Model Prototipe Alih Ragam Hujan Ke Debit Menggunakan Data Satelit TRMM Dan Jaringan Syaraf Tiruan

Ery Suhartanto; Ussy Andawayanti; Rahmah Dara Lufira; Azhar Adi Darmawan; Angelina Satya Putri

doi:10.29103/tj.v15i1.1219

Model Prototipe Alih Ragam Hujan Ke Debit Menggunakan Data Satelit TRMM Dan Jaringan Syaraf Tiruan

Ery Suhartanto, Ussy Andawayanti, Rahmah Dara Lufira, Azhar Adi Darmawan, Angelina Satya Putri

Abstract

Abstrak

Ketersediaan dan akurasi data hujan maupun debit menjadi masalah umum di setiap DAS termasuk Sub DAS Lesti. Penelitian ini fokus pada kalibrasi dan validasi data satelit TRMM terhadap pos hujan lapangan. selain itu, bertujuan untuk mengembangkan model prototipe alih ragam hujan ke debit menggunakan JST. Pemodelan ini memanfaatkan data masukan hidrologi, termasuk data satelit TRMM, hari hujan, evaporasi, dan penggunaan lahan, serta data target debit dari Sub DAS Lesti. Hasil kalibrasi dan validasi data satelit TRMM menghasilkan nilai NSE sebesar 0,97 (sangat baik) dan koefisien korelasi (R) sebesar 1,00 (sangat kuat). Selain itu, hasil pemodelan diperoleh kalibrasi terbaik model prototipe yang mengkonversi data hujan menjadi debit menggunakan JST dengan fungsi transfer logsig, menghasilkan nilai koefisien korelasi R = 0,98897 (sangat kuat) dengan skema arsitektur jaringan 8-2-10-1 (terdiri dari delapan lapisan masukan, dua lapisan tersembunyi, sepuluh neuron, satu lapisan keluaran) pada 3000 epochs.

Kata kunci: Hujan, Debit, TRMM, Jaringan Syaraf Tiruan

Abstract

The availability and accuracy of rain and discharge data is a common problem in every watershed, including the Lesti sub-watershed. This research focuses on the calibration and validation of TRMM satellite data on field rain posts. Apart from that, it aims to develop a prototype model for transferring rainfall variations to discharge using ANN. This modeling utilizes hydrological input data, including TRMM satellite data, rainy days, evaporation, and land use, as well as discharge target data from the Lesti Sub-watershed. The results of calibration and validation of TRMM satellite data produced an NSE value of 0.97 (very good) and a correlation coefficient (R) of 1.00 (very strong). In addition, the modeling results obtained the best calibration of the prototype model which converts rain data into discharge using ANN with the logsig transfer function, producing a correlation coefficient value of R = 0.98897 (very strong) with an 8-2-10-1 network architecture scheme (consisting of eight input layers, two hidden layers, ten neurons, one output layer) at 3000 epochs.

Keywords: Rainfall, Discharge, TRMM, Artificial Neural Network

Keywords

Rainfall, Discharge, TRMM, Artificial Neural Network

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DOI: https://doi.org/10.29103/tj.v15i1.1219