Interpolation Parameters in Inverse Distance-Weighted Interpolation Algorithm on DEM Interpolation Error

Although DEM occupies an important basic position in spatial analysis, so far, the quality of DEM modeling has still not reached a satisfactory accuracy. This research mainly discusses the influence of interpolation parameters in the inverse distance-weighted interpolation algorithm on the DEM interpolation error. The interpolation parameters to be studied in this paper are the number of search points, the search direction, and the smoothness factor. In order to study the optimization of IDW parameters, the parameters that have uncertain effects on DEM interpolation are found through analysis, such as the number of search points and smoothing factor. This paper designs an experiment for the optimization of the interpolation parameters of the polyhedral function and finds the optimal interpolation parameters through experimental analysis. Of course, the “optimum” here is not the only one, but refers to different terrain areas, which makes the interpolation results relatively good. The selection of search points will be one of the research focuses of this article. After determining the interpolation algorithm, the kernel function is also one of the important factors that affect the accuracy of DEM. The value of the smoothing factor in the kernel function has always been the focus of DEM interpolation research. Different terrains, different interpolations, and functions will have different optimal smoothing factors. The search direction is to ensure that the sampling points are distributed in all directions when the sampling points are sparse and to improve the contribution rate of the sampling points to the interpolation points. The selection of search shape is to improve computing efficiency and has no effect on DEM accuracy; the search radius is mainly controlled by the number of search points, and there are two methods: adaptive search radius and variable length search radius. When the weight coefficient k = 1 , 2 , 3 , 4 , the number of sampling points involved in the interpolation calculation is different, and the error in the residual varies greatly, and both increase with the increase of the number of sampling points in the parameter interpolation calculation. This research will help improve the quality evaluation of DEM.

Đánh giá thực trạng khô hạn tại tỉnh Bến Tre

Điều kiện khô hạn trong giai đoạn 2015 - 2019 được đánh giá trong nghiên cứu nhằm phục vụ sản xuất nông nghiệp trên địa bàn tỉnh Bến Tre. Các dữ liệu về điều kiện thời tiết được thu thập tại Đài Khí tượng Thủy văn Bến Tre; phương pháp nội suy IDW (Inverse Distance Weighted) được áp dụng để xây dựng bản đồ khô hạn; mức độ ảnh hưởng của khô hạn được đánh giá dựa vào chỉ số SPI (Standardized Precipitation Index). Kết quả nghiên cứu cho thấy Bến Tre có 4 vùng hạn theo các mức độ nặng, trung bình, nhẹ và không hạn. Mức độ hạn nặng và trung bình cao nhất năm 2015, 2016; các năm còn lại hạn ở mức nhẹ. Tuy nhiên, diện tích khô hạn năm 2019 là cao nhất và giảm dần theo năm 2017, 2016, 2015, 2018. Khô hạn đã và đang ảnh hưởng đến các mô hình canh tác nông nghiệp ở tỉnh Bến Tre. Vì vậy, nghiên cứu thêm về ảnh hưởng của khô hạn đến sản xuất nông nghiệp, có những định hướng sử dụng đất hợp lý và bền vững trong điều kiện ở tỉnh Bến Tre là cần thiết.

A Triangulation Meta-Learning Framework for Imputing Missing Values in Weather Time Series

Machine learning and statistical methods can help model meteorological phenomena, especially in a context with many variables. However, it is not unusual that the measurement of those variables fails, generating data gaps and compromising data history analysis. The framework combines the predictions provided by three machine learning methods: decision trees, artificial neural networks and support vector machine, together with values calculated through five triangulation methods: arithmetic average, inverse distance weighted, optimized inverse distance weighted, optimized normal ratio and regional weight. Each machine learning algorithm generates eight regression models. One of the machine learning models makes predictions based only on the date. The remaining seven models make predictions based on one weather parameter (max. temperature, min. temperature, insolation, among others), in addition to the respective date. The triangulation methods use the climatic data from three neighboring cities to estimate the parameter of the target city. The generated dataset is, posteriorly, optimized by meta-learning algorithms. The results show that the additional information provided by the new machine learning models and the triangulation methods offered a significant increase in the accuracy of the imputed data. Moreover, the statistical analysis and coefficient of determination R² showed that the meta-learning model based on regression trees successfully combined the base-level outputs to generate outputs that best fill in the missing values of the time series studied in this paper.

Analisis Daerah Rawan Longsor Di Kabupaten Brebes Memanfaatkan Citra Landsat 8 Dengan Metode Inverse Distance Weighted (IDW)

Brebes adalah salah satu Kabupaten di Provinsi Jawa Tengah. Sebagian besar wilayahnya berupa dataran rendah yang diapit sungai pemali dan sungai serayu. Maka dari itu, Kabupaten Brebes merupakan salah satu daerah yang rawan terjadi tanah longsor maupun bencana lainnya. Maka dari itu akan dilakukan penelitian untuk menganalisis daerah rawan longsor di Kabupaten Brebes dengan citra landsat 8 yang dipadukan dengan metode Inverse Distance Weighted (IDW) sehingga dapat diketahui daerah mana saja yang berpotensi longsor. Parameter yang digunakan untuk melakukan analisis adalah jenis tanah, curah hujan, dan kemiringan lereng. Ketiga parameter tersebut akan dilakukan overlay sehingga mendapatkan peta daerah rawan tanah longsor. Hasil penelitian ini diharapkan dapat digunakan oleh pemerintah setempat untuk melakukan upaya preventif sehingga dapat mengurangi kerugian dari masyarakat setempat.

The Utilization of Remote Sensing Imagery and Inverse Distance Weighted Scheme to Simulate the White Oil Effects on Soil Geotechnical Properties

This research aims to utilize a complementarity of field excavations and laboratory works with spatial analyses techniques for a highly accurate modeling of soil geotechniques properties (i.e. having lower root mean square error value for the spatial interpolation). This was conducted, for a specified area of interest, firstly by adopting spatially sufficient and well distributed samples (cores). Then, in the second step, a simulation is performed for the variations in properties when soil is contaminated with commonly used industrial material, which is white oil in our case. Cohesive (disturbed and undisturbed) soil samples were obtained from three various locations inside Baghdad University campus in AL-Jadiriya section of Baghdad, Iraq. The unified soil categorization system (USCS) was adopted and soil was categorized as clayey silt of low plasticity (CL). The cores were contaminated in a synthetically manner using two specified values of white oil (5 and 10 % of its dry weight). Then, the samples were left for three days to certify homogeneity. The results of laboratory tests were enhanced by spatial interpolation mapping, using Inverse Distance Weighted scheme for normal soil samples and those with synthetic pollution. The liquid limit rates were raised slightly as contamination rates raised, while particle size was reduced; in contrary, shear strength parameter values were decreased.