Surface water is an important source of water supply for irrigation purpose and in urban areas, sewage water is being disposed of in nearby canals without treatment. A study was conducted to investigate the dynamics of water quality of irrigation canal as a result of this practice. The study ascertained the impact of different salinity parameters, indices and approaches to examine the hazardous effects on quality of canal water. The study analyses the samples collected for various parameters like pH, TDS, EC, Na, Cl, Ca, Mg, K, CO₃, HCO₃ etc. It helped to decide the restriction on use of water based on FAO-UN guidelines. Investigations were focused on assessment of contaminants affecting the quality of water and having hazardous effects on different stages of irrigation water usage. Wilcox diagram and Doneen’s approach-based analysis helped to identify the class and quality of water. This study shall help to analyze the quality of water and provide support to the decision makers for better water resource management and policy development for irrigation purpose i.e., treatment and distribution of water resource.

This research dealt with the impact of the quality of the water source on the mechanical properties of construction materials. The mechanical properties of construction materials include compressive, tensile, and flexural strength. Water samples were collected from different resources, these samples were then synthetically investigated to identify and compare their quality parameters. After a detailed chemical analysis of water samples from three sources—wastewater, surface or canal water, and ground water—construction concrete material samples were prepared. The construction materials were developed with the same water–cement ratio, i.e., 0.60 for each concrete mix sample at two mix ratios—M1 (1:2:4) and M2 (1:1.5:3). Slump cone and compacting factor tests were conducted on the fresh concrete to determine its workability prior to its hardening. Then, at 7, 14, 21, and 28 days for each mix, tests for mechanical properties were carried out to determine the compressive, tensile, and flexure strengths. Results showed that the mechanical properties of the concrete made by utilizing wastewater and surface water were more noteworthy as compared to the concrete made by groundwater. This study will help in the production of concrete which depends on waste and surface canal water, even for large projects like rigid pavement construction and water-related structures.

Sustainable engineering practices always provide an opportunity for engineers to produce long-term solutions. In the fields of construction and irrigation, soil bed formation symmetry is very important, especially in the case of its behavior with reference to water runoff, whether natural or artificial. In this study, different soil bed formations were tested with the help of advanced hydrology apparatus under severe rainfall conditions. A major focus was to observe the water retention and volume discharge with reference to soil combinations and slope level change. Furthermore, an advanced decision-making technique incorporating artificial neural networks (ANNs) was used to predict and explore the interrelationship behavior of different parameters. It was observed that ST-1 (100% clay) performed well as it tried to retain a large quantity of water (7.28 L/min), making it suitable for irrigation, while ST-2 (100% sand) performed better for structures, as sand tries to quickly drain water, thus retaining less water (0.16 L/min). Change of slope was also another factor; at a 3% slope level along with 100% clay, water resistance was higher as compared to sand. Soil type-3 (ST-3) helped in the retention of water even at a 3% soil bed slope. This study will help engineers and designers in the optimization of soil bed formation for construction and irrigation purposes.

The performance of deep learning-based hydrological forecasting is highly sensitive to input quality, yet existing studies lack a systematic framework to evaluate the impact of high-frequency noise based on hydrological characteristics. To address this, we propose a frequency-based framework to assess the robustness of LSTM runoff prediction models. We define three hydrologically meaningful noise types—long-term trend, short-term event, and transient interference—and employ a synthetic noise injection strategy on the CAMELS dataset. Furthermore, we introduce an adaptive exponentially weighted moving average (AEWMA) algorithm that dynamically adjusts smoothing based on local signal variability. Results from dual-domain evaluation (time and frequency) indicate that model accuracy deteriorates significantly when high-frequency noise exceeds 30% of the total signal energy. Moderate adaptive smoothing (e.g., $\alpha =0.9\&0.6$) effectively preserves hydrological signals while mitigating performance loss, whereas aggressive smoothing suppresses meaningful variations. This study underscores the necessity of noise-type-specific preprocessing and suggests spectral energy ratios as quantitative thresholds for adaptive data quality control in hydrological modeling workflows.