Producing climate boundary maps using GIS interface model designed with Python

Climate and its effects need to be examined within a more planned and comprehensive framework to prevent the unfavorable impact of climate change. Thus, climate effects on the ecosystem can be identified by determining the geographical boundaries of different climate types. The Köppen, Trewartha, Thornthwaite, Erinc, Aydeniz, De Martonne, and De Martonne–Gottman methods are used in the classification of climates. These methods enable the regional differences of climate types to be determined and their changes over the years to be examined. A number of studies examining climate classes have produced graphic findings and maps. The absence of new approaches has resulted in climate classifications still being carried out via manual studies. However, a program for identifying and representing these methods in a convenient, fast, and automated way could facilitate the completion of analyses in a shorter time. The programming languages developed in recent years have made it easy to design interface models that can perform analyses faster and easier than prolonged manual methods. In this study, a climate boundary determination interface model, designed using the Python programming language, was developed for use in the ArcGIS 10.6 program to determine geographical climate boundaries automatically. The provinces of Artvin, Ordu, Rize, Trabzon, Giresun, Bayburt, and Samsun (Turkey) were chosen as the study area to test the interface model. The resulting interface model design is expected to: (1) address the dimensions of climate change in Intergovernmental Panel on Climate Change studies; (2) identify the climate changes in our country as an objective of the National Climate Change Strategy; and (3) determine the land-use changes caused by climate boundaries and examine the ownership dimension of the adaptation process in the declaration published by the International Geodesy Federation in 2014.

Iris Localization Algorithm based on Effective Area

Iris localization is the most crucial part of the iris processing because its accuracy can directly affect the accuracy of biometric identification in subsequent steps. Yet, the quality of iris images may be sharply degraded due to interference from eyelashes and reflections during image acquisition, which can affect the localization accuracy adversely. To solve the problem, an iris localization algorithm based on effective area is proposed. First, YOLOv4 is used to crop the image to obtain the effective iris area, which is beneficial in improving the accuracy of subsequent localization. Furthermore, a method to remove reflective noise is proposed, which can effectively avoid the problem of noise interference in the process of inner boundary determination. Finally, aiming at the edge deviation caused by eyelashes, an outer boundary adjustment method is proposed. The experimental results show that the proposed method achieves good performance in the localization of iris images of both good quality and noise interference and outperforms other state-of-the-art methods.

Interaction Boundary Determination of Renewable Energy Sources to Estimate System Strength Using the Power Flow Tracing Strategy

System strength is an important concept in the integration of renewable energy sources (RESs). However, evaluating system strength is becoming more ambiguous due to the interaction of RESs. This paper proposes a novel scheme to define the actual interaction boundaries of RESs using the power flow tracing strategy. Based on the proposed method, the interaction boundaries of RESs were identified at the southwest side of Korea Electric Power Corporation (KEPCO) systems. The test results show that the proposed approach always provides the identical interaction boundaries of RESs in KEPCO systems, compared to the Electric Reliability Council of Texas (ERCOT) method. The consistent boundaries could be a guideline for power-system planners to assess more accurate system strength, considering the actual interactions of the RESs.