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All Journal Teras Jurnal Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Teknologi dan Rekayasa Sumber Daya Air
Putri, Angelina Satya
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Education Engineering Environmental Science Materials Science & Nanotechnology Physics

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Studi Kalibrasi Parameter NRECA Berbasis Algoritma Genetika untuk Pemodelan Curah Hujan-Debit di DAS Rejoso Putri, Angelina Satya; Suhartanto, Ery; Andawayanti, Ussy
Jurnal Penelitian Pendidikan IPA Vol 11 No 6 (2025): June
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i6.11091

Abstract

The Rejoso watershed in Pasuruan Regency is a critical water resource that supports various sectors, including agriculture and domestic needs. However, the imbalance between water demand and availability, exacerbated by insufficient discharge measurement infrastructure, necessitates alternative approaches to determine river discharge. This study utilizes the NRECA method combined with Genetic Algorithms (GA) to estimate river discharge by calibrating key hydrological parameters, Percent Sub-Surface (PSUB) and Ground Water Flow (GWF). Data from seven rainfall stations and AWLR Winongan were analyzed for the 2004-2023 period. Calibration of the NRECA model was carried out using the Nash-Sutcliffe Efficiency (NSE) and correlation coefficient (R), both achieving values close to 1, indicating an excellent model fit. The study highlights the applicability of GA for optimizing hydrological parameters and demonstrates the potential of the NRECA-GA method in improving discharge predictions in watersheds with limited data. These findings contribute to more effective and sustainable water resource management in the Rejoso watershed.
Model Prototipe Alih Ragam Hujan Ke Debit Menggunakan Data Satelit TRMM Dan Jaringan Syaraf Tiruan Suhartanto, Ery; Andawayanti, Ussy; Lufira, Rahmah Dara; Darmawan, Azhar Adi; Putri, Angelina Satya
Teras Jurnal : Jurnal Teknik Sipil Vol. 15 No. 1 (2025): Volume 15 Nomor 1, Maret 2025
Publisher : UNIVERSITAS MALIKUSSALEH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/tj.v15i1.1219

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
Perbandingan Metode Alih Ragam Hujan Menjadi Debit dengan FJ. Mock dan NRECA di DAS Rejoso Kabupaten Pasuruan Putri, Angelina Satya; Suhartanto, Ery; Fidari, Jadfan Sidqi
Jurnal Teknologi dan Rekayasa Sumber Daya Air Vol. 3 No. 2 (2023): Jurnal Teknologi dan Rekayasa Sumber Daya Air (JTRESDA)
Publisher : Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtresda.2023.003.002.07

Abstract

Pengalihragaman hujan menjadi debit adalah suatu proses permodelan yang mengubah data curah hujan menjadi data debit. Data debit pada DAS digunakan untuk kebutuhan makhluk hidup di sekitar. Pada kenyataannya, ketersediaan data debit sering kalitidak lengkap. Maka dari itu, dilakukan studi ini yaitualih ragam hujan-debit pada lokasi DAS Rejoso, Kabupaten Pasuruan. Metode yang digunakan adalahmetode FJ. Mock dan NRECA, yang nantinya keduametode tersebut dibandingkan dengan data AWLRpada DAS Rejoso. Pada hasil analisis didapatkanbahwa metode FJ. Mock dan NRECA merupakanmetode yang cocok diterapkan pada DAS Rejosodengan nilai NSE sebesar 0,936, nilai PBIAS sebesar1,351 dan nilai koefisien korelasi (r) sebesar 0,968Kondisi debit pada suatu DAS dipengaruhi olehpenggunaan lahan disekitarnya. Hubungan antaraperubahan penggunaan lahan dengan kondisi debitrerata tahunan DAS Rejoso menunjukkan nilai debityang cenderung menaik, ditinjau dari perbandingantahun 2006, 2010, 2015 dan 2020. The transformation of rain to discharge is a modeling process that converts rainfall data into discharge data. Discharge data in the watershed is used for the needs of living things in te vicinity. In fact, the availability of discharge data is often incomplete. Therefore, this study was carried out, namely the variation of rainfall-discharge at the location os the Rejoso Watershed, Pasuruan Regency. The method used is FJ. Mock and NRECA, which later the two methods will be compared wit AWLR data in the Rejoso watershed. The results of the analysis show that the FJ. Mock and NRECA are suitable methods to be appllied to the Rejoso watershed with an NSE value of 0,936, a PBIAS value of 1,351 and a correlation coefficient (r) value of 0,968. The use of the surrounding land influences the discharge condition in a watershed. The relationship changes in land use and condition of the annual average discharge of Rejoso watershed shows that the discharge value tends to decrease in terms of comparisons in 2006, 2010, 2015 and 2020.
Validation of NRECA Parameters for Rainfall-to-Discharge Modeling in the Rejoso Watershed Putri, Angelina Satya; Suhartanto, Ery; Andawayanti, Ussy
Jurnal Penelitian Pendidikan IPA Vol 11 No 5 (2025): May
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i5.11107

Abstract

The Rejoso Watershed in Pasuruan Regency is a water source for local needs and ecological balance. However, limited discharge data due to the absence of optimal measurement infrastructure poses a challenge for sustainable watershed management. This study aims to estimate river discharge using the NRECA method, with parameter optimization for PSUB and GWF achieved through a Genetic Algorithm. The novelty of this research lies in its integration of the NRECA method and Genetic Algorithm for improved discharge estimation in data-scarce regions. Calibration and validation were conducted using a 15:5 ratio, resulting in a Nash-Sutcliffe Efficiency (NSE) value of 0.5379, categorized as “Meets” based on the range defined (0.50–0.65), and a correlation coefficient of 0.7907, indicating a “Strong” linear relationship. Validation ensures the model's reliability beyond historical calibration data, addressing potential overfitting. These findings demonstrate the NRECA method's capability, supported by Genetic Algorithm optimization, as a practical alternative for discharge estimation in watersheds with limited data. Nevertheless, the model’s performance remains sensitive to input data quality, emphasizing the need for better rainfall data. This approach contributes to improving water resource management in Rejoso and similar watersheds facing data limitations.
Co-Authors Azhar Adi Darmawan Ery Suhartanto Jadfan Sidqi Fidari Rahmah Dara Lufira Ussy Andawayanti