Search

Background and Objectives Daily precipitation, which is completely stochastic, is one of the basic components of the water cycle and has an important role in the management of surface and ground water resources in terms of quantity and quality. Indispensable element of drought analysis and flood control research is precipitation. The efficient management of surface water resources directly depends on precipitation. The lack of long-term and reliable data has made it difficult to determine precipitation behavior. In this study, we tried to determine the behavior and pattern of precipitation through human-data interaction called visual data mining approach. One of the new approaches that focuses on the use of visualization and graphics in the analysis of complexities in data is visual data mining. Visual data mining can be thought of as a combination of two disciplines, visualization and data mining. Visual data mining is also closely related to computer graphics, multimedia systems, humancomputer interaction, pattern recognition, and high-performance computing. The aim of this study is to analyze Tabriz daily precipitation data and discover the patterns in this data with the help of visual data mining approaches. Discovering these patterns and identifying rainfall behavior will help to manage floods on the one hand and droughts on the other. Methodology In this study, daily precipitation and temperature data of Tabriz Synoptic Station for the last seventy one years (1951-2021) were used to determine precipitation patterns. Tabriz has a generally cold climate as the center of the eastern Azerbaijan province and is surrounded by mountains. Recently, with the increase in data and software, data mining techniques have also started to attract attention. Data alone cannot mean anything. However, graphs consisting of data can give very meaningful information and messages. Visual data mining approach is an effort to bring data to life with different graphics. In this research, various softwares such as R, ArcGIS and Tableau were used for visualization. In addition, ExcelStat was used to check the accuracy of the data and for statistical tests. R statistical language was used to create the data mining structure and to display the data graphically. Then, different diagrams were drawn using the Tableau program. Finally, the drawn diagrams were evaluated and the final graphics were selected. ArcGIS software was used for spatial analysis and map drawing. Also, multiple linear regression method was used to predict precipitation amount and probability of occurrence. Findings According to the temperature histogram, the long-term average annual temperature in Tabriz varies between 12 and 13 degrees Celsius. Also, according to the precipitation histogram, precipitation over 5 mm in Tabriz varies between 10 and 20 days per year on average. The results obtained showed that there has been a change in precipitation patterns in the last 5 years (2017-2021). Although annual precipitation during these five years is above the 71-year average, it is still below the golden precipitation period (1961 to 1970). The results showed that the intensity of spring rains decreased significantly in Tabriz during the period 2006-2021. However, from 1971 to 1980 and from 2001 to 2010, it was observed that the spring rains were more in terms of both precipitation values and precipitation intensity. However, in the following periods from 2011 to 2021, both precipitation intensity and precipitation values decreased. The results showed that most of the spring precipitation in 1981 was 276.3 mm, making up 73.26% of the total precipitation for that year. According to the findings, precipitation has started to decrease in the spring season in recent periods. Decreased dry grain yield in the Azerbaijan region may be affected by decreased spring precipitation. As a result of this decrease, it is expected that the agricultural economy in the study area will be negatively affected. Conclusion In this study, as a first, daily precipitation in Tabriz was investigated with visualized data mining techniques. Thus, interesting findings were obtained with the help of different graphics. The results showed an increase in precipitation in the last 5-6 years. It has also been proven that the temperature behavior during this period shows an increase in average temperature, a confirmation of the increase in global temperature. Although the results of this research showed that visualized data mining is successful in precipitation analysis, it is recommended to conduct more comprehensive studies in this field in the future. [ABSTRACT FROM AUTHOR]

Optimum irrigation reservoir operation is a significant issue for water management decision makers. In this study, we used 4 different datasets of monthly amounts of water to be released from the Eleviyan irrigation reservoir in Iran as inputs in a data mining model; “if–conditional” operating rules were determined as outputs. Operating rules derived from data mining and rules obtained from an optimization model were in high concordance, especially between the 2 datasets from the preconstruction period, with differences in only 12 of 252 instances (252 months) used to compare the 2 methods. Operating rules determined for the preconstruction period using the data mining method were also consistent with optimum operating rules determined either by optimization or Monte Carlo simulation. Thus, we concluded that the decision tree subtechnique of data mining is an appropriate method for determining meaningful operating rules for the reservoir. [Supplemental materials are available for this article. Go to the publisher's online edition ofLake and Reservoir Managementto view the supplemental file.] [ABSTRACT FROM PUBLISHER]