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Abudu S.,Mexico State University | Abudu S.,Xinjiang Water Resources Research Institute | King J.P.,Mexico State University | Sheng Z.,Texas AgriLife Research Center
Journal of the American Water Resources Association | Year: 2012

This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function-noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross-correlation statistical tests. The chi-square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one- to three-month-ahead model forecasts for the testing period of 1984-1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one-month-ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two-month-ahead and three-month-ahead forecasts. For three-month-ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one- to three-month-ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin. © 2011 American Water Resources Association. Source

Wang F.,Xinjiang Water Resources Research Institute | Liu Z.,Gezhouba Xinjiang Engineering Bureau
Advances in Science and Technology of Water Resources | Year: 2013

The special long-distance drilling rigs cannot be used in the site of Neelum-Jhelum Hydropower Project in Pakistan due to their large volume. The horizontal drilling method is adopted based on the experimental and economical analyses by using light common rigs. The drilling rigs are improved by installing some stabilizing devices, and some modifications are performed by transforming the hoisting swivel elevator, the loading and unloading frame for orifice, the reaming device, the blocker and other associated components. The drilling parameters for different strata are determined based on experiments. A series of drilling components and control parameters suitable for engineering practices of the horizontal directional drilling with common drilling rigs are proposed through continuous improvement, adjustment and exploration. They are successfully applied in Neelum-Jhelum Hydropower Project and provide basis for the optimization parameters and construction schemes of excavation sections and culvert bracings. Source

Abudu S.,New Mexico State University | Abudu S.,Xinjiang Water Resources Research Institute | King J.P.,New Mexico State University | Bawazir A.S.,New Mexico State University
Journal of Hydrologic Engineering | Year: 2011

Monthly streamflow forecasting during spring-summer runoff season using snow telemetry (SNOTEL) precipitation and snow water equivalent (SWE) as predictors in the Rio Grande Headwaters Basin in Colorado was investigated. The transfer-function noise (TFN) models with SNOTEL precipitation input were built for monthly streamflow. Then, one-month-ahead forecasts of TFN models for the springsummer runoff season were modified with SWE using an artificial neural networks (ANN) technique denoted in this study as hybrid TFN + ANN. The results indicated that the hybrid TFN + ANN approach not only demonstrated better generalization capability but also improved one-month-ahead forecast accuracy significantly when compared with single TFN and ANN models. The normalized root mean squared errors (NRMSE) of one-month-ahead forecasts of TFN, ANN, and TFN + ANN approaches for spring-summer runoff season were 0.38, 0.30, and 0.25. These findings accentuate that the presented stochastic hybrid modeling approach is an advantageous option to improve one-month-ahead forecast accuracy of monthly streamflow in spring-summer runoff season in the Rio Grande Headwaters Basin. © 2011 American Society of Civil Engineers. Source

Zamani Sabzi H.,New Mexico State University | Humberson D.,New Mexico State University | Abudu S.,Xinjiang Water Resources Research Institute | King J.P.,New Mexico State University | King J.P.,Stanford University
Expert Systems with Applications | Year: 2016

In fuzzy logic controllers (FLCs), optimal performance can be defined as performance that minimizes the deviation (error term) between the decisions of the fuzzy logic systems and the decisions of experts. A range of approaches - such as genetic algorithms (GA), particle swarm optimization (PSO), artificial neural networks (ANN), and adaptive network based fuzzy inference systems (ANFIS) - can be used to pursue optimal performance for FLCs by refining the membership function parameters (MFPs) that control performance. Multiple studies have been conducted to refine MFPs and improve the performance of fuzzy logic systems through the application of a single optimization approach, but since different optimization approaches yield different error terms under different scenarios, the use of a single optimization approach does not necessarily produce truly optimal results. Therefore, this study employed several optimization approaches - ANFIS, GA, and PSO - within a defined search engine unit that compared the error values from the different approaches under different scenarios and, in each scenario, selected the results that had the minimum error value. Additionally, appropriate initial variables for the optimization process were introduced through the Takagi-Sugeno method. This system was applied to a case study of the Diez Lagos (DL) flood controlling system in southern New Mexico, and we found that it had lower average error terms than a single optimization approach in monitoring a flood control gate and pump across a range of scenarios. Overall, using evolutionary algorithms in a novel search engine led to superior performance, using the Takagi-Sugeno method led to near-optimum initial values for the MFPs, and developing a feedback monitoring system consistently led to reliable operating rules. Therefore, we recommend the use of different methods in the search engine unit for finding the optimal MFPs, and selecting the MFPs from the method which has the lowest error value among them. © 2015 Elsevier Ltd. All rights reserved. Source

Cui C.,Xinjiang Water Resources Research Institute | Abudu S.,Xinjiang Water Resources Research Institute | King J.P.,New Mexico State University | Sheng Z.,Xinjiang Water Resources Research Institute | Sheng Z.,Texas AgriLife Research Center
World Environmental and Water Resources Congress 2012: Crossing Boundaries, Proceedings of the 2012 Congress | Year: 2012

One of the largest world karez water supply systems located in the Turpan oasis, Xinjiang Uyghur Autonomous Region, China is facing challenges as water demand increases and overexploitation of groundwater by deep wells. In this paper we evaluated the vitality of the ancient karez systems in various aspects in modern society by providing examples from Turpan region of China. These aspects include the historical and cultural importance, socio-economic impacts, interactions with the surrounding environment, contribution to agricultural biodiversity in arid lands, and the unique regional characteristics of karezes. The results show that the karez systems are not only economically feasible but also a sustainable water supply for irrigation and domestic uses. Furthermore, karezes have invaluable historical, cultural and social significance. In such regions, the proper conservation and maintenance of karez systems will help sustain water supplies and contribute to economic development. © 2012 ASCE. Source

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