Article: Water Resources Management
Prediction of Water Level using Monthly Lagged Data in Lake Urmia, Iran
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Babak Vaheddoost, Ph.D. Candidate at Istanbul Technical University, Dep. Civil Engineering, vaheddoostb@itu.edu.tr
Hafzullah Aksoy, Prof. Dr. at Istanbul Technical University, Dep. Civil Engineering, haksoy@itu.edu.tr
Hirad Abghari, Associate Prof. Department of Range and Watershed Management, Faculty of Natural ResourcesUrmia University, h.abghari@urmia.ac.ir
Hirad Abghari, Associate Prof. Department of Range and Watershed Management, Faculty of Natural ResourcesUrmia University, h.abghari@urmia.ac.ir
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Abstract:
Prediction of water level fluctuations in lakes is a necessary task in hydrological and limnological studies. Lake Urmia, a hyper-saline lake in the North Western part of Iran, is dealing with a gradual atrophy. In this study, parametric and nonparametric models are used for predicting monthly water level fluctuations in Lake Urmia. Eleven previous water levels in the form of monthly lagged data are used as the known independent variables of the model while lake water level at the twelfth month is considered as the unknown dependent variable to be predicted. Parametric models used in the modelling are multi-linear regression (MLR), additive and multiplicative non-linear regression (ANLR and MNLR) and decision tree (DT) while feed forward back propagation neural network (FFBP-NN), generalized regression neural network (GR-NN) and radial basis function neural network (RBF-NN) are used to represent the non-parametric approach. Monthly water level data in Lake Urmia observed for 1966–2010 are used for the case study. Four criteria, coefficient of determination, Lin’s concordance correlation coefficient, performance index and root mean square percentage error are used in comparison of the models. The first two are considered for the success of the models while the last two for the failure. Success criteria are given a grade between 0 and 10, failure criteria receive a grade from −10 to 0. The summation of the grades is taken as the total grade of each model. It is found that regression models and FFBP-NN are superior to GR-NN, RBF-NN and DT in predicting monthly lake water level.
full text available at Springer Link
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Article: Theoretical and Applied Climatology
Structural Characteristics of Annual Precipitation in Lake Urmia Basin
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Babak Vaheddoost, Ph.D. Candidate at Istanbul Technical University, Dep. Civil Engineering, vaheddoostb@itu.edu.tr
Hafzullah Aksoy, Prof. Dr. at Istanbul Technical University, Dep. Civil Engineering, haksoy@itu.edu.tr
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Abstract:
Precipitation as the main process that brings evaporated water from the oceans to the land’s surface is a critical role player in Lake Urmia basin (Iran). As a hyper-saline lake declared as UNESCO’s biosphere reserve in Ramsar Convention, it is dealing with gradual atrophy. In this study, characteristics of annual precipitation in the Lake Urmia basin are investigated by means of several statistical measures and tests. Data in 53 meteorological stations widespread across the basin for a period of 31 years from 1981 to 2011 are considered for analysis. Fundamental statistical characteristics of the data like mean, maximum, minimum, standard deviation, coefficient of variation, coefficient of skewness, coefficient of kurtosis, auto-correlation and cross-correlation coefficients of the annual precipitation are calculated. Entropy in each station is also calculated with respect to the long-run mean precipitation of the basin. Results of the analysis are plotted in contour maps. Several tests for consistency, randomness, trend and best-fit probability distribution function are applied to investigate characteristics of the annual precipitation. Heterogeneity and dependence on local conditions are the main results revealed by this study while consistency and dependency of precipitation on North West and West of the basin are considered as the most effective among other regions. Due to the North-South oriented mountains, a relatively sharp decline in the precipitation from West to East can be compared to the gradual decline in precipitation from North to South due to smooth change in the terrain. It is also seen that such characteristics as probability distribution, consistency, randomness, trend, and uncertainty of annual precipitation in the Lake Urmia basin become more complex as crossing from West to East than crossing from North to South on the basin
full text available at Springer Link
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Conference: Watershed Management Symposium, Environmental and Water Resource Institute (EWRI). August 2015, ASCE Bechtel Conference Center, Reston, USA.
Decision Tree for Measuring the Interaction of Hyper-Saline Lake and Coastal Aquifer in Lake Urmia
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Babak Vaheddoost, Ph.D. Candidate at Istanbul Technical University, Dep. Civil Engineering, vaheddoostb@itu.edu.tr
Hafzullah Aksoy, Prof. Dr. at Istanbul Technical University, Dep. Civil Engineering, haksoy@itu.edu.tr
Hirad Abghari, Assistant. Prof at Urmia University, Dep. Natural Resources, hiradab@gmail.com
Saied Zare, Ph.D. Candidate at Dokuz Eylul University, Dep. Civil Engineering, saieed_zare@yahoo.com
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Abstract:
Lake Urmia is located in the North West of Iran. The hyper saline lake is drying up very fast and more than seventy percent of the water in the lake has vanished in recent years. In this research, the West and South banks of the lake’s basin which is known as the West Azerbaijan province of Iran are studied. During the period from March 2001 to August 2011, six pilot stations for ground water near the lake shore were monitored. Correlation, cross-correlation, distribution, and regression analysis were done for lake and pilot stations. Several decision trees were fitted to the model and the most proper one was selected to test the hypothesis. Results show that the North West of the basin is the most interactive part of the ground water and the fitted decision tree model with randomly selected data is performing well.
Keywords: Decision tree; Ground water; Hyper-saline lake; Lake Urmia
Full text available at ASCE-Library and Perma link.
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Conference: 23rd National and the 1st International conference of Geo-sciences (2014)
Time Series Analysis of
Water Level in Lake Urmia Using ANN and MLR Modeling Techniques.
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Babak Vaheddoost,
Ph.D. Candidate at Istanbul Technical University, Dep. Civil Engineering, vaheddoostb@itu.edu.tr
Saied Zare, Ph.D.
Candidate at Dokuz Eylul University, Dep. Civil Engineering, saieed_zare@yahoo.com
Hirad Abghari,
Assistant. Prof at Urmia University, Dep. Natural Resources, hiradab@gmail.com
Hafzullah Aksoy,
Prof. Dr. at Istanbul Technical University, Dep. Civil Engineering, haksoy@itu.edu.tr
Sevinc Ozkul, Prof.
Dr. at Dokuz Eylul University, Dep.
Civil Engineering, sevinc.ozkul@deu.edu.tr
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Abstract:
Known as a crucial
subject, Lake Urmia is suffering from a slow atrophy. Since the past decade,
almost 70 percent of the lake vanished rapidly but within several frequencies. Likewise,
familiar case Aral is an awaiting scenario that scientists trace for Lake Urmia.
Nowadays, simulation and computational methods are well-known and used for case
monitoring or field investigations of water resources. Artificial neural
network (ANN) as a black box method is a simulation technique which can
estimate the conditions after a strict but adaptive calibration. Using ANN’s
techniques with different activation functions in comparison with some regular
methods like multi linear regression (MLR) is a recommended research plan. What
is more, the nature of MLR helps to make a feedback for the degree of accuracy
in simulations. In this research, a lag time performance method is used on a 44
years monthly data. Therefore, three main ANN’s technique is used for
comparative estimations of the model. Generalized regression network (GRN),
radial based function (RBF) and feed forward back propagation (FFBP) is used. Additionally,
the FFBP method is manipulated throw three different activation functions of
linear-sigmoid (Purelin), tang-sigmoid (Tansig) and logarithmic-sigmoid (Logsig).
Results showed that MLR release the most accurate result with 1.0*10-8
of mean squared error (MSE) with determination coefficient (R2) of
0.9858, while GRN shares the poorest results. Consequently, it can be seen that
all applied method except GRN have acceptable results while MLR is still the
best method because of the highest determination coefficient (R2)
and MSE among the others.
Keywords:
Lake, ANN, MLR, Simulation, Urmia
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Conference: 6th international Perspective on Water Resources & The Environment (2013)
Environmental Crisis in Lake Urmia, Iran: A Systematic Review of
Causes, Negative Consequences and Possible Solutions
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Conference: 6th international Perspective on Water Resources & The Environment (2013)
Environmental Crisis in Lake Urmia, Iran: A Systematic Review of
Causes, Negative Consequences and Possible Solutions
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Vahid Garousi(1,2), Aref Najafi, Azar Samadi, Kabir Rasouli, Behzad Khanaliloo (1,2)
1: Schulich School of Engineering, University of Calgary
2: Lake Urmia Conservation Institute (LUCI), Calgary, Alberta, Canada vgarousi@ucalgary.ca, {vahid, aref, azer, kabir, ehzad@saveurmia.com}
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ABSTRACT:
Lake Urmia, located in NW of Iran, is the third largest salt-water lake on earth. Due to poor water management and construction of 48+ dams, more than 70% of the lake surface areas has already dried up. As a result, the retrieval of the lake shore has left a salt deposit behind and exposed to wind. Studies have predicted that salt storms from the dried lake will have serious impacts on the lives of 76 million people living around the lake. We are undertaking a systematic literature review in this article on a pool of 36 papers carefully selected from the literature, which have studied the Lake Urmia crisis in recent years. The systematic review synthesizes the evidence and insights reported in the existing body of knowledge in this area. This article is aimed to raise awareness and capture the attention of international organizations, NGOs and activists in the international arena and neighbouring countries. We hope that this review article would increase awareness for this major international environmental crisis in making and alert environmental and governmental decision makers in the countries around the lake.
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