İznik Gölü Havzası’nda Doğal Ortam Koşullarının Havza Yönetimi ve Planlamasına Etkisi

In the basins where natural and human environmental conditions are in intense interaction, management studies are carried out with various models in many contexts. In this study, it is aimed to create a watershed management model based on spatial auto correlation and clustering analysis over different variables of the Göksu River Basin, which has geographical diversity, interactions and potential risks. In the study, in which Geographical Information Systems (GIS) was used effectively, a multi-step system was created with deterministic, quantitative, correlation and distribution analysis methods. Four main variables (sub-models) were produced by analyzing many parameters in order to reveal all the geographical elements, interactions, natural dynamic functioning of the basin and to establish the cluster distribution as relation. The main variables consist of the geomorphological suitability-availability model, the precipitation runoff model, the multi-risk model, and the land use model. Each model was subjected to spatial correlation and cluster analysis distribution data of the basin were produced. The data, in which 5 different clusters were identified, were also analyzed in terms of problem-risk potential and sustainable-appropriate use potential. Then, the distribution data was tested for significance and clustering with the Local Moran's I-Anselin test and the Getis-Ord GI statistic. From the analyzes, it has been determined that the areas forming the high frame of the basin show a cluster with sustainable-appropriate use potential, and there is a cluster with high problem-risk potential in İnegöl Plain, north of Yenişehir and Göksu Valley. In order to ensure sustainability in the basin, it is necessary to protect ecological areas, increase flood, overflow, erosion, landslide measures, control water quality in streams and direct anthropogenic pressure concentration to more suitable areas.

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A Case Study of Using Artificial Neural Networks to Predict Heavy Metal Pollution in Lake Iznik

Mert,

Kasapoğulları

2023

Preprint

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Since high levels of heavy metals cause serious complications for water resources, plants, animals and human health, determining their presence and concentration is very important for the sustainability of the ecosystem. In recent years, rapid advances in the field of artificial neural networks (ANNs) brought them the forefront in water quality prediction. In this paper, various experiments were conducted with a model for predicting the presence of heavy metals using IBM SPSS statistics 23 software. In order to assess the water quality of Lake Iznik –an important source of water– in terms of heavy metals, water quality parameters of samples taken in the period 2015–2021 from five different water sources flowing into the lake were analyzed. A number of psychochemical were measured in samples taken from Karasu, Kırandere, Olukdere, and Sölöz streams flowing into the lake, and were used as input data for modeling, while fifteen heavy metal concentrations in Karsak stream flowing out of the lake were used as output data of the model. The analyses showed that the R2 coefficients for heavy metals were mostly close to 1. Considering the importance of the independent variable in heavy metal pollution prediction, the most effective parameters for streams stood out to be conductivity, COD, COD, and temperature, respectively. It was seen that ANN model is a good prediction tool method that can be used effectively to determine heavy metal pollution in the lake in terms of ecological sustainability in order to conservation the water quality of Lake Iznik and to eliminate the existing pollution.

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İznik Gölü Havzası’nda Doğal Ortam Koşullarının Havza Yönetimi ve Planlamasına Etkisi

Cited by 4 publications

References 13 publications

The Importance of Watershed Management in Sustainable Development

The Importance of Watershed Management in Sustainable Development

Mekânsal otokorelasyon ve kümeleme analizi yaklaşımı ile Göksu Çayı Havzası’nın (Sakarya Nehri Havzası) bütünleşik ve sürdürülebilir havza yönetim modeli

A Case Study of Using Artificial Neural Networks to Predict Heavy Metal Pollution in Lake Iznik

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