Studies addition, this will lead to an improvement in

Studies on the
water quality of rivers are extremely important, particularly because rivers
are one of the main sources of water supply for potable, agricultural, and
industrial usage. Unfortunately, river pollution has become one of the most
important environmental problems (Fan
et al., 2009, Tsihrintzis, 2013, Parsaie and Haghiabi, 2017). Monitoring the parameters of water
quality of rivers allows better management of water quality. In addition, this
will lead to an improvement in the public health
level; therefore, continuous surveying of the water quality of rivers is of
high importance (Ishak
et al., 2012). Water quality indices are
parameters that are related to the biological, physical, and chemical
properties of water. Usually, water quality is determined by measuring its
biochemical oxygen demand (BOD), chemical oxygen demand (COD) and declined dissolved
oxygen (DO) level. The dissolved oxygen level is a measure of the health of the
aquatic system. A certain minimum level of DO in water is required for the
aquatic life to survive (Basant et al., 2010). The sources of DO in a water body include re-aeration from the atmosphere, photosynthetic oxygen production
and DO loading. The sinks include oxidation of carbonaceous and nitrogenous
material, sediment oxygen demand, and
respiration by aquatic plants (Kuo et al., 2007). The chemical oxygen demand is used as a measure of the oxygen
equivalent of the organic matter content of a sample that is susceptible to
oxidation by a strong chemical oxidant. The COD is used to measure the total
quantity of oxygen-consuming substances
in the complete chemical breakdown of organic substances in water. It is an
important parameter in measuring quality and determining what organic load is
present in the water (Verma and Singh, 2013). The Biochemical oxygen demand is an approximate measure of the
amount of biochemical degradable organic matter present in a water sample. It
is defined by the amount of oxygen required for the aerobic microorganisms
present in the sample to oxidize the organic matter to a stable organic form (Chapman, 1996).  Excessive BOD loads damage
the quality of river water. It causes low DO (dissolved oxygen) concentration
and unsuitable living conditions for
flora and fauna in the river. At the same
time, BOD–DO relationships include an exchange
with the river bed and nitrification and denitrification
(Radwan et al., 2003). Nutrients and light in the phytoplankton growth, the relationship
between DO and phytoplankton concentrations and ammonia affect the BOD
degradation (Lopes et al., 2005). Dissolved oxygen levels, water temperature, water flow,
chlorophyll a and nutrient levels (ammonia, nitrite, nitrate) are among the
most critical factors for biochemical oxygen demand (BOD) in the rivers. The
oxygen consumption from degradation of organic material is normally measured as
BOD and COD, so there is an important relationship
between them. Performing the test for BOD requires significant time and
commitment for preparation and analysis. This process requires 5 days, with
data collection and evaluation occurring on the last day. A test is used to
measure the amount of oxygen consumed by these organisms during a specified
period of time (usually 5 days at 20 C). The difference in initial DO readings
(prior to incubation) and final DO readings (after 5 days of incubation) is
used to determine the initial BOD concentration of the sample. This is referred
to as a BOD5 measurement (Dogan et al., 2009). Several water quality models such as traditional mechanistic
approaches have been developed in order to manage the best practices for
conserving the quality of water. Most of these models need several different
input data which are not easily accessible and make it a very expensive and time-consuming process (Suen and Eheart, 2003). In recent
years, several studies have been
conducted on water quality forecast models (Kurunç et al., 2005, Li, 2006,
Goyal et al., 2013).
However, since a large number of factors affecting the water quality have a
complicated non-linear relation with the variables; traditional data processing
methods are no longer good enough for solving the problem (Nasr et al., 2012, Mokarram, 2015). On
the other hand, the artificial neural networks (ANNs) capable of imitating the
basic characteristics of the human brain such as self- adaptability, self-organization, and error tolerant and have
been widely adopted for model identification, analysis and forecast, system
recognition and design optimization (Diamantopoulou et al., 2005, Niu et
al., 2006, Sarkar and Pandey, 2015).Unlike
many statistically based water quality models, which assume a linear
relationship between response and prediction variables and their normal
distribution, ANNs are able to map the non-linear relationships that are
characteristics of aquatic eco-systems (Lek and Guégan, 1999). During last about two decades, ANNs have undergone an explosive
development in application in almost all the areas of research (Lerner et al., 1994, Kung and Taur,
1995, Raman and Chandramouli, 1996, Chu and Bose, 1998, Li, 2006, Ciampi and
Lechevallier, 2007, Messikh et al., 2007, Hanbay et al., 2008, Dürrenmatt and
Gujer, 2012, Abyaneh, 2014).
The ANN approach has several advantages over traditional phenomenological or
semi-empirical models since they require
known input data set without any assumptions (Gardner and Dorling, 1998). The ANN develops a mapping of the input and output variables,
which can subsequently be used to predict desired output as a function of
suitable inputs (Friedman and Kandel, 1999). A multi-layer neural network can approximate any smooth,
measurable function between input and output vectors by selecting a suitable
set of connecting weights and transfer functions (Gardner and Dorling, 1998).
ANN models have been widely applied to the water quality problems (Rogers and Dowla, 1994, Wen and
Lee, 1998, Lek and Guégan, 1999, Bowers and Shedrow, 2000, Cancelliere et al.,
2002, Kuo et al., 2004). Recently, by developing soft computing
techniques in most areas of water engineering as a powerful tool for modeling
(Azamathulla et al., 2008) researchers have attempted to
use artificial intelligence for modeling water quality
problems (Noori
et al., 2010, Abyaneh, 2014, Emamgholizadeh et al., 2014, Noori et al., 2015,
Dehdar-Behbahani and Parsaie, 2016, Parsaie and Haghiabi, 2017).

The present
study uses Mg, Ca, Na, Cl, SO4, HCO3, CO3, TDS, EC, K and SAR as the input and
considered DO, BOD and COD as the output to investigate the quality of Karun
water in the vicinity of Ahvaz city. The
novelties of this study in comparison with the previous ones are a) application of newer data, b)
sensitivity analysis of parameters by Gamma test before simulation by the neural network and c) application of more input
data for construction of neural network architecture.

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