International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 2, pp. 07-13, February 2026.
This work is licensed under a Creative Commons Attribution 4.0 International License.
A.Surekha
Dommaraju Preethi
P.Nohitha
Bhavanasi Pavan Kumar
Thallam Saritha
Chavali Thirumalesh
Department of CSE, Siddartha Institute of Science and Technology, Puttur, India.
Abstract: Water is the fundamental pillar of survival for all living organisms, yet its safety is increasingly threatened by industrial discharge, mining activities, and environmental pollutants. Adhering to World Health Organization (WHO) guidelines, metrics such as the Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI) are critical for evaluating water suitability. However, traditional methods involving manual sampling, transportation, and laboratory analysis are often inefficient and slow. This study introduces WATERNET, a real-time water quality monitoring network that utilizes LoRa (Long Range) technology to provide continuous oversight. The network architecture was developed with careful consideration of land topology. Performance simulations conducted in Radio Mobile identified a partial mesh network as the most effective topology for ensuring reliable data transmission over varied terrain. To overcome the challenges of limited environmental data, custom datasets were generated to train and validate three machine learning models: Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM). These models were tasked with classifying water as suitable for either drinking or irrigation. Experimental results demonstrated that Logistic Regression achieved the highest accuracy for drinking water classification, whereas the Support Vector Machine proved most effective for irrigation assessment. Furthermore, Recursive Feature Elimination (RFE) was employed to isolate the most significant chemical and physical water parameters, optimizing the models’ predictive performance. WATERNET offers a robust, automated solution for proactive water resource management.
Keywords: Water Quality Index, LoRa Technology, Real-time Monitoring, Irrigation Water Quality Index, Radio Mobile Simulation.
References:
- J. Tian et al., “Assessing groundwater quality for drinking and irrigation using hydrogeochemistry and machine learning in Northern China,” Agricultural Water Management, vol. 322, p. 109975, Nov. 2025, doi: 10.1016/j.agwat.2025.109975.
- N. L. Kushwaha, N. Biwalkar, K. Sur, and J. Rajput, “Integrating water quality indices with machine learning algorithms for groundwater suitability in Punjab: Implications for drinking and sustainable irrigation,” Journal of Water Process Engineering, vol. 76, p. 108253, Jul. 2025, doi: 10.1016/j.jwpe.2025.108253.
- A. Chaiyana, A. Kumari, and S. V. K. Jagadish, “Crop field segmentation and irrigation water source attribution for groundwater monitoring and projection toward conservation in the Texas High Plains,” The Science of the Total Environment, vol. 994, p. 180031, Jul. 2025, doi: 10.1016/j.scitotenv.2025.180031.
- A. Agharabi et al., “Hydro-chemical study and assessment of drinking and irrigation water quality in the Oued Fez watershed (Fez city and surrounding areas, Saïs Basin, Morocco),” Environmental Advances, vol. 20, p. 100639, May 2025, doi: 10.1016/j.envadv.2025.100639.
- S. Kanji, S. Das, and C. Rajak, “A comparative hydrochemical assessment of groundwater quality for drinking and irrigation purposes using different statistical and ML models in lower gangetic alluvial plain, eastern India,” Chemosphere, vol. 372, p. 144074, Jan. 2025, doi: 10.1016/j.chemosphere.2025.144074.
- C. Lai et al., “Spatio-temporal variation of hydrochemistry and irrigation quality of river waters in the largest alluvial plain of southwestern China,” Physics and Chemistry of the Earth Parts a/B/C, vol. 141, p. 104188, Nov. 2025, doi: 10.1016/j.pce.2025.104188.
- H. Sanad, R. Moussadek, A. Zouahri, M. O. Lhaj, L. Mouhir, and H. Dakak, “Machine learning-integrated hydrogeochemical and spatial modeling of groundwater quality indices for seawater intrusion and irrigation sustainability in coastal agroecosystems of Skhirat Region, Morocco,” Journal of Hydrology Regional Studies, vol. 62, p. 102848, Oct. 2025, doi: 10.1016/j.ejrh.2025.102848.
- I. Gómez-Lucena, E. C. Poyato, I. M. García, K. F. Draissi, and J. A. Rodríguez-Díaz, “NITRINET: A predictive model for nitrification in reclaimed water distribution in pressurised irrigation networks,” Agricultural Water Management, vol. 302, p. 108982, Jul. 2024, doi: 10.1016/j.agwat.2024.108982.
- R. Ruby, D. Dimple, and A. Giri, “Suitability of the Himalayan water quality for domestic and irrigation use: A systematic review and meta-analysis,” Chemosphere, vol. 382, p. 144410, May 2025, doi: 10.1016/j.chemosphere.2025.144410.
- A. M. Kipelo, E. K. Atibu, P. Sivalingam, and J. W. Poté, “Faecal indicator bacteria and toxic metal concentrations in domestic and irrigation water from main gardening sites in Kinshasa, Democratic Republic of the Congo,” Environmental Challenges, vol. 20, p. 101240, Jul. 2025, doi: 10.1016/j.envc.2025.101240.
- F. Essahlaoui, N. Elhajrat, M. Halimi, and A. E. Abbassi, “New approach to monitoring a wastewater irrigation system controlled by the artificial neural network (ANN),” Groundwater for Sustainable Development, vol. 23, p. 100999, Aug. 2023, doi: 10.1016/j.gsd.2023.100999.
- V. N. Prapanchan, T. Subramani, D. Karunanidhi, and P. Gopinathan, “Groundwater quality assessment for drinking and irrigation purposes and its human health risks in the Sevathur mine region, south India,” Desalination and Water Treatment, vol. 320, p. 100883, Oct. 2024, doi: 10.1016/j.dwt.2024.100883.
- M. Montiel et al., “Assessment of spatio-temporal variation in microbial quality of groundwater for irrigation and drinking water: A case study in Santa Lucia canton, Ecuador,” Groundwater for Sustainable Development, vol. 23, p. 101033, Nov. 2023, doi: 10.1016/j.gsd.2023.101033.
- A. Sadeghi-Lari, M. Bahrami, and T. Dastandaz, “Temporal and spatial variations of groundwater quantity and quality for drinking and irrigation purposes in the arid and hot weather of Southern Iran,” Physics and Chemistry of the Earth Parts a/B/C, vol. 134, p. 103582, Mar. 2024, doi: 10.1016/j.pce.2024.103582.
- S. A. M. Qureshi and S. M. Ghavami, “AquMADE: A GIS-based web application to assess groundwater quality by introducing a risk-based irrigation water quality index (RB-IWQI),” Environmental Modelling & Software, vol. 176, p. 106009, Mar. 2024, doi: 10.1016/j.envsoft.2024.106009.
- M. R. H. Raj, D. Karunanidhi, P. D. Roy, and T. Subramani, “Predicting groundwater quality for irrigation suitability on agricultural practices using machine learning, fuzzy logic and GIS techniques,” Physics and Chemistry of the Earth Parts a/B/C, vol. 141, p. 104106, Sep. 2025, doi: 10.1016/j.pce.2025.104106.