KR), Nash Sutcliffe Efficiency (NSE) serta Koefisien Korelasi (R). Penelitian ini menggunakan dua tahap perhitungan, yaitu analisis validasi data tidak terkoreksi dan data terkoreksi, dimana data terkoreksi dilakukan kalibrasi data terlebih dahulu, hasil dari validasi data TRMM terkoreksi terbaik terdapat pada periode bulanan dengan rentang kalibrasi 9 tahun dan validasi 1 tahun dengan hasil NSE = 0,929; R = 0,969; RMSE = 46,48; KR = 8,9%. Hasil tersebut menunjukkan bahwa data TRMM terkoreksi menghasilkan nilai yang lebih baik dibandingan data TRMM tidak terkoreksi karena memiliki nilai NSE dan R yang mendekati satu dan nilai RMSE dan Kesalahan Relatifnya rendah. Secara kesluruhan, dapat disimpulkan bahwa data TRMM dapat digunakan sebagai data alternatif hidrologi di DAS Grindulu.
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