Tulisan ini merupakan catatan pribadi.
Dan dapat berubah-ubah.

Pada halaman ini tersedia daftar makalah (paper) yang saya gunakan atau kumpulkan terkait penggunaan machine learning di bidang sumberdaya air. Terdapat juga beberapa makalah yang menurut saya menarik meski tidak sesuai topik yang ingin dikaji. Daftar makalah diurutkan berdasarkan tahun publikasi.

Halaman ini saya gunakan untuk merekam jejak bacaan yang kemungkinan saya jadikan referensi. Kemungkinan akan saya buat halaman terpisah untuk makalah/materi yang saya gunakan dalam penelitian.

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Daftar Isi

  1. Makalah yang sudah dibaca
  2. Dropbox
Judul Makalah
Tahun Publikasi Publikasi/Jurnal Pranala DOI Print Code Tanggal Selesai Dibaca
Penulis

Makalah yang sudah dibaca

Berikut daftar makalah yang telah saya baca. Daftar diurutkan berdasarkan tahun publikasi.

Catatan: Klik DOI untuk memperoleh akses makalah.

Berdasarkan Urutan Bacaan
19
Nov
2019
NeuralHydrology - Interpreting LSTMs in Hydrology (2019)
18
Nov
2019
Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks (2018)
29
Apr
2019
Radial Basis Function Neural Network for Modeling Rating Curves (2003)
16
Apr
2019
A Ranking of Hydrological Signatures Based on Their Predictability in Space (2018)
15
Apr
2019
Support vector machine applications in the field of hydrology: A review (2014)
28
Mar
2019
OPTIMIZATION RAINFALL-RUNOFF MODELING FOR CIUJUNG RIVER USING BACK PROPAGATION METHOD (2018)
19
Mar
2019
Urban Water Flow and Water Level Prediction Based on Deep Learning (2017)
13
Mar
2019
Prospective Interest of Deep Learning for Hydrological Inference (2017)
04
Mar
2019
Artificial Neural Networks in Hydrology. II: Hydrologic Applications (2000)
27
Feb
2019
Artificial Neural Networks in Hydrology. I: Preliminary Concepts (2000)
29
Aug
2018
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2018)
16
Aug
2018
Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan - Indonesia (2015)
14
Aug
2018
What Large Sample Size Is Sufficient for Hydrologic Frequency Analysis?—A Rational Argument for a 30-Year Hydrologic Sample Size in Water Resources Management (2018)
03
Aug
2018
MODEL PERAMALAN DEBIT ALIRAN SUNGAI MENGGUNAKAN METODE GABUNGAN SELF ORGANIZING MAPS – ARTIFICIAL NEURAL NETWORK (Studi Kasus: Sungai Tapung Kiri) (2017)
An overview of the role of Machine Learning in hydraulic and hydrological modeling (2018)
NeuralHydrology - Interpreting LSTMs in Hydrology
2019 Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Lecture Notes in Computer Science DOI P.11.02 19 Nov 2019
Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S., Klambauer, G.
Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks
2018 Hydrology and Earth System Sciences DOI P.09.04 18 Nov 2019
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., Herrnegger, M.
A Ranking of Hydrological Signatures Based on Their Predictability in Space
2018 Water Resources Research DOI P.07.03 16 Apr 2019
Addor, N., Nearing, G., Prieto, C., Newman, A.J., Le Vine, N., Clark, M.P.
OPTIMIZATION RAINFALL-RUNOFF MODELING FOR CIUJUNG RIVER USING BACK PROPAGATION METHOD
2018 SINERGI DOI P.07.05 28 Mar 2019
Sebayang, I.S.D., Suroso, A., Laoli, A.G.
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists
2018 Water Resources Research DOI P.08.10 29 Aug 2018
Shen, C.
What Large Sample Size Is Sufficient for Hydrologic Frequency Analysis?—A Rational Argument for a 30-Year Hydrologic Sample Size in Water Resources Management
2018 Water DOI P.08.14 14 Aug 2018
Li, H., Sun, J., Zhang, H., Zhang, J., Jung, K., Kim, J., Xuan, Y., Wang, X., Li, F.
An overview of the role of Machine Learning in hydraulic and hydrological modeling
2018 engrXiv DOI P.08.16
Carbajal, J. P., & Bellos, V.
Urban Water Flow and Water Level Prediction Based on Deep Learning
2017 Machine Learning and Knowledge Discovery in Databases DOI P.07.02 19 Mar 2019
Assem, H., Ghariba, S., Makrai, G., Johnston, P., Gill, L., Pilla, F.
Prospective Interest of Deep Learning for Hydrological Inference
2017 Groundwater DOI P.A.m3 13 Mar 2019
Marçais, J., de Dreuzy, J.-R.
MODEL PERAMALAN DEBIT ALIRAN SUNGAI MENGGUNAKAN METODE GABUNGAN SELF ORGANIZING MAPS – ARTIFICIAL NEURAL NETWORK (Studi Kasus: Sungai Tapung Kiri)
2017 Jom FTEKNIK P.08.17 03 Aug 2018
Tsauri, M.I.I., Suprayogi, I., Fauzi, M.
Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan - Indonesia
2015 Procedia Computer Science DOI P.08.20 16 Aug 2018
Mislan, Haviluddin, Hardwinarto, S., Sumaryono, Aipassa, M.
Support vector machine applications in the field of hydrology: A review
2014 Applied Soft Computing DOI P.07.04 15 Apr 2019
Raghavendra. N, S., Deka, P.C.
Radial Basis Function Neural Network for Modeling Rating Curves
2003 Journal of Hydrologic Engineering DOI P.08.13 29 Apr 2019
Sudheer, K.P., Jain, S.K.
Artificial Neural Networks in Hydrology. II: Hydrologic Applications
2000 Journal of Hydrologic Engineering DOI P.A.m1 04 Mar 2019
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology,
Artificial Neural Networks in Hydrology. I: Preliminary Concepts
2000 Journal of Hydrologic Engineering DOI P.A.m2 27 Feb 2019
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology

Jumlah: 15 Makalah

Dropbox

Berikut daftar makalah yang saya kumpulkan untuk dibaca. 😁.

Explaining and Interpreting LSTMs
2019 Explainable AI: Interpreting, Explaining and Visualizing Deep Learning DOI P.11.01
Arras, L., Arjona-Medina, J., Widrich, M., Montavon, G., Gillhofer, M., Müller, K.-R., Hochreiter, S., Samek, W.
Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting
2019 Water DOI P.09.02
Le, Ho, Lee, Jung
Water quality prediction based on recurrent neural network and improved evidence theory: a case study of Qiantang River, China
2019 Environmental Science and Pollution Research DOI P.09.01
Li, L., Jiang, P., Xu, H., Lin, G., Guo, D., Wu, H.
Deep-Learning Approach to the Detection and Localization of Cyber-Physical Attacks on Water Distribution Systems
2018 Journal of Water Resources Planning and Management DOI P.08.19
Taormina, R., Galelli, S.
An IoT-Big Data Based Machine Learning Technique for Forecasting Water Requirement in Irrigation Field
2018 Research and Practical Issues of Enterprise Information Systems DOI P.08.15
Ahmed, F.
Water quality prediction method based on LSTM neural network
2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) DOI P.09.03
Wang, Yuanyuan, Zhou, J., Chen, K., Wang, Yunyun, Liu, L.
A Flood Forecasting Model Based on Deep Learning Algorithm via Integrating Stacked Autoencoders with BP Neural Network
2017 IEEE Third International Conference on Multimedia Big Data (BigMM) DOI P.08.18
Liu, F., Xu, F., Yang, S.
SVM or deep learning? A comparative study on remote sensing image classification
2017 Soft Computing DOI P.08.08
Liu, P., Choo, K.-K.R., Wang, L., Huang, F.
SOM-Based Decision Support System for Reservoir Operation Management
2017 Journal of Hydrologic Engineering DOI P.08.05
Rodríguez-Alarcón, R., Lozano, S.
Wetland Water-Level Prediction Using ANN in Conjunction with Base-Flow Recession Analysis
2017 Journal of Hydrologic Engineering DOI P.08.04
Rezaeianzadeh, M., Kalin, L., Anderson, C.J.
An Overview of Big Data Applications in Water Resources Engineering
2017 Machine Learning Research DOI P.08.03
Adamala, S.
Runoff Projection under Climate Change Conditions with Data-Mining Methods
2017 Journal of Irrigation and Drainage Engineering DOI P.08.02
Sarzaeim, P., Bozorg-Haddad, O., Bozorgi, A., Loáiciga, H.A.
Monthly Water Consumption Prediction Using Season Algorithm and Wavelet Transform–Based Models
2017 Journal of Water Resources Planning and Management DOI P.08.01
Altunkaynak, A., Nigussie, T.A.
Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models
2016 Journal of Hydrology DOI P.08.06
Bai, Y., Chen, Z., Xie, J., Li, C.
A review on flood modelling and rainfall-runoff relationships
2015 IEEE 6th Control and System Graduate Research Colloquium (ICSGRC) DOI P.08.07
Jaafar, K., Ismail, N., Tajjudin, M., Adnan, R., Rahiman, M.H.F.
A rainfall forecasting method using machine learning models and its application to the Fukuoka city case
2012 International Journal of Applied Mathematics and Computer Science DOI P.08.09
Sumi, S.M., Zaman, M.F., Hirose, H.
Rainfall forecasting by technological machine learning models
2008 Applied Mathematics and Computation DOI P.08.12
Hong, W.-C.
Rainfall-runoff model usingan artificial neural network approach
2004 Mathematical and Computer Modelling DOI P.08.11
Riad, S., Mania, J., Bouchaou, L., Najjar, Y.

Jumlah: 18 Makalah