Study on Optimal Allocation of Water Resources Based on Surrogate Model of Groundwater Numerical Simulation

A model for regional optimal allocation of irrigation water resources under deficit irrigation and its applications

Construction and application of a refined model for the optimal allocation of water resources — Taking Guantao County, China as an example

Large amount of hydrological data set is a kind of big data, which has much hidden and potentially useful knowledge. Hydrological prediction is important for the state flood control and drought relief. How to forecast accurately and timely with hydrological big data becomes a big challenge. There are some forecasting techniques used widely. However, they are limited by their adaptability, the data volume and the data feature. The most important problems are the high time consumption, low accuracy and bad adaptability of prediction. In this paper, a new forecasting approach based on an integration of two tasks of data mining is put forward. This approach which is called S LMDBP combines similarity search and Levenberg-Marquardt(LM) algorithm improved Double-hidden layer Back Propagation(BP) neural network. A specialized data pretreatment including three parts is applied to process the hydrological data. The results of similarity search are then input into the improved BP neural network, which not only reduces dimensionality of training data without losing important patterns, but also improves the accuracy of the prediction. A set of experiments are conducted to validate the proposed approach. The data in the experiment is the daily water level data of three stations in Jiangxi province of China from 1950 to 2010. The experimental results demonstrate the real-timing, accuracy and robustness of our approach.

It is of great practical significance to explore the efficient water resources allocation model based on ecological environment priority in coal mine areas. In this paper, an open-well coal mining area is selected as the study area. Firstly, the changing characteristics of water supply and demand balance in the study area during 2015–2020 are studied, and the defects of the existing water resources allocation mode are analyzed. Then, considering the economic, social, and ecological factors, a multi-objective model of optimal allocation of water resources was established. Finally, the optimal water resource allocation scheme was obtained by using a particle swarm optimization algorithm. The results indicate that both the water supply and consumption in the study area decreased from 2015 to 2020. The utilization rate of fresh water also has been declining year by year, and the water source structure tilted to the reclaimed water resources. Among water users, coal mining water consumption is the highest, while ecological water consumption is the lowest. There is still a large amount of recycled water that has not been reused, resulting in a waste of water resources. The optimal water resources allocation scheme shows that the allocation scheme meets the needs of each water user within the limits of the water supply source. The sewage reuse rate reached nearly 100%, which realized the maximum utilization efficiency of water resources. The utilization rate of fresh water was 29.94%, and the dependence on freshwater resources was reduced. Reclaimed water accounts for 77.8% of the total water consumption. The water source structure has been optimized to realize efficient use of water resources.

Arid-semiarid regions have suffered from sharp conflicts among water resource utilization, mining, and the environmental protection. Sustainable development in these regions requires a close coordination between economy, society and the environment. Based on systematic hydrogeological investigations, laboratory and in-situ tests, and application of innovative methodologies including theoretical analysis modeling and prediction to study water resource distribution (including surface water, groundwater, mine water and coal mine domestic water) in mining areas, this dissertation provides detailed analysis of the current situation and trend of water uses in domestic supply, agriculture and industry. It evaluates the status development and utilization, evolution trend, exploitation and utilization potential of water resources in Shen-Dong Coal Mine area, one of Chinas extra-large coal bases. Incorporated with the long and intermediate terms' development strategies of this area, the dissertation lays out a scientific allocation scheme of water resources in different hydrological years and proposes a planning mode of water resources development and utilization and a technical scheme for comprehensive water resources utilization to provide technical supports for the optimal allocation, rational exploitation, comprehensive utilization and scientific management of water resources. This dissertation is one of the best in Changan University because of the volume of reliable data, defensible scientific analysis, and world significance of the research results.

In order to solve the problem of water resources shortage in the Western mining area of China and make it reasonably and effectively allocated, the optimal allocation model of water resources was established based on Water Evaluation and Planning System in this paper. This model simulated the water resources changes of 8 scenarios from 2015 to 2035. These scenarios are consisted of different economic growth, population changes, water saving and greening intensity. The optimal development scenario applicable to the mining area and the water resources allocation results corresponding to the scenario were determined by calculating the comprehensive benefits of society, economy and ecology. The results show that the optimal development scenario for the mining area is high-speed economic growth, powerful water saving and moderate greening. Under this scenario, the total water demand of the mining area will reach 39.73 million cubic meters by 2035, the demand shortage will be 1.7 million cubic meters, and the water shortage rate will be 4.28%. The water resources allocation result corresponding to this scenario can be used as a reference for decision makers, which can provide a scientific basis for the rational use of water resources in the Western mining area.

In this paper, based on a sample selection rule and a Back Propagation (BP) neural network, a new model of forecasting daily SO2, NO2, and PM10 concentration in seven sites of Guangzhou was developed using data from January 2006 to April 2012. A meteorological similarity principle was applied in the development of the sample selection rule. The key meteorological factors influencing SO2, NO2, and PM10 daily concentrations as well as weight matrices and threshold matrices were determined. A basic model was then developed based on the improved BP neural network. Improving the basic model, identification of the factor variation consistency was added in the rule, and seven sets of sensitivity experiments in one of the seven sites were conducted to obtain the selected model. A comparison of the basic model from May 2011 to April 2012 in one site showed that the selected model for PM10 displayed better forecasting performance, with Mean Absolute Percentage Error (MAPE) values decreasing by 4% and R2 values increasing from 0.53 to 0.68. Evaluations conducted at the six other sites revealed a similar performance. On the whole, the analysis showed that the models presented here could provide local authorities with reliable and precise predictions and alarms about air quality if used at an operational scale.

The rapid development of artificial intelligence technology makes Robotics more intelligent and flexible. In this context, the kinematics inverse algorithm of the Robotics has become the basis and key technology for the further development of the Robotics. The key point of the inverse algorithm of the Robotics is to coordinate the Robotics operation arm with the corresponding action actuator at the end to realize the space attitude control of the Robotics system, and to make a theoretical basis for the motion analysis of the later Robotics. However, the traditional form of Robotics kinematics inverse algorithm avoids a lot of iterative computational solution process, which increases the complexity of the whole algorithm. Therefore, based on the above situation, this paper proposes a Robotics inverse solution algorithm based on improved BP (back propagation) neural network. In this paper, in the application of the actual algorithm, aiming at the convergence problem of the traditional BP neural network algorithm, an improved BP neural network algorithm based on the excitation function is proposed. By selecting the adaptive processing function in each layer of the neural network, the selection is matched with it. The learning rate, thus improving the accuracy of the entire motion inverse algorithm. At the same time, in order to further reduce the calculation of joint quantification, this paper also creatively introduces the algorithm of plane division auxiliary dynamic model construction. The simulation results show that the inverse kinematics algorithm based on improved BP neural network proposed in this paper has obvious advantages in solving the kinematics inverse problem of six-degree-of-freedom Robotics compared with the traditional inverse solution algorithm.

Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network

According to the accuracy of BP (Back Propagation) neural network in leveling fitting, an improved BP neural network model is proposed. In order to overcome the defect of the low convergence rate and local minimal in leveling fitting. The BP neural network is improved by using Relu (Rectified Linear Units) function as network activation function and Adam (Adaptive Moment Estimation) algorithm as network optimization function. The experimental results show that the improved BP neural network can effectively improve the accuracy.

Aiming at the problem of nonlinear coupling error in the measurement of parallel six-axis accelerometers, this study improves the back propagation (BP) neural network and proposes an improved BP neural network decoupling model that introduces the gradient descent with momentum and the Levenberg-Marquardt (LM) algorithm. By introducing the momentum factor in the model updating stage, the LM algorithm is used in the local learning stage to improve the convergence speed and shock resistance of the network, and to enhance the accuracy of the algorithm. Based on the mid-frequency standard vibration device APS 129 ELECTRO-SEIS (SPEKTRA, Stuttgart, Baden-Württemberg, Germany), the calibration data are obtained and the improved BP neural network decoupling model is trained to complete the nonlinear decoupling of the test set. Compared with the linear decoupling method, the decoupled six-axis accelerometers with the improved BP neural network model have acceleration measurement accuracies of 0.035%, 0.018% and 0.039% along the x, y and z axes, respectively, which indicates that the model has high decoupling accuracy, and it can significantly improve the measurement accuracy of the sensors. The research results can provide theoretical support for high-precision inertial navigation.

recipitation shortage and the consequent loss of several water resources, as well as the population growth, are the most important problems in arid and semi-arid regions like Iran. The providence of basic tools for optimal water resources management is considered as one of the main solutions to this problem. Since the agricultural sector is the main user of water resources, the present study presented a model based on an artificial neural network method for optimal allocation of water resources in the agricultural sector during the statistical period of 2007-2016. The objective function was determined for each product in the agricultural sector as well as product performance, each product revenues, and cultivated area of the demand function. Maximization of the objective function (to maximize economic profits) and optimal allocation of water resources were; then, conducted by using the neural network. The results of the application of the artificial neural network method to the problem of optimal water allocation showed that, in this section, higher revenues could be obtained through economic policies as well as changing the pattern of cultivation. Furthermore, the results revealed that about 44 percent of the optimal allocation revenues of water resources ($115 billion) were improved between the agricultural sectors, compared to the current situation, by applying a coefficient of 0.9 compared to two coefficients of 0.75.

We present a framework for capacity allocation decisions for assembly-test (A-T) facilities that is comprised of an optimization model and a simulation model. The optimization and simulation models are used iteratively until a feasible and profitable capacity plan is generated. The models communicate using an automated feedback loop and at each iteration the model parameters are adjusted. We describe the role of the optimization model, the simulation model and the feedback loop. Once the capacity plan is generated, it is passed down to the shop-floor for implementation. Hence, decision makers can develop accurate and more profitable execution level capacity plans using the integrated model which utilizes both optimization and simulation models. In this paper, we focus on the optimization model for capacity planning for the entire A-T facility at the individual equipment (resource) level for a two-week planning period and briefly discuss the simulation and the adjustment model.

With the enhancement of data mining technology, competitive sports informatization has become an inevitable development trend. It has become a common phenomenon to use data mining technology to help athletes train scientifically, assist coaches in rational decision-making, and improve team competitiveness. In competitive sports, cyclists' adaptation to training has a complex relationship with their physical performance. In order to explore the correlation between data and provide better training data for athletes, this study proposes a load prediction model based on BP neural network (Back propagation, BP). Considering the local convergence and random assignment of traditional BP model, an adaptive genetic algorithm with improved selection operator is used to determine the initial weights and thresholds of BP neural network to improve the accuracy of the prediction model. The experimental results show that the improved adaptive genetic algorithm improves the overall optimization ability of the BP neural network, the improved BP neural network model has good stability in the convergence process, and the algorithm can search for better weight thresholds. Compared with the basic BP neural network prediction model, the accuracy of the optimized prediction model is increased by 11.86%, and the average error value is reduced by 26.21%, which is a guide to improve the training effect of the cycling team's competitive sports.

North China is a typical developing region where the conflict between large water demand and little natural water resources exist. Thus, scientifically optimizing the allocation of regional water resources is particularly significant in this area. In this study, Haixing County, China was selected as the research region. Then, regional optimization model of water resource allocation was established, of which the objective function aims at the maximum integrated benefit of regional social and economy. In this model, two forbidden red lines of water use amount and water use efficiency were considered as the constraints, and Artificial Fish Swarm Algorithm was applied to calculate the optimization of water resource allocation. In 2020, the p = 50% total water shortage is forecast as 0.747 × 106 m3 or 2.1%. The results show that the efficiency and benefits have been improved, which illustrates that the optimization allocation of water resources through Artificial Fish Swarm Algorithm is reasonable. The research could provide a crucial technical support for regional water resource management.