Titlebook: Water Quality Management; Assessment and Inter Gholamreza Asadollahfardi Book 2015 The Editor(s) (if applicable) and The Author(s), under e

粗糙

Water Quality Indices (WQI),. Due to the extent of water quality parameters, water quality indices (WQI) could be used as a point scale for interpretation of these parameters. WQI is the essential prerequisite of water quality management. Since 1978, much effort have been done to present techniques to summarize water quality d

幻想

Selection of Water Quality Monitoring Stations,k design is an efficient method to manage water quality. The most crucial part is to find appropriate locations for monitoring stations. In the past, most of water quality selection stations were subjective and the designs on the network had some human error. However, now there are several mathemati

OMIT

Time Series Modeling,terministic model are not applicable. In this regards, stochastic modeling are applied for estimating the future value of water quality parameters. There has been much effort in developing this technique for solving other engineering matters. Time series modeling as a stochastic model is trying to m

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Water Quality Indices (WQI),. Due to the extent of water quality parameters, water quality indices (WQI) could be used as a point scale for interpretation of these parameters. WQI is the essential prerequisite of water quality management. Since 1978, much effort have been done to present techniques to summarize water quality d

Ondines-curse

Artificial Neural Network, Driven Models using information and collected data (input data) find out the relationship between inputs and outputs. In this regard, Artificial Neural Network (ANN) is one of the Data Driven Models which has recently been applied as a tool for modeling complicated processes. In this chapter, after

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微不足道

Introducing of Ce-Qual-W2 Model and Its Application,e water quality parameters of surface and groundwater. While detail information about surface water or ground water are available, deterministic models for predicting future values of water quality is more proper than stochastic models. In this regard, numerical models demonstrated an impressive cap

Postmenopause

Artificial Neural Network, Driven Models using information and collected data (input data) find out the relationship between inputs and outputs. In this regard, Artificial Neural Network (ANN) is one of the Data Driven Models which has recently been applied as a tool for modeling complicated processes. In this chapter, after

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Oafishness

Book 2015cessary and having enough data without proper interpretation is not helpful for water quality management decisions..Hence, analysis of the existing data and prediction of future of water quality is vital. The current volume first defines the importance of water quality parameters regarding public he