Aug 2026· International Journal of Technology and Emerging Research· Vol 2, pp. 118-126· 0 citations· 8 references
TL;DR
An end-to-end Internet of Things framework designed for real-time water quality monitoring and predictive pollution modeling and a hybrid machine learning architecture—combining Long Short-Term Memory (LSTM) networks for time-series forecasting and Random Forest models for anomaly classification—is proposed.
Abstract
Water pollution poses a severe threat to global public health, aquatic biodiversity, and sustainable resource management. Traditional monitoring methods rely on manual sample collection and laboratory analysis, which are labor-intensive, time-consuming, and fail to provide early warning capabilities. This paper proposes an end-to-end Internet of Things (IoT) framework designed for real-time water quality monitoring and predictive pollution modeling. The framework integrates a network of low-power, multisensor edge nodes deployed across aquatic bodies to measure key parameters including pH, turbidity, dissolved oxygen (DO), total dissolved solids (TDS), and temperature. Data collected from the sensor layer is transmitted via low-power wide-area network protocols (LoRaWAN/MQTT) to a centralized cloud analytics platform. To enable proactive environmental management, a hybrid machine learning architecture—combining Long Short-Term Memory (LSTM) networks for time-series forecasting and Random Forest models for anomaly classification—is proposed to predict spatial-temporal pollution trends and identify illegal dumping events before severe contamination occurs. This paper proposes a conceptual architecture intended to guide future implementation and validation
Keywords: machine learning; edge computing; Internet of Things (IoT); Water Quality Monitoring; Time-Series Prediction; Environmental Sensing; LoRaWAN
The growing threats to river water quality demand innovative approaches for effective monitoring and protection. This paper proposes an Internet of Things (IoT)-based river water quality monitoring system to address this challenge. The proposed system utilizes a network of sensors strategically deployed within the river, measuring crucial parameters like temperature, pH, dissolved oxygen, turbidity, and conductivity. Sensor data is continuously transmitted wirelessly to a central hub for processing and analysis. Utilizing cloud computing platforms, the system enables real-time data visualization and analysis, allowing for prompt identification of potential pollution events. Additionally, the system integrates alerting mechanisms to notify relevant authorities, facilitating timely interventions. This paper presents the design, implementation, and field testing of the proposed system, along with a thorough evaluation of its performance. The results demonstrate the system's effectiveness in capturing comprehensive water quality data, facilitating real-time monitoring, and enabling proactive water management strategies.
E. Amrutha, Dinesh Ram S P, Ranil Vikram P· International Journal of Lat...· 0 citations
Forest fires are among the most destructive natural hazards, posing significant threats to ecosystems, infrastructure, and human life. In Mediterranean regions such as Algeria, the frequency and intensity of wildfires have increased due to climate change and human activities. This article proposes a cloud-centric hybrid Internet of Things (IoT) and machine learning (ML) framework for intelligent forest fire monitoring and prevention. The proposed system integrates distributed IoT sensor nodes equipped with temperature and humidity sensors that continuously collect environmental data and transmit them to a central gateway through NRF24L01 communication modules, while long-range communication with the cloud platform is achieved using a SIM808 cellular module. To enhance predictive capabilities, the framework combines real-time IoT sensing data with complementary meteorological variables, including wind speed and rainfall, obtained from external meteorological services during dataset construction. Six supervised ML models—logistic regression, decision tree, random forest, XGBoost, LightGBM, and CatBoost—were evaluated using historical Algerian wildfire data (2000–2003) together with a recent dataset collected in 2024. Experimental results show that XGBoost achieved the highest overall predictive performance with a test accuracy of 98.75% and an F1-score of 98.97%, while Random Forest and CatBoost also demonstrated robust and stable performance. Logistic Regression achieved competitive results with significantly lower computational cost, making it suitable for resource-constrained IoT environments. The proposed hybrid IoT–ML framework enables early wildfire risk assessment and supports proactive decision-making for forest management. These findings demonstrate the potential of integrating IoT sensing, meteorological information, and machine learning to support sustainable environmental monitoring within ambient intelligence systems.
Ali Kourtiche, Souad Belhia, Mahmoud Fahsi et al.· Journal of Ambient Intellige...· 0 citations
Rising industrialization and urbanization have significantly impacted water quality, making effective monitoring essential. Traditional monitoring systems are often expensive, time-consuming, and lack real-time capabilities, especially in developing regions. This paper proposes a low-cost, IoT-based water quality monitoring system that continuously tracks key parameters such as pH, turbidity, temperature, and dissolved oxygen. The system uses affordable sensors, microcontrollers, and wireless communication to enable real-time data collection and transmission. Its modular and scalable design allows deployment even in rural and resource-limited areas. Data collected from multiple sensing nodes is sent to a cloud platform for storage and analysis. Continuous monitoring helps in early detection of contamination, while data analytics supports anomaly detection and improved decision-making. The study reviews existing systems and highlights their limitations, including high cost and lack of scalability. Experimental results show that the proposed system provides reasonably accurate measurements compared to standard laboratory equipment. Overall, the system offers a cost-effective and efficient solution for real-time water quality monitoring, with future scope for integrating machine learning and large-scale smart city applications.
G. Kézdi, Jurgen Willman· International Journal of Mod...· 0 citations
Modern agriculture faces severe challenges from climate change, resource depletion, and growing global food demand. Traditional farming practices, which rely on manual monitoring and blanket resource applications, often result in sub-optimal crop yields, excessive water usage, and environmental degradation. To address these limitations, this paper proposes an integrated framework combining Internet of Things (IoT) architecture with Deep Learning (DL) models for real-time monitoring, predictive analytics, and automated, sustainable agricultural management.
A wireless sensor network (WSN) deploying multi-modal IoT devices—measuring soil moisture, temperature, humidity, pH, and light intensity—continuously streams microclimate data to a cloud platform. Concurrently, field cameras and unmanned aerial vehicles (UAVs) capture high-resolution images. Convolutional Neural Networks (CNNs) process these visual inputs for early disease detection and pest identification, while Long Short-Term Memory (LSTM) networks analyze time-series sensor streams to forecast soil moisture levels and environmental trends.
Experimental evaluations demonstrate that the proposed IoT-DL framework achieves over 95% accuracy in crop health classification and yield forecasting. By leveraging predictive insights, the system’s automated decision support optimizes irrigation schedules and nutrient delivery, reducing water and fertilizer consumption by up to 30%. Ultimately, this end-to-end framework bridges physical sensing and artificial intelligence, offering a scalable, resource-efficient solution for precision farming and long-term food security.
K. J. Prakash, M.Rathamani· RCHUB JOURNAL OF COMPUTATION...· 0 citations
Smart water distribution systems play a vital role in addressing modern water management challenges caused by urbanization, population growth, industrial expansion, and climate change. Traditional water supply networks often suffer from high water losses, inefficient monitoring, delayed fault detection, and increased operational costs due to manual management practices. To overcome these limitations, smart water management integrates real-time sensor networks, Internet of Things (IoT) technologies, wireless communication, cloud computing, and data analytics. Advanced sensors continuously monitor critical parameters such as water pressure, flow rate, water quality, leakage, temperature, pH, and contamination levels across pipelines, reservoirs, treatment plants, and consumer endpoints. This study examines the architecture, communication mechanisms, sensing technologies, and optimization techniques used in smart water distribution systems. The proposed framework employs layered deployment of pressure, flow, and water-quality sensors combined with cloud-based analytics and predictive control algorithms. Wireless communication technologies such as ZigBee, LoRaWAN, GSM, and Wi-Fi enable efficient data transmission across distributed infrastructure. The system supports real-time leakage detection, pressure regulation, contamination monitoring, predictive maintenance, and energy-efficient pump scheduling. Simulation results demonstrate significant improvements in leakage detection accuracy, operational efficiency, water conservation, energy savings, and infrastructure reliability compared to conventional distribution networks. The findings indicate that smart water distribution systems powered by real-time sensor networks can transform traditional water utilities into intelligent, adaptive, and sustainable platforms, supporting resilient water management and future smart city development.
James Carter, Patricia Hall· International Journal of Mod...· 0 citations
Addressing the growing problem of urban air pollution requires that sophisticated air pollution monitoring and forecasting systems be incorporated into civil engineering practice. New technologies that support smarter and more sustainable urban environments include the Internet of Things (IoT), big data analytics, and machine learning (ML). These technologies allow for real-time data acquisition, high-resolution monitoring, and precise predictive optimization. Continuous, spatially dense air quality monitoring is made possible by IoT-based sensor networks, while big data frameworks can effectively integrate and analyse diverse, multi-source datasets. Meanwhile, ML and deep learning can further improve forecasting accuracy, enabling urban planners and civil engineers to foresee pollution trends and implement proactive mitigation measures. Notwithstanding these developments, challenges persist for data integration, sensor calibration, model transparency, and the reliability of inexpensive monitoring systems. To overcome these constraints, improvements are needed in terms of data accuracy, robust calibration techniques, and the implementation of interpretable ML models. This review emphasizes the vital role of civil engineers in promoting resilient and sustainable urban development, advancing effective air quality management, and safeguarding public health through interdisciplinary collaboration between data scientists and policymakers.
Prakash Pathak, Pradip Jadhao· Ecology, Economy and Society...· 0 citations
