Monitoring soil salinity via remote sensing technology under data scarce conditions: A case study from Turkey

Modeling lake level fluctuation is very essential for planning and design of hydraulic structures along the lake coasts. In this study, namely two different adaptive-neuro-fuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), and gene expression programming (GEP) were applied to forecast 1-, 2- and 3-month ahead lake-level fluctuations of Manyas and Tuz, Turkey. Comparison of the models indicated that the optimal ANFIS-GP models performed better than the optimal ANFIS-SC and GEP models in forecasting 1- and 3-month ahead lake levels while the ANFIS-SC model showed better accuracy than the other models in 2-month ahead forecasting. The ANFIS-GP model comprising lake level values of current and one previous months successfully forecasted 1-month ahead lake level with root mean square error (RMSE) of 0.251 and coefficient of determination (R2) of 0.872. For the Tuz Lake, the optimal ANFIS-SC models were found to be better than the optimal ANFIS-GP and GEP models for forecasting 1- and 2-month ahead lake levels while the GEP model performed better than the other models in and 3-month ahead lake level forecasting. The ANFIS-SC model comprising lake level values of current and three previous months successfully forecasted 1-month ahead lake level with RMSE of 0.120 and R2 of 0.724. Based on the comparisons, it was found that the GEP, ANFIS-GP and ANFIS-SC models could be successfully employed in forecasting lake level fluctuations.

Comparison of Different Data-Driven Approaches for Modeling Lake Level Fluctuations: The Case of Manyas and Tuz Lakes (Turkey)

This study, it was aimed to be enabling to use of fish scale as a bio -indicator for heavy metal pollution that is gradually increase in natural waters. For this purpose scaled carps , Cyprinus carpio (Linnaeus, 1758) were exposed to 7.5, 15, 30 and 60 µg L -1 concentrations of trivalent chromium for 21 day and deformities appearing on the scales were examined under electron microscopy. It was found that scale deformations in scales were more in focus region and increased with increasing chromium concentration in water. Radius, circulars and lepidonts on scale damaged at various rates depending on chromium concentrations. In addition, more damage was observed in anterior region as compared with in posterior region of the scales. This study showed that scales were very affected from chromium accumulation. For these reasons scales have an important role as a bio -indicator to determine the pollution parameters, especially for heavy metal pollution.

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Effect of chromium on scale morphology in scaly carp (cyprinus carpio l.)

In this paper, we introduce a new approach, based on a unified framework incorporating Data Envelopment Analysis (DEA) and Ordered Weighted Averaging (OWA), for assessing water quality in contextual settings that involve a large number of hydrochemical parameters In order to enhance discrimination among water sources, the DEA model is adopted with data-driven input variables, called “surrogate optimistic closeness values,” computed through an aggregation procedure that includes the observed values of the hydrochemical parameters with OWA weights The proposed DEA-OWA methodology has been employed to assess the quality of 51 water samples, collected from irrigation wells in Sereflikochisar Basin, Turkey, by means of 19 hydrochemical parameters Using different subjectivity levels, the Surrogate Water Quality Indices (SWQIs) that are produced are proven effective in enhancing discrimination among the water sources while enabling a more robust water quality-based ranking The k-means analysis has been used for clustering the water quality of the wells into Excellent, Good, Permissible, and Unsuitable rather than using pre-set boundaries Only one water source has been identified as Excellent, whereas 1765%, 4510%, and 3529% of the sampled wells, respectively, are categorized with Good, Permissible, and Unsuitable water quality Inferred from wells’ location, the results suggest that the groundwater might be drastically affected by saline water intrusion from Lake Tuz The latter conclusion has been corroborated through a Tobit regression analysis

A Surrogate Water Quality Index to assess groundwater using a unified DEA-OWA framework

Soil management is an essential concern in protecting soil properties, in enhancing appropriate soil quality for plant growth and agricultural productivity, and in preventing soil erosion. Soil scientists and decision makers require accurate and well-distributed spatially continuous soil data across a region for risk assessment and for effectively monitoring and managing soils. Recently, spatial interpolation approaches have been utilized in various disciplines including soil sciences for analysing, predicting and mapping distribution and surface modelling of environmental factors such as soil properties. The study area selected in this research is Tuz Lake Basin in Turkey bearing ecological and economic importance. Fertile soil plays a significant role in agricultural activities, which is one of the main industries having great impact on economy of the region. Loss of trees and bushes due to intense agricultural activities in some parts of the basin lead to soil erosion. Besides, soil salinization due to both human-induced activities and natural factors has exacerbated its condition regarding agricultural land development. This study aims to compare capability of Local Polynomial Interpolation (LPI) and Radial Basis Functions (RBF) as two interpolation methods for mapping spatial pattern of soil properties including organic matter, phosphorus, lime and boron. Both LPI and RBF methods demonstrated promising results for predicting lime, organic matter, phosphorous and boron. Soil samples collected in the field were used for interpolation analysis in which approximately 80% of data was used for interpolation modelling whereas the remaining for validation of the predicted results. Relationship between validation points and their corresponding estimated values in the same location is examined by conducting linear regression analysis. Eight prediction maps generated from two different interpolation methods for soil organic matter, phosphorus, lime and boron parameters were examined based on R2 and RMSE values. The outcomes indicate that RBF performance in predicting lime, organic matter and boron put forth better results than LPI. However, LPI shows better results for predicting phosphorus.

https://iopscience.iop.org/article/10.1088/1755-1315/95/6/062010/pdf

Interpolation Approaches for Characterizing Spatial Variability of Soil Properties in Tuz Lake Basin of Turkey

Tuz Lake is second biggest lake as a surface area in the Turkey. Tuz Lake has an important salt production potential for Turkey and occupies a wide area in the central part of Anatolia. The Tuz Lake basin occupies about 1,500 km2, and located between several cities and towns (Konya, Aksaray, Cihanbeyli, Kulu, Sereflikochisar). The lake altitude is 905 m and distances maximum 80 and 50 km, North to South and East to West respectively. About 3 billion population are living around Lake. It has small catchment basin sources. Environmental problems have increased on a World basis during the last century. Water pollution is one of the important problems discharged form the neighbour cities and towns. So that, salt production form the lake has some difficulties due to industrial pollutants. An other and main problem of the lake decreasing water level that it is seen third biggest lake after Beysehir lake. It seen to be dry in 10to 20 years period. The aim of this study, investigation of environmental problems of Tuz Lake and to improve some suggestions on these problems.

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H. Nagehan Ucan

Papers

Environmental Problems of Tuz Lake (Konya-Turkey) #