Applied Water Science最新文献

The primary objective of this study is to assess changes in the water capacity of Aswan High Dam Lake (AHDL) due to evaporation and seepage losses. To achieve this, a comprehensive methodology was applied, incorporating Sentinel-3 imagery for surface area extraction using remote sensing techniques. By integrating water area calculations from satellite images, field data, and the lake’s water balance equation, monthly evaporation and seepage losses were estimated for 2021 and 2022. The results indicate that the average monthly evaporation losses for 2021 were approximately 1.41 billion cubic meters (Bm³), closely aligning with the Ministry of Water Resources and Irrigation (MWRI) estimates of 1.37 Bm³, representing a slight overestimation of 2.92% by the water balance method. Similarly, the average monthly seepage losses for 2022 were estimated at 0.005 Bm³, compared to MWRI’s reported 0.0046 Bm³, reflecting an overestimation of 8.70%. Additionally, the study found that the average monthly evaporation rate within AHDL was 210.88 mm/month, closely matching the Aswan High Dam Authority’s (AHDA) computed value of approximately 204.9 mm/month. These findings demonstrate that the water balance method, when integrated with remote sensing and field data, serves as a reliable tool for estimating monthly evaporation and seepage losses, as well as evaporation rates in AHDL.

A data-driven artificial intelligence framework is developed to predict and optimize the performance of nanophotonics-enabled solar membrane distillation reactor for water and wastewater treatment. An artificial neural network (ANN) is constructed to model the nonlinear relationships between operating conditions and system performance. The ANN model considers five input parameters: feed temperature, coolant temperature, solar irradiance, feed flow rate, and sweeping air humidity, and predicts three key performance indicators: permeate flux, gain-to-output ratio (GOR), and heat loss. To address the challenge of ANN architecture selection, a genetic algorithm (GA) is employed to systematically optimize the network architecture: hidden layers and neurons. Unlike traditional ANN-based membrane distillation models that rely on manual tuning, the proposed GA-optimized framework provides a computationally efficient and globally optimal approach for ANN architecture optimization. The constructed ANN model using GA demonstrates high accuracy without overfitting, achieving coefficients of determination values approaching 0.99, mean squared error below (1times {10}^{-3}), and average relative error under 6%. Single-objective GA optimization is applied to determine the optimal operating conditions for maximizing flux, minimizing heat loss, and maximizing GOR. To account for trade-offs among these performance indicators, multi-objective optimization is conducted using the nondominated sorting genetic algorithm II. The optimization results identify practical operating ranges, with a maximum flux of (1.38-1.54 ( text{kg}/{text{m}}^{2}/text{h})), heat losses ranging from (32.47) to (33.38%), and a GOR reaching 0.944–1.24. The algorithm is validated against published experimental data and demonstrates superior predictive accuracy over trial-and-error ANN models, confirming its robustness and applicability for membrane distillation optimization.

Riparian vegetation is crucial for both terrestrial and aquatic ecosystems, providing numerous essential benefits. The absence of riparian vegetation along streams can lead to various pressures that negatively affect macroinvertebrates and their habitats. This study assessed the effect of riparian vegetation on water quality and macroinvertebrates in the Gilgel Gibe tributaries of southwestern Ethiopia. Eighteen sample sites were collected through cross-sectional studies. A total of three thousand two hundred twenty three macroinvertebrates were counted from 50 families and 75 plant species. The first-order streams had higher plant species diversity than the second and third-order streams. Studied tributaries were dominated by Ephemeroptera (42.96%), Diptera (17.49%), Odonata (15.19%) and Coleoptera (11.35%). Fabaceae were the most diverse family, with 13 Number of species, followed by Lamiaceae and Rubiaceae (each with 5 number of species). Non-parametric output diversity indexes such as Evennes, Shannon, Simpson, Margalef, % Ephemeroptera-Trichoptera, and Biological Monitoring Working Party all showed significant results. This means that these indicators showed a statistically significant difference between plant species and land use categories. These data imply that vegetation types and land use types have an impact on water quality and macroinvertebrates communities. Furthermore, vegetation types were the main factors that influenced water quality and macroinvertebrates diversity.