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Open research questions in Water Quality Monitoring Technologies

119 gap statements mined from Water Quality Monitoring Technologies papers in our 4.5M-paper local library, which holds 888 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

Choosing where to publish on Water Quality Monitoring Technologies? See the ranked Earth Science journals.

What the literature leaves open

  • The lack of real-time, cloud-based drainage monitoring systems with reliable alert mechanisms. The limited integration of IoT monitoring with cloud analytics and remote access. The insufficient focus on durability and protection of drainage components.

    IOT Controlled Drainage Monitoring System with Cloud · 2026 · DOI

  • Future research will focus on incorporating multimodal data fusion using water quality parameters, real-time video-based disease monitoring, edge-based deployment strategies, and explainable AI techniques to enhance interpretability and trust.

    A hybrid deep learning and machine learning framework for automated freshwater fish disease diagnosis towards sustainable aquaculture · 2026 · DOI

  • Conventional floating waste removal methods have limitations such as high labor intensity and insufficient adaptability to small- and medium-sized water bodies.

    Design of an Intelligent Water Surface Cleaning System for Small- and Medium-Sized Water Bodies · 2026 · DOI

  • The paper lacks detailed discussion of limitations in waste recognition accuracy, obstacle detection range, or water quality parameter measurement precision.

    Design of an Intelligent Water Surface Cleaning System for Small- and Medium-Sized Water Bodies · 2026 · DOI

  • Traditional fish freshness assessment methods are subjective and time-intensive. Automated freshness detection systems are needed for real-time applications.

    Deep Learning Based Fish Species and Freshness Detection Using Convolutional Neural Networks · 2026 · DOI

  • Conventional cleaning methods are mostly manual, inefficient, and time-consuming. The literature survey indicates that although significant progress has been made in developing water garbage collection systems, several challenges still remain.

    Smart Water Boat Garbage Collector · 2026 · DOI

  • Traditional water monitoring and dispensing methods lack real-time monitoring capabilities and fail to provide immediate responses when water quality deviates from safe standards. There is a need for an efficient, automated, and cost-effective solution for maintaining water quality.

    Smart Sensor-Driven Water Monitoring and Controlled Dispensing Framework · 2026 · DOI

  • The lack of real-time water quality monitoring systems in small and medium-scale wastewater treatment plants. The need for scalable and cost-effective solutions for environmental monitoring.

    Prediction of chemical oxygen demand in industry effluents using machine learning and IoT: a case study in Tequila, Jalisco · 2026 · DOI

  • Low-cost sensors have limitations such as sensor drift, reduced accuracy, and sensitivity to environmental conditions. Many existing systems lack a balance between cost, accuracy, reliability, and field deployability.

    Design and Prototyping of a Low-Cost, Automated Water Quality Analysis and Reporting System for Rural and Urban Water Sources · 2026 · DOI

  • Conventional laboratory-based methods are costly, time-consuming, and unable to capture real-time variations in water quality. Many existing systems lack a balance between cost, accuracy, reliability, and field deployability.

    Design and Prototyping of a Low-Cost, Automated Water Quality Analysis and Reporting System for Rural and Urban Water Sources · 2026 · DOI

  • Future advancements could include AI-based garbage recognition, camera-based monitoring, and cloud-based data logging for predictive maintenance of urban drainage systems.

    Automatic Drainage Cleaning Robot · 2026 · DOI

  • The lack of automated drainage cleaning systems poses a significant risk to human health, safety, and the environment. Traditional drainage cleaning methods are hazardous and inefficient.

    Automatic Drainage Cleaning Robot · 2026 · DOI

  • Tracks affected by severe occlusions, unfavorable poses, incomplete point clouds, imageboundary intersections, or insufficient temporal evidence were discarded rather than used to generate potentially unreliable biometric estimates.

    Autonomous underwater stereo vision system for non-invasive fish length estimation in marine environments · 2026 · DOI

  • Manual feeding practices have limitations, such as irregular schedules and inefficient feed utilization. There is a need for an efficient, reliable, and scalable solution for modern aquaculture management.

    Smart IoT-Based Feeding System with Solar Panel Integration as an Efficient Solution for Freshwater Fish Farming · 2026 · DOI

  • Conventional metering has limitations, such as the use of a single meter for an entire building. There is a need for an IoT-based intelligent liquid metering and control system.

    An IoT-based intelligent liquid metering and control system · 2026 · DOI

  • Traditional water quality monitoring methods have limitations such as manual sampling and sensor degradation. Most systems lack a dedicated mechanism for automatic sensor cleaning.

    Smart Water Quality Monitoring System · 2026 · DOI

  • The lack of automated, eco-friendly solutions for removing floating debris from pond surfaces. The need for a system that can operate within an efficient power budget and provide real-time water quality monitoring.

    Solar Based Pond Water Cleaner - An Automated Solar-Powered Surface Cleaning System with IoT Monitoring · 2026 · DOI

  • Future research should focus on enhancing robust perception, adaptive stabilization, and human-computer interaction security. Future research should investigate the use of artificial intelligence and machine learning in intelligent rescue technologies. Future research should evaluate the practical application effect of intelligent rescue technologies.

    A review of Rescue Methods After Intelligent Rescue Equipment Approaches a Drowning Person · 2026 · DOI

  • There is a need for more research on intelligent rescue technologies for drowning rescue. There is a lack of standardization in the design and development of intelligent rescue devices.

    A review of Rescue Methods After Intelligent Rescue Equipment Approaches a Drowning Person · 2026 · DOI

  • The system needs to be robust and reliable for real-time monitoring of water quality parameters. The system needs to be able to detect pollutants in marine environments accurately. The system needs to be able to operate in harsh environments.

    DESIGN AND IMPLEMENTATION OF MACHINE LEARNING BASED DETECTION OF POLLUTANTS IN MARINE ENVIRONMENT · 2026 · DOI

  • Expanding the dataset with more fish species and images captured under different conditions. Integrating IoT sensors to monitor storage conditions. Developing a mobile or embedded application for real-time freshness detection.

    Deep Learning Based Fish Species and Freshness Detection Using Convolutional Neural Networks · 2026 · DOI

  • Add more sensors for complete water quality analysis. Develop a dedicated mobile app for real-time readings and instant alerts. Make the device energy-efficient using solar panels for operation in remote areas.

    Smart Water Quality Monitoring System · 2026 · DOI

  • Further testing and evaluation of the system in real-world scenarios. Development of more advanced sensors and algorithms for water quality monitoring and obstacle detection. Integration of the system with other environmental management technologies.

    Solar Based Pond Water Cleaner - An Automated Solar-Powered Surface Cleaning System with IoT Monitoring · 2026 · DOI

  • Long-term component lifespan and degradation behavior were not evaluated, - The study did not assess the system's performance under varying climatic conditions, - The system's energy storage capacity is limited to 50-100 Ah, - The system's production capacity is limited to 1000-1500…

    Integrated Solar-Powered Clean Water Treatment System for Smart Building: A Case Study on Sustainable Technology and Building Deployment in the Remote Region · 2026 · DOI

  • Evaluating the system's long-term performance and component lifespan, - Assessing the system's performance under varying climatic conditions, - Investigating the potential for scaling up the system's production capacity, - Exploring alternative energy storage solutions to improve the system's energy…

    Integrated Solar-Powered Clean Water Treatment System for Smart Building: A Case Study on Sustainable Technology and Building Deployment in the Remote Region · 2026 · DOI

Most-cited papers in Water Quality Monitoring Technologies

Most recent work

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Open questions raised by papers listed on this page include: (1) The lack of real-time, cloud-based drainage monitoring systems with reliable alert mechanisms (“IOT Controlled Drainage Monitoring System with Cloud”, 2026); (2) Automated freshness detection systems are needed for real-time applications (“Deep Learning Based Fish Species and Freshness Detection Using Convolutional…”, 2026); (3) There is a need for an efficient, automated, and cost-effective solution for maintaining water quality (“Smart Sensor-Driven Water Monitoring and Controlled Dispensing Framework”, 2026); (4) The lack of real-time water quality monitoring systems in small and medium-scale wastewater treatment plants (“Prediction of chemical oxygen demand in industry effluents using machine…”, 2026); (5) The lack of automated drainage cleaning systems poses a significant risk to human health, safety, and the environment (“Automatic Drainage Cleaning Robot”, 2026). 119 gap statements have been mined from Water Quality Monitoring Technologies papers in our 4.5M-paper local library, which holds 888 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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