• Tunnel and Underground Space
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• v.10 no.4
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• pp.506-515
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• 2000

A lot of underground construction projects have been conducted by economical, social and military purposes in Korea for the last three decades. As a result, magnificent amount of data were obtained from geological site investigations, laboratory and field tests, design and field monitoring. But up to now, these valuable informations were neither systematically stored nor utilized efficiently resulting in a great loss of time and money. In this study, a database system named GeoINFO was developed using Microsoft Access 97 for management of informations which can be obtained from underground construction. The developed database system is especially designed to cover three major types of underground facilities-tunnels, underground storages and rock slopes and has multi-layered tree structures for data input. The system also has a unique indexing system for efficient data search using Visual Basic code.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1283-1293
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• 2022

The purpose of this study is to predict residual chlorine in order to maintain stable residual chlorine concentration in sedimentation basin by using artificial intelligence algorithms in water treatment process employing pre-chlorination. Available water quantity and quality data are collected and analyzed statistically to apply into mathematical multiple regression and artificial intelligence models including multi-layer perceptron neural network, random forest, long short term memory (LSTM) algorithms. Water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage data are used as the input parameters to develop prediction models. As results, it is presented that the random forest algorithm shows the most moderate prediction result among four cases, which are long short term memory, multi-layer perceptron, multiple regression including random forest. Especially, it is result that the multiple regression model can not represent the residual chlorine with the input parameters which varies independently with seasonal change, numerical scale and dimension difference between quantity and quality. For this reason, random forest model is more appropriate for predict water qualities than other algorithms, which is classified into decision tree type algorithm. Also, it is expected that real time prediction by artificial intelligence models can play role of the stable operation of residual chlorine in water treatment plant including pre-chlorination process.