Internet of Underwater Things (IoUTs) is completely embraced by acoustic sensor nodes that are commonly battery consumption. The sensor’s battery life is restricted and it is problematic while it needs to recharge. Moreover, these kinds of underwater sensor nodes may form the cluster to store the huge amount of energy. The cluster head formation is the main problem that the cluster head needs to use

Flood occurs as a result of high intensity and long-term rainfalls accompanied by snowmelt which flow out of the main river channel onto the flood prone areas and damage the buildings, roads, and facilities and cause life losses. This study aims to implement extreme gradient boosting (EGB) method for the first time in flood susceptibility modelling and compare its performance with three advanced benchmark

Lithology identification of geological beds in the subsurface is fundamental in reservoir characterization. Recently, automated log analysis has an increasing demand in reservoir research and the oil industry. In this context, Machine Learning (ML) techniques arise as a surrogate model to provide lithology identification in a fast way. However, to achieve suitable performance, ML techniques require

The Acknowledgments section of this article has been corrected.

We analyzed an extended time series of Schumann Resonance recordings with two multi-parametric statistical methods, the generalized linear Logistic Regression—LogReg and the non-linear Random Forest—RF, in order to test their potential for earthquake prediction within a narrow time-space-magnitude window of 48 h, 250 km from our observing site, and events higher than magnitude 4 of the Richter scale

Field data collection and geospatial map generation are critical aspects in different fields such as road asset management, urban planning, and geospatial applications. However, one of the primary impediments to data collection is the availability of spatial and attribute data. This issue is aggravated by the high cost of conventional data collection and data processing methods and by the lack of geospatial

With the explosive growth of geological data, considerable researches are focused on accurately retrieving specific information from massive data and fully exploiting the potential knowledge and information in unstructured data. Currently, the researches on unstructured content retrieval mostly ignore the association of semantics and knowledge or only consider the association of a singular granularity

Reasonable evaluation of the comprehensive effect of coordinated coal-water development is necessary for the sustainable development of ecologically vulnerable area. Taken Nalinhe mining area as the research area and focused on the thick coal seam mining, an evaluation index system of the comprehensive effect of coordinated coal -water development was proposed. The analytic hierarchy process modified

This paper addresses the problem of feature selection aiming to improve a flood forecasting model. The proposed model is carried out through a case study that uses 18 different time series of thirty-five years of hydrological data, forecasting the level of the Xingu River, in the Amazon rainforest in Brazil. We employ a Genetic Algorithm for the task of feature selection and exploit several different

This paper deals with the online offset detection in GPS time series recorded in volcanic areas. The interest for this problem lies in the fact that an offset can indicate the opening of eruptive fissures. A Change Point Detection algorithm is applied to carry out, in an online framework, the offset detection. Experimental results show that the algorithm is able to recognize the offset generated by

Crop classification based on object-based image analysis (OBIA) is increasingly reported. However, it is still challenging to produce high-quality crop type maps by using recent techniques. This article introduces a new object-based crop classification algorithm which contains 4 steps. First, a random forest (RF) classifier is trained by using the initial training set, which tends to have a relatively

Since the beginning of space exploration, the galaxy classification has been a vexing problem that has always muddled the astrophysicists. A number of techniques have proven their remarkable utility in the classification of galaxies, however, upon analysis, these methods revealed certain inefficiencies that cannot be overlooked. The traditional classification of galaxies in the universe contains a

NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) carries a laser altimeter that fires 10,000 pulses per second towards Earth and records the travel time of individual photons to measure the elevation of the surface below. The volume of data produced by ICESat-2, nearly a TB per day, presents significant challenges for users wishing to efficiently explore the dataset. NASA’s National Snow

From the practice of tourism industry, many scenic spots emphasize emergency rescue and ignore risk prevention in safety management.From the perspective of research, a large number of literatures focus on the emergency management of scenic spots, and lack the concept and method of systematic risk management. This research puts forward the natural disaster risk assessment method in tourist area by the

A method to forecast air temperature TA in Elqui Valley (south of Chile’s Atacama Desert) using an artificial neural network (ANN) and meteorological time series data relevant to this zone, is proposed. This zone has one of the most sensitive climates in South America due to the influence of phenomena such as El Niño/La Niña, the Southeast Pacific Subtropical Anticyclone, Humboldt Current, and Madden–Julian

2D underground models that reflect the physical, mechanical and structural properties of the geological units below the ground are called geotechnical cross-sections. In order to visualize these cross-sections, geotechnical engineers record the measurements obtained from vertical electrical sounding, seismic refraction or drilling methods. The visualization process is done by hand and it may vary depending

A large number of georeferenced quantitative data about rock and geoscience surveys are buried in geological documents and remain unused. Data analytics and information extraction offer opportunities to use this data for improved understanding of ore forming processes and to enhance our knowledge. Extracting spatiotemporal and semantic information from a set of geological documents enables us to develop

The Curie point-depth, frequently related to the depth to the bottom of the magnetic source, is widely employed as an estimator of temperature at depth when borehole temperature data are not available. The Curie point-depth is calculated using the spectral analysis of the magnetic data derived from aeromagnetic or satellite surveys. In this paper, MATLAB user-friendly GUI are constructed to calculate

In this article we develop a new method to grid passive microwave data in the presence of spatial correlation patterns. Our proposal combines a Tychonov inverse method with a generalized cross validation procedure to grid the observations over a discrete retrieval grid. To build this grid, the study region is partitioned into objects following an object-based image analysis procedure. Then, this partition

Modeling the rate of penetration (ROP) plays a fundamental role in drilling optimization since the achievement of an optimum ROP can drastically reduce the overall cost of drilling activities. Evolved Extreme learning machine (ELM) with the evolutionary algorithms and multi-layer perceptron with Levenberg-Marquardt training algorithm (MLP-LMA) were proposed in this study to predict ROP. This paper

The accurate expression of terrain morphology is the essence of the urban terrain model construction. As the skeleton of urban terrain, urban road has special geometric and semantic characteristics. The road is a strip-shaped feature, which is flat in the horizontal direction and gently undulating in the vertical direction, and the roads are interlinked and connected. This research takes Jianye District

The purpose of this study was to investigate improve the utilization rate of water resources and using multi-objective optimization algorithms to prediction water demand for the next 30 years, then the swarm intelligence approach was used to analysis for the development and utilization of water resources. The results obtained in this study include built a group optimization intelligent algorithm and

Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam. Partial Least Squares (PLS) sub-model strategy is one of the outstanding multivariate analysis

3D geological modeling data can only be measured rarely or indirectly, as it is necessary to interpolate the sparse and uneven geological data in the study area. Given the problems in the interpolation of data from different sources, we have introduced stratum interpolation and visualization technology based on the moving least squares (MLS) method and Poisson surface reconstruction method. The MLS

This study is aimed to elucidate individual fault response and peculiar earthquake characteristics in complex seismotectonic environment of southern Pakistan (Balochistan, Sindh and frontal offshore areas). The southern Pakistan is a seismic-mélange wherein earthquake prone sources are diversified and closely associated with active plate margins. The spatial patterns of seismicity are significant in

We consider the challenging task of evaluating the commercial viability of hydrocarbon prospects based on limited information, and in limited time. We investigate purely data-driven approaches to predicting key reservoir parameters and obtain a negative result: the information that is typically available for prospect evaluation and is suitable for data-based methods, cannot be used for the required

In recent days, people in remote area suffer a lot due to variety of natural calamities such as flooding, earthquake and so on. It has been noted that people used to stay in top portions of their house when there is a flooding issue. Hence, it is very difficult for the rescue team to identify the location of a person by looking at the parts of a person such as hands, legs and partial image of a face

The third-generation meteorological satellites equipped with highly-improved imagers provide a huge amount of Earth observation data. Himawari-8 is the first unit of the Japan Meteorological Agency’s third generation of geostationary satellites. After its starting operation in 2015, there are several websites that provide remotely sensed images in real time. However, it is hard to develop a real-time

The accurate acquisition of information concerning farmland in coal mining areas with high groundwater levels can provide a basis for land dynamic monitoring and protection. In this study, visible light images from an unmanned aerial vehicle (UAV) were used as the data source, from which farmland located in the coal mining areas with high groundwater levels were extracted. Based on the optimal scale

A methodology to select the maximum level of wavelet decomposition to forecast seven days of daily inflows by a hybrid model wavelet-based artificial neural network (WANN) is proposed. The wavelet decomposition was employed to decompose an input time series into approximation and detail components, and the approximations were used as inputs to artificial neural networks (ANN) for WANN hybrid models

High quality, accessible Digital Elevation Model (DEM) datasets play a major role in monitoring the changes in the Earth’s surface. This study proposes a novel method to increase the vertical precision of CARTOSAT 10 m DEM by blending it with publicly available SRTM (Shuttle Radar Topography Mission) DEM using machine learning methods. Machine learning methods such as Genetic Programming (GP) and Artificial

Dynamic monitoring data of the groundwater level form an important basis to understand the current state of groundwater development and utilization, which is required to plan its sustainable exploitation. The dynamic data of groundwater level are typical space-time series data with the characteristics of nonlinear strong space-time correlation. However, because of the influences of human or natural

Physical height changes play an important role in the modernization of national/international levelling networks in the discipline of geodesy. This paper represents a software package, named PHCSOFT (Physical Height Changes SOFTware) which allows to compute and plot physical height changes from Global Geopotential Models (GGMs) based on a monthly interval GRACE (Gravity Recovery and Climate Experiment)

Forests play a vital role in the regulation of climate, absorption of carbon dioxide, hydrological cycle, conservation of water, soil and biodiversity and help mitigate natural disasters. With the help of various remote sensors, high-resolution satellite images are being collected nowadays, which helps in tackling the global challenges of forest mapping in remote areas. Each landscape will grow different

Gullies characterized by frequent material exchange have profoundly affected the loess landforms evolution in the Loess Plateau of China. Accompanying gully development, various types of gullies have shaped the distinctive loess landforms. Thus, the spatial variations of gully morphology map the regional differences of gully development and loess landforms. Based on 6 key test areas with 5 m resolution

Deep learning framework aids the researchers in learning different application areas to a greater extent. Deep learning framework is preferred over machine learning since it helps to learn the input from end to end, whereas latter one require the inputs to be cut into pieces according to the need. This paper proposes a Multi-Variant Deep Learning framework for learning and classifying the hyperspectral

Land use classification is fundamental both for monitoring and predicting regional development patterns and for planning and regulating land use. This research proposed a joint probability-based classifier for land use classification of multispectral remote sensing data and applied it to the Lake Baiyangdian region of North China. This classifier, based on the vine copula method, was suitable for dealing

The huge amount of active research has been focused on developing the remote sensing based applications for providing the object classification procedure focusing on the energy reflected on the earth surface, the remote sensor collects data and its management with analysis of all kinds of spatial information with enhanced accuracy. This paper proposes the Real-time Image processing method to implement

Nomenclature in any science is of fundamental importance. From performance comparison of isometric and modified or hybrid log-ratio transformations of major elements, we present a complete multidimensional classification scheme for altered igneous rocks for High-Mg rock varieties and magma and rock types. This scheme is efficiently applicable through a computer package called Multidimensional Igneous

Due to the recent climate changes and their consequences such as flash floods and droughts, there is a need for Land Use Land Cover mapping to monitor environmental changes which have effects on ecology, policy management, health, and disaster management. It should be noticed that recent droughts caused by climate change, and on the other hand, population growth has increased the rate of urbanization

With the availability of multi-sensor, multi-temporal, multi-resolution and multi-spectral images from operational Earth observation satellites, remote sensing image fusion has become a valuable tool. The goal of remote sensing image fusion is to integrate complementary information from multi-source data such that the new images are more suitable for human visual perception and computer-processing

This paper proposes an eigenvector spatial filtering-based (ESF-based) regression model for land cover pattern simulation in China’s Hubei province. The significance and influence of biophysical, climatic, and socio-economic factors have been detected and analyzed in the study region. The ESF-based multinomial logistic regression (spatial model) is constructed for discrete choices to take spatial autocorrelation

Digital elevation model (DEM) provides information of geographic landscapes at multiple scales. However, the study on the scales of DEM is not sufficient and there still remain many issues in DEM scaling. Generating DEMs of different scales has to consider terrain skeletons and terrain structural details for preserving terrain feature hierarchies. Existing literatures (e.g., maximum Z-tolerance approach)

Generally, traditional convolutional neural networks (CNN) models require a long training time and output high-dimensional features for content-based remote sensing image retrieval (CBRSIR). This paper aims to examine the retrieval performance of the MobileNets model and fine-tune it by changing the dimensions of the final fully connected layer to learn low dimensional representations for CBRSIR. Experimental

Modern Underwater Wireless Sensor Networks (UWSN) would provide big administrations with numerous underwater surveying and technical applications, working in the unstable submerged deep-water conditions. A huge obstacle in these networks is the lifetime limit. The submerged correspondence frameworks mostly employ acoustic communication today. Acoustic interchange communication offers longer ranges

PNN is a feed-forward neural network in which there is an important parameter called smoothing parameter. This work implemented a combination of PNN with PSO optimization in order to estimate unique smoothing parameters for each SWIR bands of ASTER image and classified the ASTER image to different hydrothermal alteration zones (argillic, phyllic, propylitic and vegetation covering). The stydy area

With the development of remote sensing and large-scale environmental modelling, large amount of environmental data are continuously becoming available. An intuitive and comprehensive visualization of these data could facilitate data exploration, communication and collaboration between the stakeholders for informed decisions making. In Poyang lake basin regions, we demonstrate how to develop a software

Streamflow prediction is a significant undertaking for water resources planning and management. Accurate forecasting of streamflow always being a challenging task for the hydrologist due to its high stochasticity and dynamic patterns. Several traditional and the deep learning models have been applied to simulate the complex nature of the hydrological system. However, to develop and explore a better

In this paper, we present a calculation for settling times of grains falling under the action of the gravitational field inside a Jupiter mass protoplanet formed via disk instability, where the grains are assumed to be grown by the process of cold welding. As the energy equation, we have considered the conductive-radiative case of heat transport. In our calculation, we have not used any density model

Due to the extremely complex regional geological tectonic movements in the Alpide Himalayan seismic belt with the dense population and rapid economic development, the frequent seismic activity has caused huge concern. In geology, the research on the spatiotemporal characteristics of seismic activity in different areas at different scales has always been valuable to explore the mechanism of seismic

Spatial missions such as the Kepler mission, and the Transiting Exoplanet Survey Satellite (TESS) mission, have encouraged data scientists to analyze light curve datasets. The purpose of analyzing these data is to look for planet transits, with the aim of discovering and validating exoplanets, which are planets found outside our Solar System. Furthermore, transiting exoplanets can be better characterized

There are substantial problems in the photogrammetric image matching especially in the images taken by UAVs in the regions where grassland, waterfront, forest, buildings, bridges, high-voltage lines etc. in the urban and rural areas. The main reason of these problems are color, tone, texture, contrast and scale changes cannot be successfully detected in between sequential images. To solve these problems

An efficient and effective spatiotemporal prediction algorithm for PM2.5 (i.e. particulate matter with a diameter of less than 2.5 micrometers) is urgently needed to study the distribution of PM2.5 over a continuous spatiotemporal domain, which not only helps to make scientific decisions on the prevention and control of PM2.5 pollution but also promotes meaningful assessment of the quantitative relationship

Longchuan River Basin is located in Chuxiong (central Yunnan province, China) that is subject to severe drought. Agriculture is critical to local economy, which can lead to intense agricultural activity. Accordingly, delineating groundwater potential zones and developing usable groundwater resources are required to meet potable and irrigating water demands in the zone. Remote sensing (RS), geographic

Planner surfaces play an important role in several mapping and planning applications in urban areas. Automated algorithms to extract such surfaces are essential since they reduce the time and cost when compared to editing these DEMs manually. These surfaces could be constructed from either correlation-based DEMs or LIDAR point clouds. The advantages of the first over the second make it more appealing

Developments in deep learning for time-series problems have shown promising results for data prediction. Particulate Matter equal or smaller than 10 μm (PM10) have increased importance in the research field due to the negative impact in the respiratory system. PM10 particles show non-linear behavior, hence it is not an easy task to implement techniques to predict subsequent concentration of the particles

Automatic Image Annotation (AIA) is used in image retrieval systems to retrieve the images by predicting tags for images. To achieve image retrieval with high accuracy, an automatic image annotation approach by using Multiclass SVM with the hybrid kernel is proposed. The hybrid kernel is a combination of Radial Basis Function (RBF) and Polynomial Kernel which overcomes the drawbacks of single kernels

Desertification is one of the major challenges facing humanity. However, there is no effective way to effectively control desertification. China is experiencing serious desertification, and to alleviate this trend, China has implemented a series of major ecological projects since 1978 and has achieved good results. Remote sensing can record changes in surface processes over a long period of time and

An integrated approach of morphometric and litho-structural studies for flood risk assessment of Ala River Basin (ARB) in Akure city, southwestern Nigeria aided by geospatial techniques was carried out. The Ala River Basin comprises of four sub-basins covering a total area of 427km2 with fourth stream order of dendritic and sub-trellis drainage patterns. The values obtained for the morphometric features

When 3D modeling of the geological body is carried out based on the section, problems such as unable to close the structural plane, slow rendering speed of the model and uneven and long and narrow section triangle network will occur due to the situation of super near point or too dense fold point. To solve the appealing problem, this paper proposes a section thinning-densification method based on the