Tropical Cyclone Track Prediction in the TCWC Indonesia Monitoring Area Using Deep Recurrent Neural Networks Article Swipe

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Fakhrul Alam , Gede Putra Kusuma , Ida Pramuwardani ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48084/etasr.12531

Tropical Cyclones (TCs) are rapidly rotating large-scale storm systems and rank as the second most destructive natural hazards after earthquakes. Disaster mitigation in TC-prone regions is critical, particularly in view of the two-fold increase in population in these areas over the past five decades. This research focuses on the Area of Monitoring (AoM) in Indonesia for TC, which spans from 20°N to 20°S latitude and from 90°E to 141°E longitude. The dataset is sourced from the International Best Track Archive for Climate Stewardship (IBTrACS), filtered to include only TCs that occur in this AoM region. The preprocessing involved using a sliding window with a sequence length of three to generate input features. Four Recurrent Neural Network (RNN) models were evaluated: Long Short-Term Memory (LSTM), bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). All models were trained using incremental learning, comprising 158 iterations for the northern AoM region and 69 iterations for the southern AoM region. The evaluation results indicate that the LSTM model has high performance, with an average Mean Absolute Error (MAE) of 0.24485 degrees in the northern region and 0.22330 degrees in the southern region.

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Recurrent Neural Network

Environmental Science

Cartography

Remote Sensing

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Recurrent neural network Track (disk drive) Tropical cyclone Sliding window protocol Storm Artificial neural network Meteorology Population Preprocessor Computer science Geography Climatology Deep learning Sequence (biology) Flash flood Trajectory Mean squared error Natural disaster Latitude Environmental science Cartography Remote sensing Warning system Data pre-processing Rank (graph theory)

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Landing Page: https://doi.org/10.48084/etasr.12531
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DOI: https://doi.org/10.48084/etasr.12531

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Title: Tropical Cyclone Track Prediction in the TCWC Indonesia Monitoring Area Using Deep Recurrent Neural Networks

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Publication year: 2025

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Publication date: 2025-12-08

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Authors: Fakhrul Alam, Gede Putra Kusuma, Ida Pramuwardani

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Concepts: Recurrent neural network, Track (disk drive), Tropical cyclone, Sliding window protocol, Storm, Artificial neural network, Meteorology, Population, Preprocessor, Computer science, Geography, Climatology, Deep learning, Sequence (biology), Flash flood, Trajectory, Mean squared error, Natural disaster, Latitude, Environmental science, Cartography, Remote sensing, Warning system, Data pre-processing, Rank (graph theory)

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abstract_inverted_index.model	165
abstract_inverted_index.occur	90
abstract_inverted_index.spans	58
abstract_inverted_index.storm	7
abstract_inverted_index.these	37
abstract_inverted_index.three	107
abstract_inverted_index.using	98, 139
abstract_inverted_index.which	57
abstract_inverted_index.(GRU),	130
abstract_inverted_index.141°E	68
abstract_inverted_index.Memory	122
abstract_inverted_index.Neural	114
abstract_inverted_index.length	105
abstract_inverted_index.models	117, 136
abstract_inverted_index.region	149, 182
abstract_inverted_index.second	13
abstract_inverted_index.window	101
abstract_inverted_index.(LSTM),	123
abstract_inverted_index.0.22330	184
abstract_inverted_index.0.24485	177
abstract_inverted_index.Archive	79
abstract_inverted_index.Climate	81
abstract_inverted_index.Network	115
abstract_inverted_index.average	171
abstract_inverted_index.dataset	71
abstract_inverted_index.degrees	178, 185
abstract_inverted_index.focuses	46
abstract_inverted_index.hazards	17
abstract_inverted_index.include	86
abstract_inverted_index.natural	16
abstract_inverted_index.rapidly	4
abstract_inverted_index.region.	94, 157, 189
abstract_inverted_index.regions	24
abstract_inverted_index.results	160
abstract_inverted_index.sliding	100
abstract_inverted_index.sourced	73
abstract_inverted_index.systems	8
abstract_inverted_index.trained	138
abstract_inverted_index.(BiGRU).	134
abstract_inverted_index.Absolute	173
abstract_inverted_index.Cyclones	1
abstract_inverted_index.Disaster	20
abstract_inverted_index.TC-prone	23
abstract_inverted_index.Tropical	0
abstract_inverted_index.decades.	43
abstract_inverted_index.filtered	84
abstract_inverted_index.generate	109
abstract_inverted_index.increase	33
abstract_inverted_index.indicate	161
abstract_inverted_index.involved	97
abstract_inverted_index.latitude	63
abstract_inverted_index.northern	147, 181
abstract_inverted_index.research	45
abstract_inverted_index.rotating	5
abstract_inverted_index.sequence	104
abstract_inverted_index.southern	155, 188
abstract_inverted_index.two-fold	32
abstract_inverted_index.(BiLSTM),	126
abstract_inverted_index.Indonesia	54
abstract_inverted_index.Recurrent	113, 128
abstract_inverted_index.critical,	26
abstract_inverted_index.features.	111
abstract_inverted_index.learning,	141
abstract_inverted_index.(IBTrACS),	83
abstract_inverted_index.Monitoring	51
abstract_inverted_index.Short-Term	121
abstract_inverted_index.comprising	142
abstract_inverted_index.evaluated:	119
abstract_inverted_index.evaluation	159
abstract_inverted_index.iterations	144, 152
abstract_inverted_index.longitude.	69
abstract_inverted_index.mitigation	21
abstract_inverted_index.population	35
abstract_inverted_index.Stewardship	82
abstract_inverted_index.destructive	15
abstract_inverted_index.incremental	140
abstract_inverted_index.large-scale	6
abstract_inverted_index.earthquakes.	19
abstract_inverted_index.particularly	27
abstract_inverted_index.performance,	168
abstract_inverted_index.Bidirectional	132
abstract_inverted_index.International	76
abstract_inverted_index.bidirectional	124
abstract_inverted_index.preprocessing	96
cited_by_percentile_year
countries_distinct_count	1
institutions_distinct_count	3
citation_normalized_percentile.value	0.7759822
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False