Advanced Geomatics

Near-Real-Time Precise Point Positioning Technique with Single-Frequency Raw GNSS Observations on Android Smartphones

Hüseyin Pehlivan — 2023-09-12

In this study, positioning performance was evaluated by making single-frequency GNSS (Global Navigation Satellite System) observations under real-time conditions with a smartphone. In experiments, GNSS observations were recorded with the Xiaomi Redmi Note 8 Pro via the Geo++ RINEX Logger application. Measurements were made with the geodetic-grade CHC I80 GNSS receiver to evaluate the performance of the smartphone. In addition to the collected raw observation data set, solutions were realized with the Near-Real-Time Precise Point Positioning (N-RT-PPP) technique by using satellite orbit and clock correction products produced under real-time conditions from the CNES (National Centre for Space Studies) archive. When all the observations with the epoch difference are examined, it is observed that the root mean square error (RMSE) values of the GPS/GLONASS observations give better results than the only-GPS solutions. In addition, in the epoch differenced time series produced from the smartphone, an improvement between 92% and 98% was observed for the part below 1 cm horizontally and 2 cm vertically after the fluctuation.

GLOF Hazard Assessment using Geospatial Techniques in Hunza Nagar, Gilgit Baltistan, Pakistan

Ahsan Iqbal — 2023-09-12

Worldwide in different regions, increase in temperature has caused variations in many natural phenomena particularly expansion, contraction and creation of glacial lakes Hindu Kush Himalayas (HKH) region. In the recent past, several of these lakes have been burst out and generated Glacial Lake Outburst Floods (GLOFs) causing considerable human life loss damages to infrastructure and properties in downstream areas. This study is an effort to assess GLOF hazard in Nagar valley, Gilgit Baltistan using Geo-spatial Technique. Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) and Google Earth image has been utilized as input data. Buffer analysis is applied to demarcate the hazard zone and map the elements at risk. The results indicated that Passu Lake has potential to cause GLOF. The volume of the lake has been increased from 788383.79 m³ (2016) to 892910.494 m³ (2018). The exposed areas include portions of Karakoram Highway and some villages downstream to Passu Lake along Hunza River. The outcomes of this study will be helpful in reducing the adverse impacts of GLOFs events in Passu sub-watershed. The results can also assist decision makers to develop a mechanism for reliable and cost effective monitoring of glacier lakes and GLOFs hazard and risk assessment using advance geospatial hydrologic/hydraulic modeling techniques.

Landslide Risk Assessment using Geo-spatial Technique: A study of District Abbottabad, Khyber Pakhtunkhwa, Pakistan

Anum Gull — 2023-09-12

The study focused on identifying the causes and landslide-prone areas in Abbottabad District of Northern Pakistan. Remote sensing data, including NASA's Shuttle Radar Topographic Mission's (SRTM) Digital Elevation Model (DEM) and Landslide-8 imagery, were used in combination with geographic indices to identify the factors of landslides, such as slope, aspect, elevation, vegetation cover, hydrology, SAVI, and land cover change. The weighted overlay technique was used to assign weights to map layers and find the risk zones in the study area. The study revealed that the region is at risk of landslides due to high rock, sloppy areas, and built-up expansion. The major cluster of landslide risk is in the western and southern parts of the region, which are also more populated. The results and landslide susceptibility maps can be used to better understand the existence of landslides and for mitigation purposes, but field surveys are necessary for better predictions. Overall, the study provides valuable information for relevant authorities to prioritize landslide mitigation efforts in the region.

On Rational and Irrational Values of Trigonometric Functions

Veli Akarsu — 2023-09-12

The beginning of trigonometry goes back 4000 years from today. Today, there are Euclidean and Non-Euclidean trigonometry. It can be said that trigonometry emerged from the need to make maps containing the position information of the stars, to determine the time and to make a calendar, mostly in astronomy. Each of the six trigonometric functions is defined according to the directed plane angle. For each angle in the domain of these functions, their values correspond to a real number consisting of rational or irrational numbers. Trigonometric functions have an important position in basic sciences and technology as well as calculations in engineering and architecture. In addition to being a branch of mathematics, trigonometry is widely used in solving geometry and analysis problems. It has an indispensable importance in engineering and architectural design and calculations. The values of trigonometric functions are usually an irrational number, with the exception of some special angle values. Irrational numbers are numbers that are not proportional. In other words, they are numbers whose results are uncertain. Examples are numbers such as pi, e, and radical. The irrational values of trigonometric functions, which are infinite decimal expansions, are given in the tables by rounding them to only four or six digits. When entering the trigonometric function values in the tables (or the values obtained in the libraries of electronic calculators) into algebraic operations, the resulting numbers are approximated. In the article, besides showing that most of the trigonometric function values such as sinθ, cosθ and tanθ of many θ angles are irrational, the effect of these functions on the result values of calculations made in engineering and architecture is interpreted.

Forecasting of Water Levels by Artificial Neural Networks Technique in Lake Michigan-Huron

Mehmet Fehmi Yıldız — 2023-09-12

Water is an indispensable resource for all living things on Earth. Therefore, it is important to pay attention to current water consumption and to comply with safety precautions. Many water sources in the world experience ups and downs in the water level. Lake Michigan-Huron is an 8 km long body of water formed by the merging of Lake Michigan and Huron. The Huron and Michigan hydrological description is a single lake because the water from the Strait of Mackinac, which connects these lakes, balances what it expects. The flow is generally eastward, but the water moves in both directions depending on the local structure. Lake Michigan-Huron combined is the largest freshwater lake in the world. The aim of this study is to estimate the changes in water levels of Lake Michigan-Huron in the USA. In this study, the estimation of water levels on a monthly basis was investigated by using three different artificial neural network (ANN) models in order to predict the Michigan-Huron Lake water levels one month in advance. The ANN models used are Multilayer ANN (MANN), Radial Based ANN (RBANN) and Generalized Regression ANN (GRANN). The data sample consists of a 104-year (1918-2021) record of mean lake water level. 75% of all data were used for the training phase and 25% for the testing phase. Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and coefficient of determination (R2) were used as evaluation criteria. When the results are examined, all models give very good predictions during the training and testing phases. However, according to the test results, the model algorithms that give the most successful results are RBANN, MANN and GRANN, respectively.

In this study, the estimation of water levels on a monthly basis was investigated by using three different ANN models in order to predict the Michigan-Huron Lake water levels one month in advance. The ANN models used are Multilayer ANN (MANN)), Radial Based ANN (RBANN) and Generalized Regression ANN (GRANN). The data sample consists of a 104-year (1918-2021) record of mean lake water level. 75% of all data were used for the training phase and 25% for the testing phase. Mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2) were used as evaluation criteria.

When the results are examined, it is seen that all models make very good predictions during the training and testing phases. However, according to the test results, the model algorithms that give the most successful results are RBANN, MANN and GRANN, respectively.