Toward industrial revolution 4.0: Development, validation, and application of 3D-printed IoT-based water quality monitoring system

Wong, Yong Jie; Nakayama, Rei; Shimizu, Yoshihisa; Kamiya, Akinori; Shen, Shang; Rashid, Idlan Zarizi Muhammad; Sulaiman, Nik Meriam Nik

doi:10.1016/j.jclepro.2021.129230

Cited by 33 publications

(28 citation statements)

References 51 publications

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“…The models in general did not have a considerable increase in R 2 after the addition of 5 predictors (Table 3). In some cases, with even fewer predictors there was no substantial improvement in R 2 , for example with NO 3 -N at both stations after 3 predictors. There are some predictors that repeat themselves in most cases.…”

Section: Model Performance With Varying Subset Of Predictorsmentioning

confidence: 94%

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Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest

Arhab,

Huang

2023

Sensors

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Despite advancements in sensor technology, monitoring nutrients in situ and in real-time is still challenging and expensive. Soft sensors, based on data-driven models, offer an alternative to direct nutrient measurements. However, the high demand for data required for their development poses logistical issues with data handling. To address this, the study aimed to determine the optimal subset of predictors and the sampling frequency for developing nutrient soft sensors using random forest. The study used water quality data at 15-min intervals from 2 automatic stations on the Main River, Germany, and included dissolved oxygen, temperature, conductivity, pH, streamflow, and cyclical time features as predictors. The optimal subset of predictors was identified using forward subset selection, and the models fitted with the optimal predictors produced R2 values above 0.95 for nitrate, orthophosphate, and ammonium for both stations. The study then trained the models on 40 sampling frequencies, ranging from monthly to 15-min intervals. The results showed that as the sampling frequency increased, the model’s performance, measured by RMSE, improved. The optimal balance between sampling frequency and model performance was identified using a knee-point determination algorithm. The optimal sampling frequency for nitrate was 3.6 and 2.8 h for the 2 stations, respectively. For orthophosphate, it was 2.4 and 1.8 h. For ammonium, it was 2.2 h for 1 station. The study highlights the utility of surrogate models for monitoring nutrient levels and demonstrates that nutrient soft sensors can function with fewer predictors at lower frequencies without significantly decreasing performance.

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Section: Model Performance With Varying Subset Of Predictorsmentioning

confidence: 94%

“…The technological advancement in the field of sensors science has allowed for in situ measurements without manual sampling [1,2]. Ion-selective electrodes (ISE), wet-chemical analyzers, and optical sensors are the three categories in which current commercially available sensor technologies for nutrient analysis fall [3].…”

Section: Introductionmentioning

confidence: 99%

Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest

Arhab,

Huang

2023

Sensors

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“…For example, wireless sensors for soil moisture measurement and precision agriculture techniques effectively support the control and reduction of freshwater use in farming activities [24,25]. In addition, extended IoT Sustainability 2022, 14, 66 2 of 4 networks and data platforms allow for transparent water monitoring during manufacturing operations, improving the efficiency of industrial water management [26]. At the same time, artificial intelligence algorithms (e.g., machine learning) and data analytics (e.g., big data) could also foster the transition towards smart water management in agricultural, industrial, and urban environments [27][28][29].…”

Section: Special Issue Backgroundmentioning

confidence: 99%

Water Management in Agriculture and Industry: Challenges, Trends, and Opportunities

Aivazidou

2021

Sustainability

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“…However, smallholder farmer communities (SFCs) have not benefited from adopting solutions to increase irrigation efficiency because of the high starting costs and the high skill levels needed to understand the technology. Recently, the fourth industrial revolution (IR 4.0) paradigm shift has ushered in a surge in technological innovation and sustainable transformation across various industries and sectors [ 4 ]. The innovative use of printed sensors for measurement and monitoring in smart agricultural applications has garnered considerable attention.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Smart and Sustainable Low-Cost Irrigation System for Anomaly Detection Using Deep Learning

Benameur,

Dahane,

Kechar

et al. 2024

Sensors

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The agricultural sector faces several difficulties today in ensuring the safety of food supply, including water scarcity. This study presents the design and development of a low-cost and full-featured fog-IoT/AI system targeted towards smallholder farmer communities (SFCs). However, the smallholder community is hesitant to adopt technology-based solutions. There are many overwhelming reasons for this, but the high cost, implementation complexity, and malfunctioning sensors cause inappropriate decisions. The PRIMA INTEL-IRRIS project aims to make digital and innovative agricultural technologies more appealing and available to these communities by advancing the intelligent irrigation “in-the-box” concept. Considered a vital resource, collected data are used to detect anomalies or abnormal behavior, providing information about an occurrence or a node failure. To prevent agro-field data leakage, this paper presents an innovative, smart, and sustainable low-cost irrigation system that employs artificial intelligence (AI) techniques to analyze anomalies and problems in water usage. The sensor anomaly can be detected using an autoencoder (AE) and a generative adversarial network (GAN). We will feed the autoencoders’ anomaly detection models with time series records from the datasets and replace detected anomalies with the reconstructed outputs. When integrated with an IoT platform, this methodology is a tool for easing the labeling of sensor anomalies and can help create supervised datasets for future research. In addition, anomalies can be corrected by prediction models based on deep learning approaches, applying CNN/BiLSTM architecture. The results show that AEs outperform the GANs, achieving an accuracy of 90%, 95%, and 97% for soil moisture, air temperature, and air humidity, respectively. The proposed system is designed to ensure that the data are of high quality and reliable enough to make sound decisions compared to the existing platforms.

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Toward industrial revolution 4.0: Development, validation, and application of 3D-printed IoT-based water quality monitoring system

Cited by 33 publications

References 51 publications

Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest

Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest

Water Management in Agriculture and Industry: Challenges, Trends, and Opportunities

An Innovative Smart and Sustainable Low-Cost Irrigation System for Anomaly Detection Using Deep Learning

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