Intelligent Wide-Area Water Quality Monitoring and Analysis System Exploiting Unmanned Surface Vehicles and Ensemble Learning

Cao, Huiru; Zhongwei, Guo; Wang, Shian; Cheng, Haixiu; Zhan, Choujun

doi:10.3390/w12030681

Cited by 42 publications

(25 citation statements)

References 34 publications

(45 reference statements)

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“…The data in this paper is from three sections of the Luan River monitored by the automatic water quality monitoring station in Tangshan city, Hebei Province, China, from 5 July 2018 to 26 March 2019, namely, Daheiting reservoir, Luanxian bridge, and Jianggezhuang. The automatic monitoring station uses the automatic positioning cruise system of an unmanned surface vehicle (USV) to locate the position of the cross-section and uses the data acquisition module to collect experimental data every 4 hours, and then uses the transmission module and cloud service module for data transmission and cloud storage, respectively [26,27]. The alternative names, data volume, and location information of the monitoring sections are listed in Table 1.…”

Section: Study Area and Monitoring Datamentioning

confidence: 99%

Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Yan

Liu

et al. 2021

Water

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The current global water environment has been seriously damaged. The prediction of water quality parameters can provide effective reference materials for future water conditions and water quality improvement. In order to further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new comprehensive deep learning water quality prediction algorithm. Firstly, the water quality data are cleaned and pretreated by isolation forest, the Lagrange interpolation method, sliding window average, and principal component analysis (PCA). Then, one-dimensional residual convolutional neural networks (1-DRCNN) and bi-directional gated recurrent units (BiGRU) are used to extract the potential local features among water quality parameters and integrate information before and after time series. Finally, a full connection layer is used to obtain the final prediction results of total nitrogen (TN), total phosphorus (TP), and potassium permanganate index (COD-Mn). Our prediction experiment was carried out according to the actual water quality data of Daheiting Reservoir, Luanxian Bridge, and Jianggezhuang at the three control sections of the Luan River in Tangshan City, Hebei Province, from 5 July 2018 to 26 March 2019. The minimum mean absolute percentage error (MAPE) of this method was 2.4866, and the coefficient of determination (R2) was able to reach 0.9431. The experimental results showed that the model proposed in this paper has higher prediction accuracy and generalization than the existing LSTM, GRU, and BiGRU models.

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Section: Study Area and Monitoring Datamentioning

confidence: 99%

Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Yan

Liu

et al. 2021

Water

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“…Madeo et al [13] Water quality monitoring pH sensor, ORP sensor, salinity sensor, dissolved oxygen probe -Cao et al [14] Water quality monitoring pH sensor, TDS sensor, turbidity sensor Ensemble learning algorithm…”

Section: Water Quality Monitoringmentioning

confidence: 99%

“…Recently, a number of researchers have developed novel ASV or AUV systems for long-term continuous water quality monitoring and seawater sampling [ 6 , 12 , 13 , 14 , 15 ]. For example, Li et al [ 12 ] implemented a water color remote sensing-oriented USV (WC-USV), which is composed of an unmanned surface vehicle platform with a ground control station, a data acquisition module, and a transmission module, to execute autonomous navigation and obstacle avoidance, water sample collection, water quality measurement, meteorological information measurement, and remote control tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Madeo et al [ 13 ] presented a low-cost USV, named the water environmental mobile observer (WeMo), to provide a modular array of sensors for measuring the pH value, oxidation-reduction parameter (ORP), salinity, and dissolved oxygen. Cao et al [ 14 ] proposed an intelligent wide-area water quality monitoring and online analysis system to analyze the pH, total dissolved solids (TDS), and turbidity values for estimating the water quality level, which comprises an automatic cruise intelligent USV, a water quality monitoring system (WQMS), and a water quality analysis algorithm. Cryer et al [ 15 ] presented an ASV, named the C-Worker 4 (CW4), equipped with the conductivity, temperature, depth (CTD), dissolved O 2 (DO), pH, pCO 2 , nitrate, chlorophyll fluorescence, coloured dissolved organic matter (CDOM), turbidity, and dissolved organic carbon (DOC) sensors, to collect high-resolution measurements in shallow coastal environments.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Water Quality Monitoring and Water Surface Cleaning for Unmanned Surface Vehicle

Chang

Hsu

Hung

et al. 2021

Sensors

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Water is one of the most precious resources. However, industrial development has made water pollution a critical problem today and thus water quality monitoring and surface cleaning are essential for water resource protection. In this study, we have used the sensor fusion technology as a basis to develop a multi-function unmanned surface vehicle (MF-USV) for obstacle avoidance, water-quality monitoring, and water surface cleaning. The MF-USV comprises a USV control unit, a locomotion module, a positioning module, an obstacle avoidance module, a water quality monitoring system, a water surface cleaning system, a communication module, a power module, and a remote human–machine interface. We equip the MF-USV with the following functions: (1) autonomous obstacle detection, avoidance, and navigation positioning, (2) water quality monitoring, sampling, and positioning, (3) water surface detection and cleaning, and (4) remote navigation control and real-time information display. The experimental results verified that when the floating garbage located in the visual angle ranged from −30° to 30° on the front of the MF-USV and the distances between the floating garbage and the MF-USV were 40 and 70 cm, the success rates of floating garbage detection are all 100%. When the distance between the floating garbage and the MF-USV was 130 cm and the floating garbage was located on the left side (15°~30°), left front side (0°~15°), front side (0°), right front side (0°~15°), and the right side (15°~30°), the success rates of the floating garbage collection were 70%, 92%, 95%, 95%, and 75%, respectively. Finally, the experimental results also verified that the applications of the MF-USV and relevant algorithms to obstacle avoidance, water quality monitoring, and water surface cleaning were effective.

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“…Besides the WSN, there is some off-line or point-to-point data acquisition performed for WARM applications. Many mobile sensor nodes are implemented by being mounted on autonomous vehicles such as floating boats [35], amphibious vehicles [36], UAVs [37], and underwater vehicles [38]. All of these require a considerable amount of electric energy for their mobility.…”

Section: Introductionmentioning

confidence: 99%

LDAP: Lightweight Dynamic Auto-Reconfigurable Protocol in an IoT-Enabled WSN for Wide-Area Remote Monitoring

Rahman

Wahid

2020

Remote Sensing

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IoT (Internet of Things)-based remote monitoring and controlling applications are increasing in dimensions and domains day by day. Sensor-based remote monitoring using a Wireless Sensor Network (WSN) becomes challenging for applications when both temporal and spatial data from widely spread sources are acquired in real time. In applications such as environmental, agricultural, and water quality monitoring, the data sources are geographically distributed, and have little or no cellular connectivity. These applications require long-distance wireless or satellite connections for IoT connectivity. Present WSNs are better suited for densely populated applications and require a large number of sensor nodes and base stations for wider coverage but at the cost of added complexity in routing and network organization. As a result, real time data acquisition using an IoT connected WSN is a challenge in terms of coverage, network lifetime, and wireless connectivity. This paper proposes a lightweight, dynamic, and auto-reconfigurable communication protocol (LDAP) for Wide-Area Remote Monitoring (WARM) applications. It has a mobile data sink for wider WSN coverage, and auto-reconfiguration capability to cope with the dynamic network topology required for device mobility. The WSN coverage and lifetime are further improved by using a Long-Range (LoRa) wireless interface. We evaluated the performance of the proposed LDAP in the field in terms of the data delivery rate, Received Signal Strength (RSS), and Signal to Noise Ratio (SNR). All experiments were conducted in a field trial for a water quality monitoring application as a case study. We have used both static and mobile data sinks with static sensor nodes in an IoT-connected environment. The experimental results show a significant reduction (up to 80%) of the number of data sinks while using the proposed LDAP. We also evaluated the energy consumption to determine the lifetime of the WSN using the LDAP algorithm.

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Intelligent Wide-Area Water Quality Monitoring and Analysis System Exploiting Unmanned Surface Vehicles and Ensemble Learning

Cited by 42 publications

References 34 publications

Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Autonomous Water Quality Monitoring and Water Surface Cleaning for Unmanned Surface Vehicle

LDAP: Lightweight Dynamic Auto-Reconfigurable Protocol in an IoT-Enabled WSN for Wide-Area Remote Monitoring

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