Prediction of China's Water Shortage in the Year of 2025

2013 ◽  
Vol 409-410 ◽  
pp. 83-88 ◽  
Author(s):  
Fu Rong Zhang
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Fresh Water ◽  
Neural Network Model ◽  
Water Scarcity ◽  
Water Demand ◽  
Water Shortage ◽  
Gray Model ◽  
International Water ◽  
Fresh Water Resource

In order to predict the demand of fresh water in China in the year of 2025, a mathematical model is proposed based on the summation of demand of water in ten major regions in China. The gray model is applied to predict the fresh water resource in the year of 2025 while neural network model is applied to predict the fresh water demand. The degree of water shortage is evaluated by the international water scarcity assessment criteria which are commonly used. The conclusion is that some provinces in China may be faced with big challenges for water shortage.

scholarly journals Identification of Neural Network Model of Robot to Solve the Optimal Control Problem

2021 ◽  
Vol 20 (6) ◽  
pp. 1254-1278
Author(s):  
Elizaveta Shmalko ◽  
Yuri Rumyantsev ◽  
Ruslan Baynazarov ◽  
Konstantin Yamshanov
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mathematical Model ◽  
Optimal Control ◽  
Neural Network Model ◽  
Software Package ◽  
Control Object ◽  
Robotic Systems ◽  
Object Model ◽  
Modern Machine

To calculate the optimal control, a satisfactory mathematical model of the control object is required. Further, when implementing the calculated controls on a real object, the same model can be used in robot navigation to predict its position and correct sensor data, therefore, it is important that the model adequately reflects the dynamics of the object. Model derivation is often time-consuming and sometimes even impossible using traditional methods. In view of the increasing diversity and extremely complex nature of control objects, including the variety of modern robotic systems, the identification problem is becoming increasingly important, which allows you to build a mathematical model of the control object, having input and output data about the system. The identification of a nonlinear system is of particular interest, since most real systems have nonlinear dynamics. And if earlier the identification of the system model consisted in the selection of the optimal parameters for the selected structure, then the emergence of modern machine learning methods opens up broader prospects and allows you to automate the identification process itself. In this paper, a wheeled robot with a differential drive in the Gazebo simulation environment, which is currently the most popular software package for the development and simulation of robotic systems, is considered as a control object. The mathematical model of the robot is unknown in advance. The main problem is that the existing mathematical models do not correspond to the real dynamics of the robot in the simulator. The paper considers the solution to the problem of identifying a mathematical model of a control object using machine learning technique of the neural networks. A new mixed approach is proposed. It is based on the use of well-known simple models of the object and identification of unaccounted dynamic properties of the object using a neural network based on a training sample. To generate training data, a software package was written that automates the collection process using two ROS nodes. To train the neural network, the PyTorch framework was used and an open source software package was created. Further, the identified object model is used to calculate the optimal control. The results of the computational experiment demonstrate the adequacy and performance of the resulting model. The presented approach based on a combination of a well-known mathematical model and an additional identified neural network model allows using the advantages of the accumulated physical apparatus and increasing its efficiency and accuracy through the use of modern machine learning tools.

scholarly journals Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Hao Peng ◽  
Han Wu ◽  
Junwu Wang ◽  
Tayfun Dede
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Water Consumption ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Water Demand ◽  
Average Error ◽  
Local Optimum ◽  
Construction Engineering ◽  
Bp Neural Network Model

The scientific and effective prediction of the water consumption of construction engineering is of great significance to the management of construction costs. To address the large water consumption and high uncertainty of water demand in project construction, a prediction model based on the back propagation (BP) neural network improved by particle swarm optimization (PSO) was proposed in the present work. To reduce the complexity of redundant input variables, this model determined the main influencing factors of water demand by grey relational analysis. The BP neural network optimized by PSO was used to obtain the predicted value of the output interval, which effectively solved the shortcomings of the BP neural network model, including its slow convergence speed and easy to fall into local optimum problems. In addition, the water consumption interval data of the Taiyangchen Project located in Xinyang, Henan Province, China, were simulated. According to the results of the case study, there were four main factors that affected the construction water consumption of the Taiyangchen Project, namely, the intraday amount of pouring concrete, the intraday weather, the number of workers, and the intraday amount of wood used. The predicted data were basically consistent with the actual data, the relative error was less than 5%, and the average error was only 2.66%. However, the errors of the BP neural network model, the BP neural network improved by genetic algorithm, and the pluralistic return were larger. Three conventional error analysis tools in machine learning (the coefficient of determination, the root mean squared error, and the mean absolute error) also highlight the feasibility and advancement of the proposed method.

scholarly journals A System for Detecting Anomalous Observations in Data on Healthcare Services

2021 ◽  
Vol 27 (3) ◽  
pp. 356-367
Author(s):  
S. L. Blyumin ◽  
R. V. Scheglevatykh ◽  
A. A. Naydenov ◽  
A. S. Sysoev
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Network Model ◽  
Neural Network Model ◽  
Healthcare Services ◽  
Neural Network Classifier ◽  
The Neural Network ◽  
Incoming Information ◽  
Process Behavior ◽  
The Mathematical Model

A description of the mathematical model of a neural network classifier of data on healthcare in the institutions of the Lipetsk region is given in order to identify atypical (abnormal) records. Anomaly detection refers to the problem of finding data that is inconsistent with some expected process behavior or metric occurring in the system. Due to the large number of inputs to the neural network model, the time it takes to process the incoming information also increases. To assess what factors should be transmitted to the input of the neural network classifier, an approach to the reduction of the neural network model based on sensitivity analysis is proposed. The description of a set of software tools for solving the problem is presented.

scholarly journals Kelangkaan air bersih : Telaah sistem pelayanan penyediaan air bersih di Kabupaten Bekasi

2016 ◽  
Vol 12 (2) ◽  
pp. 175-186
Author(s):  
Ninin Gusdini ◽  
M. Januar J Purwanto ◽  
Kukuh Murtilaksono ◽  
Kholil Kholil
Keyword(s):  
Water Scarcity ◽  
Water Demand ◽  
Supply And Demand ◽  
Water Source ◽  
Water Shortage ◽  
Raw Water ◽  
Performance Factors ◽  
Piping Systems ◽  
High Level

Imbalance of supply and demand is the beginning of a scarcity of clean water. To detect the occurrence of water scarcity needed calculation of water demand to estimating water source to be used. Water scarcity occurs due to natural factors associated with limited sources of raw water to water drinking, the system performance of water supply services are inefficient and increasing of water demand. The high level of leakage, inefficient processing, and low of coverage service from PDAM are performance factors that is causing water scarcity. Meanwhile, population growth and development of the region is demand side factor which led to the scarcity of water. Shortage of safe drinking water to the community caused by the condition of water management is not optimal. This is demonstrated by the leakage rate is still above the standard that tolerated (20%), a relatively small customer coverage is 15.69% of the population that has been underserved by the piping systems, idle capacity because of unoptimal instalation. Scarcity can anticipate with three ways: (1) increasing quantity and quality of raw water, (2) save the use of water and recycle waste water, (3) decrease of leakage, improve PDAM performance, and efficiency of water treatment.

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