TY - GEN
T1 - Empirical Research on 3D Analysis and Flow Prediction of Upstream Rivers Using Drones
AU - Hoshino, Yukinobu
AU - Rathnayake, Namal
AU - Dang, Tuan Linh
AU - Rathnayake, Upaka
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This study aims to develop an AI prediction system for watersheds, utilizing data from Sri Lanka and Vietnam in collaboration with overseas researchers. Initially, a foundational AI prediction system will be constructed, and basic data will be gathered based on river data from Japan. Subsequently, topographic data of the watershed area, managed by the joint research partner institution, will be incorporated into the AI prediction system through drone-based aerial measurement and analysis. Moreover, visits to the joint research institution will be conducted to gather information on water damage and electricity supply. This information will then be integrated into the AI prediction system. Utilizing the collected data and additional observation data, efforts will be made to enhance the accuracy of the AI prediction algorithm, identify potential issues, and overcome them. The ultimate goal is to establish new AI pre-diction technology by comparing the results of this study with both domestic and international research outcomes. This paper will present data from a drone scan of the upstream watershed and discuss the future use of AI based on the results of three-dimensional analysis and image processing.
AB - This study aims to develop an AI prediction system for watersheds, utilizing data from Sri Lanka and Vietnam in collaboration with overseas researchers. Initially, a foundational AI prediction system will be constructed, and basic data will be gathered based on river data from Japan. Subsequently, topographic data of the watershed area, managed by the joint research partner institution, will be incorporated into the AI prediction system through drone-based aerial measurement and analysis. Moreover, visits to the joint research institution will be conducted to gather information on water damage and electricity supply. This information will then be integrated into the AI prediction system. Utilizing the collected data and additional observation data, efforts will be made to enhance the accuracy of the AI prediction algorithm, identify potential issues, and overcome them. The ultimate goal is to establish new AI pre-diction technology by comparing the results of this study with both domestic and international research outcomes. This paper will present data from a drone scan of the upstream watershed and discuss the future use of AI based on the results of three-dimensional analysis and image processing.
KW - AI prediction system
KW - drone measurement
KW - flood control
KW - International joint research
KW - power supply
KW - watershed management
UR - https://www.scopus.com/pages/publications/85214645383
U2 - 10.1109/SCISISIS61014.2024.10760166
DO - 10.1109/SCISISIS61014.2024.10760166
M3 - Conference contribution
AN - SCOPUS:85214645383
T3 - 2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024
BT - 2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024
Y2 - 9 November 2024 through 12 November 2024
ER -