Using Physically Estimated Temperatures as Inputs to Enhance the Performance of an LSTM Model for Predicting Water Temperatures in Fishponds. – Think Lab

Journal Papers

December 1, 2025

文章發表於 Smart Agricultural Technology, 101563. (N/M=4/126=3.17%, AGRONOMY, 2023)

Using Physically Estimated Temperatures as Inputs to Enhance the Performance of an LSTM Model for Predicting Water Temperatures in Fishponds.

Yang, T. H.*, & Lin, J. C. (2025, December).

Development of an edge computing-based sensory network to mitigate the impact of natural disasters in pond aquaculture
August 2, 2024・2023 GSsympo

A Novel Multimodal Large Language Model-Based Approach for Urban Flood Detection Using Open-Access Closed Circuit Television in Bandung, Indonesia
November 8, 2025・Water, 17(18), 2739. (N/M=40/128=31.25%, WATER RESOURCES, 2023)

Using Physically Estimated Temperature as Inputs to Enhance the erformance of the LSTM Model for Predicting Water Temperature in Fishponds
November 8, 2025・Smart Agricultural Technology

Combination of Large Language Models and Portable Flood Sensors for Community Flood Response: A Preliminary Study
April 1, 2025・Water 2025, 17(7), 1055. (N/M=40/128=31.25%, WATER RESOURCES, 2023)

初探貝葉斯定理評估臺灣工業區洪水損失的可行性
March 1, 2025・台灣水利，第七十三卷第一期。(EI)

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