Land readjustment for minimizing public expenditures on school lands: a case study of Turkey

Education infrastructure issues have been one of the most important topics in developing countries that have difficulty reserving money for education infrastructure needs, especially Turkey. Development plans have a crucial role in meeting school infrastructure needs in Turkey. Generally, the expropriation method has been used for land transitions from private to public ownership. However, scarcity of monetary resources for such land transitions and legal/technical deficiencies delays the process. The main objectives of this paper are to respond to the need of the education sector by using land management tools and to identify the most suitable land management tool for allocating basic and primary school lands free of charge. Current trends, education indicators, management responsibility and financial resources, statistical and economical information, education infrastructure needs, and qualitative-quantitative characteristics of the pupils in the Turkish education system are analyzed based on national and international reports. Then, the property right problems experienced in the process of allocating school lands are defined. Methods of transferring school lands to public ownership are examined, and the benefit-cost relationships are revealed. Land readjustment (LR) would make an important contribution to planning decisions and the implementation stage in the sustainable management of school lands.     

A new approach to minimize the loss of life after natural hazards by using Android operating system

Nowadays, the usage rates of smartphones are increasing rapidly. With the versatility of its features, smartphones have succeeded in attracting users. Performing this study has also affected the usage rate of smartphones every day. It is possible to determine the position with GPS (Global Positioning System) technology which is located in smartphones. In this study, smartphone location notification was used to detect the locations of the people who were under debris after the earthquake by means of the phones on them. People who cannot be reached for any reason will be immediately identified, and emergency interventions will be possible. Thus, the survival rate of the injured will be high with early intervention. In this study, it was aimed to minimize the loss of life after the earthquake and all the negativities that would be experienced in society due to this loss. The developed application has been tested in the external world, and the obtained data are given in results section.

     

Effects of reverse geocoding on OpenStreetMap tag quality assessment

OpenStreetMap (OSM), a widely-used open-source geographic information system platform, provides a vast geographic dataset in which users contribute both geometric information (nodes, ways, and relations) and semantic information (tags). This method of voluntary contributions is governed by the collective effort of the users. It is widely acknowledged that the quantity of tag information is substantial, but its quality is often poor. Researchers are therefore trying to assess the quality of the tags and enhance the data through various integration experiments. This article investigates the validity of the tags for geographical objects in metropolitan areas using municipal data and a reverse geocoding technique. The proposed method evaluates the data quality and the matching process carried out by reverse geocoding, using municipal points of interest as a reference. The accuracy of the tag and address information and road network centrality metrics were assessed for the OSM objects that were matched to the locations of interest. The tags were found to match the points of interest with an accuracy of 88%. Furthermore, the tag values were categorized and analyzed based on their similarity. It is concluded that in metropolitan settings where centers of interest are closely located, the accuracy of tags and addresses tends to decrease.     

A physically constrained wavelet-aided statistical model for multi-decadal groundwater dynamics predictions

A physically constrained wavelet-aided statistical model (PCWASM) is presented to analyse and predict monthly groundwater dynamics on multi-decadal or longer time scales. The approach retains the simplicity of regression modelling but is constrained by temporal scales of processes responsible for groundwater level variation, including aquifer recharge and pumping. The methodology integrates statistical correlations enhanced with wavelet analysis into established principles of groundwater hydraulics including convolution, superposition and the Cooper–Jacob solution. The systematic approach includes (1) identification of hydrologic trends and correlations using cross-correlation and multi-time scale wavelet analyses; (2) integrating temperature-based evapotranspiration and groundwater pumping stresses and (3) assessing model prediction performances using fixed-block k-fold cross-validation and split calibration-validation methods. The approach is applied at three hydrogeologicaly distinct sites in North Florida in the United States using over 40 years of monthly groundwater levels. The systematic approach identifies two patterns of cross-correlations between groundwater levels and historical rainfall, indicating low-frequency variabilities are critical for long-term predictions. The models performed well for predicting monthly groundwater levels from 7 to 22 years with less than 2.1 ft (0.7 m) errors. Further evaluation by the moving-block bootstrap regression indicates the PCWASM can be a reliable tool for long-term groundwater level predictions. This study provides a parsimonious approach to predict multi-decadal groundwater dynamics with the ability to discern impacts of pumping and climate change on aquifer levels. The PCWASM is computationally efficient and can be implemented using publicly available datasets. Thus, it should provide a versatile tool for managers and researchers for predicting multi-decadal monthly groundwater levels under changing climatic and pumping impacts over a long time period.     

Hindcasting multidecadal predevelopment groundwater levels in the Floridan aquifer

Establishing predevelopment benchmark groundwater conditions is challenging without long-term records to discern impacts of pumping and climate change on aquifer levels. Understanding periodic natural cycles and trends require 100 years or more data which rarely exist. Using limited records, we develop an approach to hindcast multidecadal levels and examine the temporal evolution of climatic and pumping impacts. The methodology includes a wavelet-aided statistical model, constrained by temporal scales of physical processes responsible for groundwater level variation, including rainfall, evapotranspiration and pumping stresses. The model and hindcasts are tested at three sites in Florida using traditional split calibration-verification methods for the period of record and with the documented historical drought and wet years for the period of no-record. The pumping impact is quantified over time and compared with regional groundwater models, revealing that withdrawals are responsible for 30 to 70% of the declines in levels since 1960s. Hindcasting yielding 110 years of monthly levels is used to assess the effect of climate change and pumping on the frequency of critical low levels. At all three sites, the frequencies of critical low levels increase significantly in the 1960 to 2015 period when compared to the 1904 to 1959 period. For example, at site 1, the return period of the critical low level is shortened by 3.9 years due to climate change and 2.2 years due to pumping.     

Frequency based imputation of precipitation

Changing climate and precipitation patterns make the estimation of precipitation, which exhibits two-dimensional and sometimes chaotic behavior, more challenging. In recent decades, numerous data-driven methods have been developed and applied to estimate precipitation; however, these methods suffer from the use of one-dimensional approaches, lack generality, require the use of neighboring stations and have low sensitivity. This paper aims to implement the first generally applicable, highly sensitive two-dimensional data-driven model of precipitation. This model, named frequency based imputation (FBI), relies on non-continuous monthly precipitation time series data. It requires no determination of input parameters and no data preprocessing, and it provides multiple estimations (from the most to the least probable) of each missing data unit utilizing the series itself. A total of 34,330 monthly total precipitation observations from 70 stations in 21 basins within Turkey were used to assess the success of the method by removing and estimating observation series in annual increments. Comparisons with the expectation maximization and multiple linear regression models illustrate that the FBI method is superior in its estimation of monthly precipitation. This paper also provides a link to the software code for the FBI method.     

Assessment of occupational risks from human health and environmental perspectives: a new integrated approach and its application using fuzzy BWM and fuzzy MAIRCA

Stochastic Environmental Research and Risk Assessment - Occupational safety issues encountered in the worksite environment are the issues that companies should consider in improving their...