Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station

Pradeep Hewage, Ardhendu Behera, Marcello Trovati, Ella Pereira, Morteza Ghahremani, Francesco Palmieri, Yonghuai Liu

Soft Computing 2020 article cited by 728

Source Open-access PDF More in Climate & Environment

importance 4/10 ai assisted Hydrological Forecasting Using AI agricultural weather forecasting stage: research

Problem

Farmers need detailed short-to-medium-range, high-resolution local forecasts, but major numerical weather prediction models only provide 6-hourly forecasts at 10-20 km grid length.

Why it matters

Non-predictive or inaccurate weather forecasts can severely impact farming communities.

What the AI did

Developed a lightweight forecasting system combining local weather stations with temporal convolutional networks (TCN) and LSTM networks trained on time-series data.

Results

not stated in source

Impact

Peer-reviewed journal paper with 705 citations indicates broad scholarly recognition of the method.

Record

Venue	Soft Computing
Published	2020
Work type	article
Authors	Pradeep Hewage, Ardhendu Behera, Marcello Trovati, Ella Pereira, Morteza Ghahremani, Francesco Palmieri, Yonghuai Liu
Citations	728 (OpenAlex, at import time)
References	56
Model or method	Temporal convolutional network (TCN); LSTM
Organization	not stated in source
Primary source	https://doi.org/10.1007/s00500-020-04954-0
Open access	hybrid
Keywords	weather forecasting, TCN, LSTM, time-series, local weather stations

Importance rating

4 / 10

real but narrower advance

Abstract says inaccurate forecasts severely impact users such as farmers, but gives no scale; a lightweight local forecasting tool is a real but narrower advance.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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