Landslide susceptibility mapping for a landslide-prone area (Findikli,NE of Turkey) by likelihood-frequency ratio and weighted linear combination models

Landslides are very common natural problems in the Black Sea Region of Turkey due to the steep topography, improper use of land cover and adverse climatic conditions for landslides. In the western part of region, many studies have been carried out especially in the last decade for landslide susceptibility mapping using different evaluation methods such as deterministic approach, landslide distribution, qualitative, statistical and distribution-free analyses. The purpose of this study is to produce landslide susceptibility maps of a landslide-prone area (Findikli district, Rize) located at the eastern part of the Black Sea Region of Turkey by likelihood frequency ratio (LRM) model and weighted linear combination (WLC) model and to compare the results obtained. For this purpose, landslide inventory map of the area were prepared for the years of 1983 and 1995 by detailed field surveys and aerial-photography studies. Slope angle, slope aspect, lithology, distance from drainage lines, distance from roads and the land-cover of the study area are considered as the landslide-conditioning parameters. The differences between the susceptibility maps derived by the LRM and the WLC models are relatively minor when broad-based classifications are taken into account. However, the WLC map showed more details but the other map produced by LRM model produced weak results. The reason for this result is considered to be the fact that the majority of pixels in the LRM map have high values than the WLC-derived susceptibility map. In order to validate the two susceptibility maps, both of them were compared with the landslide inventory map. Although the landslides do not exist in the very high susceptibility class of the both maps, 79% of the landslides fall into the high and very high susceptibility zones of the WLC map while this is 49% for the LRM map. This shows that the WLC model exhibited higher performance than the LRM model.     

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.

     

A new methodological perspective on the impact of energy consumption on economic growth: time series evidence based on the Fourier approximation for solar energy in the USA

From the empirical energy literature, it is observed that studies focusing on the energy-economic growth nexus ignore the possible existence of gradual breaks as they employ methods without or with sharp structural breaks. Therefore, one can argue that they may yield biased and inefficient output in the presence of gradual breaks. The goal of this paper is to investigate the impact of solar energy consumption on GDP utilizing quarterly data over the period 1984–2018 for the USA. For this purpose, the paper performs a unit root test and a cointegration test that are based on the Fourier approximation to take gradual breaks into account. The paper also performs the dynamic ordinary least squares estimator to estimate long-run parameters. The findings document that there exists cointegration in the empirical model and that GDP is positively associated with solar energy consumption. Some implications based on the empirical findings are presented in the paper.

     

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.