Real-Time Monitoring of Small Reservoir Hydrology Using ICT and Application of Deep Learning for Prediction of Water Level

2021 ◽  
pp. 139-157
Author(s):  
Tsugumu Kusudo ◽  
Daisuke Hayashi ◽  
Daiki Matsuura ◽  
Atsushi Yamamoto ◽  
Masaomi Kimura ◽  
...  
Keyword(s):  
Deep Learning ◽  
Water Level ◽  
Real Time ◽  
Real Time Monitoring ◽  
Small Reservoir

scholarly journals Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1547
Author(s):  
Jian Sha ◽  
Xue Li ◽  
Man Zhang ◽  
Zhong-Liang Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Best Management Practices ◽  
Input Data ◽  
Management Practices ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

scholarly journals Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3930 ◽  
Author(s):  
Ayaz Hussain ◽  
Umar Draz ◽  
Tariq Ali ◽  
Saman Tariq ◽  
Muhammad Irfan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Air Pollutants ◽  
Garbage Collection ◽  
Air Pollutant ◽  
Real Time Monitoring ◽  
Collection System ◽  
The Real ◽  
The Status

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand

2022 ◽  
Vol 308 ◽  
pp. 118336
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Kaiser Calautit ◽  
Jo Darkwa ◽  
Christopher Wood
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Real Time Monitoring ◽  
Window Opening ◽  
Reducing Energy

Design of a Dynamic Data Acquisition and Internet Network Based Transmission System for Reservoir Water Level

2014 ◽  
Vol 678 ◽  
pp. 207-211
Author(s):  
Dong Bing Dai ◽  
Ya Jun Wang ◽  
Ya Ping Jiang ◽  
Zheng Guang Liu
Keyword(s):  
Life Cycle ◽  
Water Level ◽  
Real Time ◽  
Mechanical Transmission ◽  
Real Time Monitoring ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Level Measurement ◽  
High Possibility ◽  
Internet Network

The traditional reservoir water level monitoring system utilizes the sensors placed in the water has drawbacks of: 1) high possibility of water seepage; 2) short life cycle; 3) difficulty to achieve real-time monitoring; and 4) not easy to maintain. This paper presents a design of water level measurement sensor which relies on mechanical transmission tracking method. Since there is no need to place the sensor into the water, the life cycle is improved. Furthermore, by remote data transmission over the internet network, A real-time monitoring of the water level can be achieved, and thus significantly improve the overall management efficiency of reservoir resource utilization.

Real-time monitoring of high-power disk laser welding statuses based on deep learning framework

2019 ◽  
Vol 31 (4) ◽  
pp. 799-814 ◽  
Author(s):  
Yanxi Zhang ◽  
Deyong You ◽  
Xiangdong Gao ◽  
Congyi Wang ◽  
Yangjin Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Laser Welding ◽  
Real Time ◽  
High Power ◽  
Real Time Monitoring ◽  
Disk Laser ◽  
Learning Framework
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