Design of a distributed software system architecture for monitoring and forecasting the state of water resources based on machine learning and geospatial analysis

Abstract

The study is aimed at designing the architecture of a distributed software system for continuous monitoring and high-accuracy forecasting of the state of water resources amid rising climate risks and anthropogenic pressure. The relevance is driven by the limited nature of discrete observations and the need for timely analytics over large, spatially distributed data. The goal is to substantiate architectural principles that ensure scalability, fault tolerance, and low latency when integrating heterogeneous sources and predictive computations. A microservice architecture with asynchronous messaging, containerization, and orchestration is proposed, enabling independent scaling of collection, processing, and analytics functions. The system integrates remote sensing data streams and in-situ water quality sensors, as well as hydrometeorological time series. Machine learning includes LSTM networks for time series and ensemble gradient boosting models for classification and regression; geospatial analytics is implemented using GDAL and PostGIS. Validation was performed on a five-year dataset using RMSE and R . Comparative experiments with a monolithic prototype showed a consistent advantage of the microservice approach under load: as request rates grow, the system maintains acceptable response time, more uniform resource utilization, and a low error rate. On water quality forecasting tasks, ensemble methods achieved the lowest error and highest R , outperforming linear models and recurrent networks in the accuracy-to-training-cost trade-off. Data path analysis identified the main sources of latency in preprocessing and inference and confirmed the effectiveness of asynchronous patterns and caching. The results demonstrate that decomposition into independent services and elastic scaling are critical for 24/7 environmental monitoring services. The architecture provides isolation of resource-intensive tasks, heterogeneous hardware profiling, and rapid evolution of algorithms without downtime. The practical significance lies in the transition from reactive control to proactive management, which reduces environmental and economic risks. Limitations are related to infrastructure cost and model cold starts; directions for optimizing inference and data pipelines are outlined. Future work includes deploying accelerated inference on ONNX Runtime, expanding auto-scaling policies for peak loads, strengthening fault tolerance via circuit breaker patterns, and formalizing SLO/SLI for managed operational reliability. Planned efforts include adaptive caching, streaming data cleansing, and continuous A/B testing of models in the production environment.