Selection of renewable energy systems sites using the MaxEnt model in the Eastern Mediterranean region in Turkey

Determination of forest fire risk with respect to Marchalina hellenica potential distribution to protect pine honey production sites in Turkey

Turkey is the leading producer of pine honey worldwide, accounting for 90% of global production, largely due to the presence of Marchalina hellenica populations. However, in recent years, devastating forest fires have caused substantial damage to Pinus brutia forests and M. hellenica populations, leading to a dramatic decline in pine honey production areas. The urgency for early intervention procedures against forest fires and relocation of M. hellenica populations to other P. brutia forests has become apparent. A comprehensive assessment of 25 criteria was conducted to investigate the thresholds and behaviors of each criterion, which play a vital role in the distribution of M. hellenica, using the maximum entropy model (MaxEnt). To evaluate the impact of forest fires on the distribution of M. hellenica, the potential locations of pine honey production areas were determined for 2022. Furthermore, the susceptibility of forest fires was modeled for all pine honey production months. The findings revealed that forest fires have destroyed 384.9 km2 (12.8% of the total pine honey production area), predominantly in August and September, with the most severe damage in Marmaris (156 km2) and significant impacts in Ula, Köyceğiz, and Milas. The analysis facilitates the estimation of new areas suitable for M. hellenica and pine honey production while providing valuable insights into strategies for mitigating forest fires and formulating proactive protection protocols.

Land Suitability Assessment for Pistachio Cultivation Using GIS and Multi-Criteria Decision-Making: A Case Study of Mardin, Turkey

Site selection for pistachio orchards is an important issue for sustainable agricultural policies, crop productivity, agricultural planning, and communities. This study aims to investigate suitable places for pistachio in the Mardin Province (SE Turkey) by considering several variables, such as meteorological data, topographic conditions, economic factors, and soil characteristics, using Geographic Information System (GIS) and Multi-Criteria Decision Analysis. Pistachio farmers, expert opinions, and literature data were used to determine the requirements for pistachio cultivation. Four main assessment criteria (thirteen sub-criteria), sixty value ranges, and fourteen exclusion criteria were determined for the pistachio land suitability assessment. The weighting of the evaluation criteria was calculated using the Analytical Hierarchy Process (AHP). Farmers and experts have stated that meteorological factors are more important than soil, topography, and economic factors. All data were transferred to the GIS environment, and a land suitability map was created using the weighted linear combination method. The results show that Mardin province has very suitable lands for pistachio cultivation. The resulting map determined that the 228,891.59 ha area in Mardin province is very suitable for pistachio. To evaluate the accuracy of the land suitability map generated for pistachio, the Receiver Operating Characteristic (ROC) curve was used. The value of the area under the curve (AUC) was calculated to be 0.806, which indicates that the study is consistent. The created suitability map will be an essential data source for developing sustainable agricultural strategies in the Southeastern Anatolia region.

Identifying the potential soil pollution areas derived from the metal mining industry in China using MaxEnt with mine reserve scales (MaxEnt_MRS)

Identifying the potential soil pollution areas derived from the metal mining industry usually requires extensive field investigation and laboratory analysis. Moreover, the previous studies mainly focused on a single or a few mining areas, and thus couldn't provide effective spatial decision support for controlling soil pollution derived from the metal mining industry at the national scale. This study first conducted a literature investigation and web crawler for the relevant information on the metal mining areas in China. Next, MaxEnt with mine reserve scales (MaxEnt_MRS) was proposed for spatially predicting the probabilities of soil pollution derived from the metal mining industry in China. Then, MaxEnt_MRS was compared with the basic MaxEnt. Last, the potential soil pollution areas were identified based on the pollution probabilities, and the relationships between the soil pollution probabilities and the main environmental factors were quantitatively assessed. The results showed that: (i) MaxEnt_MRS (AUC = 0.822) obtained a better prediction effect than the basic MaxEnt (AUC = 0.807); (ii) the areas with the soil pollution probabilities higher than 54% were mainly scattered in the eastern, south-western, and south-central parts of China; (iii) GDP (45.7%), population density (30.1%), soil types (15.5%), average annual precipitation (3.9%), and land-use types (3.1%) contributed the most to the prediction of the soil pollution probabilities; and (iv) the soil pollution probabilities in the areas with all the following conditions were higher than 54%: GDP, 7600-2612670 thousand yuan/km²; population density, 152-551 people/km²; precipitation, 924-2869 mm/year; soil types, Ferralisols or Luvisols; and land-use types, townland, mines, and industrial areas. The above-mentioned results provided effective spatial decision support for controlling soil pollution derived from the metal mining industry at the national scale.

Standardizing the factors used in wind farm site suitability models: A review

As global wind energy capacity continues to expand, the need to site commercial wind farms in productive, affordable, and technically feasible locations has become increasingly important. The use of wind farm site suitability models to identify these locations has grown consequently, thus increasing interest in standardizing certain aspects of these models' development. This systematic review of wind farm site suitability studies seeks to identify similarities and differences in the selection and representation of their enlisted siting factors. The review focuses on how subjective modeling decisions, such as vocabulary choices and dataset selection, occur in the literature, based on five identified themes: 1) Deciding Upon Siting Factors, which explains how a study's geographical context, selected modeling approach, and modeler decisions can influence siting factor selection; 2) Classifying Data and Siting Factor Terminology, which addresses the extent and the advantages of consistent siting factor vocabulary; 3) Implementing Siting Factors as Constraints or as Evaluation Criteria, which covers the importance of consistent implementation and of specifying logic when enlisting siting factors to assess potential wind farm sites; 4) Utilizing Primary and Secondary Data, which details how a study's reliance on external or self-collected datasets influences siting factor representation; and 5) Data Source and Accessibility, which highlights the inconsistent provision of citations and dataset sources, and the availability of datasets for siting factors to the broader scientific community. Standardizing the selection and representation of siting factors would benefit comparisons between wind farm site suitability studies and communication of model outputs to a wider audience.

Flood susceptibility mapping by best-worst and logistic regression methods in Mersin, Turkey

Flood disasters resulting from excessive water in stream beds inflict extensive damage. Floods are caused by the expansion of cities, the erosion of riverbeds, inadequate infrastructure, and increasing precipitation due to climate change. Floods cause great damage to agricultural areas and settlements. Regions that may be affected by floods should be identified, and precautions should be taken in these areas to prevent these damages. Flood susceptibility maps are produced for this reason. The purpose of this study was to construct a flood susceptibility map so that susceptible locations in Mersin may be identified. Firstly, 429 flood events were identified for the flood inventory map. Twelve conditioning factors, namely elevation, slope, distance to river, distance to drainage, drainage density, soil permeability, precipitation, land cover/land use, stream power index (SPI), topographic wetness index (TWI), aspect, and curvature were used to create flood susceptibility maps, applying logistic regression and best-worst methods. The flood inventory data were used to prepare susceptibility maps and test their consistency. The receiver operating characteristic (ROC) curve was used for consistency analysis. In logistic regression, 86% of floods were located within 20% of the study area that was categorized as high and very high susceptibility. According to the value of the area under the ROC curve (AUC), logistic regression had a 0.901 value. Land use, soil permeability, and elevation were the most important factors in the logistic regression method. In the best-worst method, 85% of floods were located within the 14% of the study area categorized as high and very high susceptibility. According to the AUC value, the best-worst method had a 0.898 value. Elevation, distance to river, and precipitation factors had the highest coefficient value in the best-worst method. Based on the AUC values, the flood susceptibility maps had a high prediction capacity.

The combination of fuzzy analytical hierarchical process and maximum entropy methods for the selection of wind farm location

Wind energy is one of the important renewable energy alternatives due to its wide potential and meeting increasing energy demand. However, location selection in wind farms is a complex spatial decision process for decision-makers. This study aimed to determine suitable wind farm locations by combining Fuzzy-Analytical Hierarchical Process (F-AHP) and Maximum Entropy (MaxEnt) methods for Hatay Province, Turkey. Firstly, nine decision criteria for selecting suitable wind farm locations were determined by climate, environmental, social and economic factors. Secondly, the F-AHP and MaxEnt models were implemented and suitable areas were mapped according to five suitability classes. Finally, F-AHP and MaxEnt model results were combined to define and classify priority locations for the wind farm. Study results show that wind speed, air densities and elevation are important criteria for F-AHP, while wind speed, wind power density and distance from power criteria are the most important factors for MaxEnt. Very high and high suitable wind farm locations of Hatay Province cover 21.6% in F-AHP and 29.8% in the MaxEnt model, while very low and low suitable areas cover 48.1% of the study area in both model results. To determine the priority wind farm location, F-AHP and MaxEnt model results were overlapped and reclassified according to the combination of suitability classes. The priority classes show that 62.9% of the study area is unsuitable for the wind farm. However, the limited area was determined as the 1st-priority area (3.2%), 2nd-priority area (4.9%) and 3rd-priority area (6.2%) to locate the wind farm. Consequently, the study methodology enables a more precise evaluation by combining different model results for decision-makers to select the optimum wind farm location selection.

Identifying potentially contaminated areas with MaxEnt model for petrochemical industry in China

The presence of heavy metal and organic pollutants in wastewater effluents, flue gases, and even solid wastes from petrochemical industries renders improper discharges liable to posing threats to the ecological environment and human health. It is beneficial for pollution control to find out the regional distribution of contaminated sites. This study explored the relationship between the petrochemical contaminated areas and natural, socio-economic, and traffic factors. Ten indicators were selected as input variables, and the MaxEnt model was conducted to identify the potentially contaminated areas. Moreover, among these 10 variables, the factors that have the great impact on the results were determined according to the contribution of variables. The results showed that the MaxEnt model performed well with AUC of 0.981 ± 0.004, and 90% of the measured contaminated sites was located in areas with medium and high probability of contamination in the prediction results. The map of potentially contaminated areas indicated that the areas with high probability of contamination were distributed in Yangtze River Delta, Beijing, Tianjin, southern Guangdong, Fujian coastal areas, central Hubei and northeast Hunan, central Sichuan, and southwest Chongqing. The responses of variables presented that high probability of petrochemical contamination tended to appear in cities with developed economy, dense population, and convenient transportation. This study presents a novel way to identify the potentially contaminated areas for petrochemical sites and provides a theoretical basis to formulate future management strategies.

The use of regression tree method for Sentinel-2 satellite data to mapping percent tree cover in different forest types

Quantifying forest systems is of importance for ecological services and economic benefits in ecosystem models. This study aims to map the percent tree cover (PTC) of various forest stands in the Buyuk Menderes Basin, located in the western part of Turkey with different characteristics in the Mediterranean and Terrestrial transition regions Sentinel-2 data with 10-m spatial resolution. In recent years, some researches have been carried out in different fields to show the capabilities and potential of Sentinel-2 satellite sensors. However, the limited number of PTC researches conducted with Sentinel-2 images reveals the importance of this study. This study aimed to demonstrate reliable PTC data in landscape planning or ecosystem modeling by introducing an advanced approach with high spatial, spectral, and temporal resolution and more cost-effective. In this study, a regression tree algorithm, one of the popular machine learning techniques for ecological modeling, was used to estimate the tree cover's dependent variable based on high-resolution monthly metrics' spectral signatures. Six frames of TripleSat images were used as training data in the regression tree. Monthly Sentinel-2 bands and produced metrics including NDVI, LAI, fCOVER, MSAVI2, and MCARI were almost the first time used as predictor variables. Stepwise linear regression (SLR) was applied to select these predictor bands in the regression tree and a correlation coefficient of 0.83 was obtained. Result PTC maps were produced and the results were evaluated based on coniferous and broadleaf. The results were tested using high spatial resolution TripleSat images and higher model accuracy was determined in both forest types. The high correlation is due to the Sentinel 2 satellite's band characteristics and the metrics are directly related to the tree cover. As a result, the high-accuracy availability of the Sentinel2 satellite is seen to map the PTC on a regional scale, including complex forest types between the Mediterranean and terrestrial transition climates.

A GIS-based multi-criteria decision-making method for the selection of potential municipal solid waste disposal sites in Mersin, Turkey

Due to rapid urbanization and the resulting rapid population increases, an important problem for cities today is the elimination of solid waste or finding suitable places for waste storage. Municipal solid waste disposal (MSWD) site selection is one of the most important steps in urban waste management. Many criteria political, economic, social, and technological should be considered in this process. Geographic information systems (GIS) and multi-criteria decision-making (MCDM) are tools that are superior to traditional methods in the planning phase of site selection studies. In this study, suitable MSWD sites were determined in Mersin (a Turkish province) based on GIS and the analytic hierarchy process, an MCDM method. Unsuitable areas in the study were removed at the beginning of the analysis. Eleven evaluation criteria were selected: elevation, slope, permeability, distance from lineaments, groundwater level, distance from rivers and water surfaces, distance from roads, distance from settlements, distance from protected areas, and land cover. Considering the evaluation and exclusion criteria, 19.12% of the study area was deemed suitable, and 80.88% was determined unsuitable for an MSWD site. An MSWD suitability map was created as a result of the study. The outcomes indicate that 80,377 ha and 83,022 ha of the study area were classified as high and very high suitability, respectively. Based on these results, we evaluate whether the locations of existing solid waste landfills are appropriate and propose alternative solid waste landfills for each district.