https://scwe.uoz.ac.ir/ Soft Computing in Water Engineering en daily 1 Wed, 07 Jan 2026 00:00:00 +0330 Wed, 07 Jan 2026 00:00:00 +0330 https://scwe.uoz.ac.ir/article_238613.html In this study, convolutional neural networks (CNN) and statistical methods are used to optimize pump performance and reliability in drinking water wells. An evaluation of seasonal variations in pump efficiency and operational characteristics was conducted over a four-year period in Bandar Torkaman, Iran, using deep learning and probabilistic analysis. To extract features from time-series pump data, a multi-layer CNN architecture was developed using 32 and 64 layers of filters. The model displayed significant seasonal variations, with optimal accuracy in winter (R2 = 0.95, MABE = 14.60, RMSE = 39.4 in January) and reduced reliability in summer (R2 = 0.54, MABE = 171.60, RMSE = 386.9). In 10 out of 12 months, the Weibull distribution best characterized water levels, whereas the Lognormal distribution dominated flow rate patterns in 7 months, revealing system behavior. Winter operations achieved 4.42% efficiency and minimal energy consumption (3.15 kWh/m3) compared to summer operations, which achieved 0.70% efficiency and 24.31 kWh/m3. According to the study, optimal operating parameters include flow rates of 7.5-8.5 m3/h, water levels of 45-65 meters, and seasonally-adjusted RPM ranges (1100-1200 RPM in winter, 2350-3040 RPM in summer). These findings offer valuable insights for optimizing pump operations in regions with significant seasonal variations, as well as highlighting the importance of adaptive management strategies and season-specific predictive modeling approaches to enhance system performance and energy efficiency. https://scwe.uoz.ac.ir/article_240765.html Background: Arid wetlands like Iran's Bakhtegan face severe desiccation from climate change and human activities. Monitoring these ecosystems is hindered by insufficient in-situ data and complex non-linear dynamics, making high-precision monthly forecasting in ungauged basins a significant challenge for water engineering. Objective: This study develops a robust monthly surface water extent (SWE) forecasting framework using a multi-scale hybrid Stacking Ensemble. The goal is to integrate satellite data with explainable AI (XAI) to ensure physical interpretability and provide a reliable tool for early warning systems. Methods: A 10-year (2013 to 2023) dataset of Landsat 8/9, CHIRPS, and MODIS data was extracted via Google Earth Engine. After Savitzky-Golay denoising, a two-layer Stacking Ensemble integrating XGBoost, ExtraTrees, and Ridge Regression was implemented. Hydrological memory was explicitly modeled using 1-month and 12-month lags alongside 6-month cumulative water balances. Results: The framework achieved high accuracy with a Nash-Sutcliffe Efficiency (NSE) of 0.81, R-squared of 0.81, and PBIAS of -3.55 percent. SHAP analysis identified 12-month area persistence and 6-month cumulative hydro-balance as the primary drivers. The model successfully isolated anthropogenic signals, with late-summer residuals indicating unmodeled irrigation withdrawals and dam impacts. Conclusions: This research offers a scalable and interpretable soft computing tool for water management in data-scarce regions. By providing reliable forecasts, it supports decision makers in optimizing water allocation and preventing ecological collapse in endangered hypersaline wetlands globally. https://scwe.uoz.ac.ir/article_240766.html Soil hydraulic properties, defining a soil's capacity to store, transmit, and redistribute water, are fundamental to hydrological processes. Investigating the agents that influence these properties is therefore critical for the sustainable management of soil and water resources. This is particularly vital in arid regions, where the enhancement of soil hydraulic characteristics can significantly improve rainwater infiltration, concurrently conserving water through the reduction of surface runoff and evaporative losses. While the impact of soil physico-chemical properties on hydraulic characteristics is well established, the contribution of soil-dwelling fauna is still unwell understood and often overlooked. This review synthesizes current knowledge on how these organisms (soil-dwelling fauna) engineer soil properties and describes their influence. Soil-dwelling fauna, predominantly invertebrates, function as critical bioturbators that engineer soil hydrology. Through the creation and maintenance of biopores, these organisms directly enhance soil hydraulic conductivity, preferential flow paths, and infiltration rates, while concurrently improving overall soil structur stability. Furthermore, through bioturbation, soil fauna enhance aggregate stability, redistribute organic matter, and mitigate soil compaction. These activities collectively improve critical hydraulic properties, bolster erosion control, and increase soil fertility, thereby supporting sustainable soil and water management. While soil fauna such as earthworms, dung beetles, termites, ants, and mole crickets are recognized as key bioturbators enhancing soil hydraulic properties, the magnitude and efficacy of their effects are highly contingent upon species identity, environmental context, and temporal dynamics. Conversely, the excavation of extensive tunnel networks by some soil-dwelling vertebrates can destabilize soil architecture, promoting its degradation and accelerating erosion. Overall, the influence of soil fauna on hydraulic properties is not universal but varies according to body size of organisms, excavation methods, burrow architecture, bioturbation modes, and adaptive behaviors across heterogeneous environments. These findings highlight the complex role of soil-dwelling fauna in shaping soil-water interactions and hydraulic properties. https://scwe.uoz.ac.ir/article_240767.html This study examines the water footprints of five different jalizi crop production systems (Gandak, Sefidak, Pashmak, watermelon, and melon). A questionnaire survey was conducted on 277 farms. Blue, green, and grey water footprints were calculated based on methodology presented within the Water Footprint Assessment Manual. Simultaneously, physical economic, as well as economic, water productivities were investigated along with correlation analyses with emergy-based sustainability indices. The outcome shows that water footprints varied between 513.6 and 849.2 million cubic meters per ton, with Pashmak accounting for lowest water footprints, showing a percentage variation of 65. Blue water footprints were found to contribute largely (43.8-83.5%) within all five production systems within this hyperdry environment, with only 54 mm being deposited annually through rain, showing irrigation as their absolute dependability, with the grey component contributed through nitrogenous fertilization being in the range of (15.5%–55.5%). Similarly, green water footprints were found to be insignificantly low, being <1.1% as rain was negligible. However, Pashmak was found to be most effective with highest physical (1.95 kg/m³) as well as economic (58,416 Toman/m3) productivities. This might be due to its relatively shorter crop-growing duration of 2.5 months. Also, a strong negative correlation was found between emergy renewability coefficients and water footprint (r= -.955, p <0.011), clearly ascertaining the complementary nature of water footprint studies. Achieving precision through drip technology within this water-stressed crop production, necessitating reduced use of "950–1050" nitrogen within "200–300" kg per ha, as well as appropriate crop improvement, largely emerge as imperative measures.