چكيده به لاتين
Rivers are vital components of our ecosystem, providing essential resources and supporting diverse aquatic life. Understanding and maintaining the environmental health of river networks is of great importance. This research examines the comprehensive field of environmental monitoring in river networks with primary focus on Haraz River as a case study. The overall goal is to design an optimal monitoring network that can effectively assess the environmental status and water resources of the river, while minimizing the number of required monitoring points. One of the key innovations of this study is the consideration of the placement of monitoring stations in areas characterized by adverse water quality conditions and exposure to significant environmental threats. To achieve this goal, a multi-level approach was employed that combined information with different characteristics and at several levels. The criteria examined in this study included vital indicators of water quality, including chemical oxygen demand (CBOD), nitrate, phosphate, and dissolved oxygen. In addition, the study included an assessment of salmon habitats along the river and classified river quality into five classes: very good, good, moderate, bad and very bad To effectively integrate data and information sources, this research used several techniques rooted in Dumpster-Shafer theory. These techniques included deterministic numbers, ordinary fuzzy numbers and interval fuzzy numbers. In addition, this study used the fuzzy evidence reasoning technique to synthesize the data. In order to calculate the belief functions, fuzzy membership functions and interval fuzzy numbers, the simulation results of the calibrated QUAL2Kw models of the Haraz River in the warm and cold seasons were applied. Monthly simulation models of QUAL2Kw in the Haraz River according to various meteorological and hydrological conditions were used in defining the belief functions, fuzzy membership functions and interval fuzzy numbers of various approaches. This research presents a multi-level information diffusion approach for spatio-temporal environmental monitoring in river networks in the Haraz River as a case study. Based on the information extracted from the statistical analysis and mass functions in the Dumpster-Shafer technique, the values of belief and desirability of the water quality in the candidate monitoring points in the Haraz River were determined into each of the very good to very bad classes. Considering the number 0.1 as the threshold in the "very bad" class, resulted in selection of points located at 30, 70, 80, 100, 110, 120 and 130 km as the monitoring stations. In the fuzzy Dumpster-Shafer approach, considering the number 0.1 as the threshold in the "very bad" class, resulted in selection of points located at 30, 90, 100, 110, 120, and 130 km as monitoring stations. In this technique, in the selected monitoring points, the belief of belonging to the "very bad" class was much higher than the "very good" and "good" classes. In the interval fuzzy Dumpster-Shafer approach, considering the belief functions as the interval fuzzy numbers and considering the numerical criterion of 0.1, resulted in selection of all candidate monitoring points except for km 70. In the evidential reasoning approach in the extraction and analysis of interval belief, the stream head-flow has the most favorable water quality and the point located at km 120 has the most unfavorable status These findings not only provide valuable insights into the environmental health of the Haraz River, but also provide a strong framework for future river network monitoring efforts. This research emphasizes the importance of comprehensive and innovative monitoring strategies in protecting our valuable river ecosystems.
