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Streamflow forecasting process exhibited highly nonstationary and stochastic pattern, thus not easy to be done with simple models. There is a need to develop an efficient and precise streamflow forecasting system which is vital for water management at hydrological infrastructures like Aswan High Dam (AHD). As the decision makers will be able to dec...
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For migratory species, successful navigation is critical to fitness. In Atlantic salmon, for example, there is evidence that during migration from natal streams to the sea, passage through waters with poorly defined or mixed water velocity patterns may constrain directional navigation, causing individuals to become trapped or delayed in lakes or ot...
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... The current mainstream prediction models encompass grey models (Ding, 2021), neural network models , time series models (González et al., 2024), the Holt-Winters exponential smoothing method (Hayana and Ali, 2022), and their hybrid models (Jin, 2022). These predictive models have not only demonstrated excellent performance in fields such as agriculture, industry, and medicine but have also been extensively applied in water resource management in arid and semi-arid regions. ...
Water resources are the lifelines of the agricultural development in Xinjiang. Currently, the problem of water shortage for agriculture in this region is becoming increasingly severe. Against this backdrop, predicting the changing trends of water supply and agricultural water use in Xinjiang and analyzing the supply and use relationship between them are of great practical significance for ensuring the sustainable development of regional agriculture. Firstly, we conducted an in-depth analysis of the water supply and agricultural water use patterns in Xinjiang over the past two decades. Secondly, we evaluated and compared several mainstream water resource prediction models, ultimately developing a novel GM(1,1)-NN essemble model. Validation results demonstrated that this model exhibits superior accuracy in forecasting water supply and agricultural water use compared to other existing models. Finally, we utilized the newly developed GM(1,1)-NN essemble model to predict short-term water supply and agricultural water use trends in Xinjiang. Based on these findings, we proposed recommendations for water resource conservation from both technological and regional planting perspectives. The key results are as follows: (1) There are significant regional disparities in water resources in Xinjiang, primarily attributed to uneven precipitation distribution and imbalanced economic development. (2) The GM(1,1)-NN essemble model demonstrates high short-term predictive accuracy for both water supply and agricultural water use in Xinjiang. (3) According to our GM(1,1)-NN essemble model’s projections, both water supply and agricultural water use in Xinjiang are expected to exhibit a downward trend in the coming years. The reduction in agricultural water use will help allocate more water resources to non-agricultural sectors. (4) Despite these improvements, the contradiction between water shortage and the high proportion of agricultural water use (approaching to 88%) remains unresolved. Therefore, it is recommended to reduce agricultural water use through the widespread adoption of water-saving facilities and the optimization of crop planting structures across different regions.
The Nile has given not only material gifts to Egypt and the world, but also intellectual gifts to science, especially to geoscience. The Nile still has much to teach science-especially about climatology, as it reflects climatic behaviors over vast areas in tropical and subtropical zones. These climatic behaviors have been documented across time with some of its extraordinarily long records surviving to present day. The records provide insights to the perpetual change of climate and support quantification of change in a stochastic framework.
A high concentration of dissolved oxygen is essential for the maintenance of healthy aquatic ecosystems. Aeration studies have been conducted in both closed systems and open channels utilizing conventional hydraulic structures. However, the feasibility of aeration through screens with square openings has not yet been explored. This study aimed to evaluate the aeration efficiency (E20) in an open channel system using screens with square jets. Five input parameters were analyzed: angle of inclination, number of square jets, discharge, hydraulic radius, and Froude number. The results showed that Nj, α, and Q significantly influence E20. The highest E20 recorded was 37% on level terrain with moderate discharge in the open channel. This method has the potential to enhance oxygen concentrations in rural regions where skilled labor and mechanised systems may be challenging. The study also focused on identifying suitable soft computing models for predicting E20. Five machine learning approaches were employed: artificial neural network, random forest with Bagging, Gaussian process utilizing the Pearson VII function kernel (GP_PUK), support vector machine with the PUK kernel function (SVM_PUK), and a radial basis function (SVM_RBF). The GP model employing the PUK kernel function demonstrated superior performance.
The spillway is a component of the dam's flow control channel that serves to enhance regulation and boost the flow rate that passes through the spillway building. By rapidly changing local pressure and velocity, surface cracks can cause cavitation damage. Cavitation is defined as the phenomenon where pressure is less than 1 atm, resulting in air bubbles on the building surface, causing holes due to the release of aggregate grains on the construction surface. Unremoved cavitation can endanger the bottom surface of the spillway due to scouring/erosion. Damage to the canal floor due to continuous scouring can cause water to enter through the scour cracks, thus disrupting the stability of the spillway. Hence, this article mainly discusses a cavitation study that was conducted at the dam spillway. Based on the review in this paper, to determine hydraulic cavitation features of a spillway, most research employ prototype observation, physical model observation, and numerical model simulation. Furthermore, more comprehensive results produced by numerical simulations than by actual investigation is proved by previous research reviewed in this paper. However, using both a physical model and a numerical simulation to determine a spillway's hydraulic cavitation characteristics is reliable.
Ozone (O3) from the troposphere is one of the substances that has a strong effect on air pollution in the city of Tanger. Prediction of this pollutant can have positive improvements in air quality. This paper presents a new approach combining deep-learning algorithms and the Holt–Winters method in order to detect pollutant peaks and obtain a more accurate forecasting model. Given that LSTM is an extremely powerful algorithm, we hybridized with the Holt–Winters method to enhance the model. Making use of multiple accuracy metrics, the models' efficiency is investigated. Empirical findings reveal the superiority of the hybrid model by providing forecasts that are more accurate with an index of agreement equal to 0.91.
