Groundwater level forecasting is a paramount necessity for integrated management of a basin. Development of suitable models is an essential step in determining groundwater level fluctuations in the future. The main objective of this study was to provide a powerful hybrid model by combining the group method of data handling (GMDH) and certain signal processing techniques, ie, ensemble empirical mode decomposition (EEMD), wavelet transform (WT) and wavelet packet transform (WPT) for groundwater level forecasting in monthly time steps. Two different plains were selected to assess the performance of the afore-mentioned methods. The results showed that all of these preprocessing methods improved the capability of the group method of data handlin

Prediction of the particle concentration has received a great deal of research interest in recent decades, especially in the cities exposed to dust storms; hence, the use of early warning systems based on more accurate predictions to inform about the air quality situation in the next hours is very important. Thus, this study was conducted to improve the prediction accuracy of the particulate matter of aerodynamic diameter≤ 2.5-μm particle concentration in the short term and long term, by combining the discrete wavelet transform technique with the artificial intelligence methods, ie, adaptive neuro-fuzzy inference system, support vector regression, and artificial neural network. The data of the concentration of the suspended particles in

Background and objectives:?The changes in desert areas depend on climate condition and water balance of upstream watershed. satellite image can help us in distinguishing the trend of areas of Playa wetland.And with achieving these trend, both the status of the non-conventional water resources will be identified and this information can be used in wind erosion management. Materials and methods:?In the present study, the changes in Gavkhuni Wetland was evaluated using MODIS satellite images from 2000 to 2016. For this purpose, after performing the required modifications on the satellite images, they were classified and their changes in studies time intervals were detected. Since the changes of desert areas depend on the humidity variations, t

Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessments. To address this issue of long computational time, this paper proposes that the prediction of maximum water depth rasters can be considered as an image-to-image translation problem where the results are generated from input elevation rasters using the information learned from data rather than by conducting simulations, which can significantly accelerate the prediction process. The proposed approach was implemented by a deep convolutional neural network trained on flood simulation data of 18 designed hyetographs on three selected catchme

The deactivation of Earth Observing-1 satellite has resulted in the termination of capturing Hyperion data as a unique source of hyperspectral satellite imagery. These images also were collected through an on-demand service and thereby are not available for the entire Earth’s surface. The Operational Land Imager (OLI) sensor, on the other hand, provides a free source of multi-spectral images with global coverage. Recognized these facts, the aim of this paper is to enhance the spectral resolution of OLI images by using existing Hyperion imageries to generate a high spectral resolution image for a desired date and site. This was conducted through the artificial neural network (ANN). To find the suitable ANN, we compared the performance of

Increasing availability and quality of actual, as opposed to scheduled, open transport data offers new possibilities for capturing the spatiotemporal dynamics of the railway and other networks of social infrastructure. One way to describe such complex phenomena is in terms of stochastic processes. At its core, a stochastic model is domain-agnostic and algorithms discussed here have been successfully used in other applications, including Google's PageRank citation ranking. Our key assumption is that train routes constitute meaningful sequences analogous to sentences of literary text. A corpus of routes is thus susceptible to the same analytic tool-set as a corpus of sentences. With our experiment in Switzerland, we introduce a method for bui

This article presents a computer-aided design framework for the generation of non-standard structural forms in static equilibrium that takes advantage of the interaction between human and machine intelligence. The design framework relies on the implementation of a series of operations (generation, clustering, evaluation, selection, and regeneration) that allow to create multiple design options and to navigate in the design space according to objective and subjective criteria defined by the human designer. Through the interaction between human and machine intelligence, the machine can learn the nonlinear correlation between the design inputs and the design outputs preferred by the human designer and generate new options by itself. In additio

This paper proposes a new design approach based on an iterative machine learning algorithm to speed up the topological design exploration of compression-only shell structures with planar faces, considering both structural performance and construction constraints. In this paper, we show that building neural networks allows one to train a surrogate model to accelerate the structural performance assessment of various possible structural forms without going through a significantly slower process of geometric form-finding. The geometric form-finding methods of 3D graphic statics are used as the primary structural design tool to generate a single-layer, compression-only shell with planar faces. Subdividing the force diagram and its polyhedral cel

Aims: Water quality is an important factor in determining groundwater uses. An effort has been made to determine the groundwater quality of the Yazd-Ardakan aquifer. This research was conducted to fill the research gap in aquifer quality in the study area and make a comprehensive evaluation of the study aquifer using different water quality indices. The results can be used for decision-makers better to understand the water quality situation in the area.Materials & Methods: In order to carry out this study, the Ryznar Stability Index (RSI), Langelier saturation index (LSI), Larson–Skold index (LS), and Puckorius scaling index (PSI) were considered to determine groundwater quality for industrial use. Also, the drinking water quality index (

Aims: Producing a land use/land cover map is a fundamental step in different studies. This study aimed to assess the ability of hierarchical, pixel-based and object-oriented classification methods to produce land use/cover maps.Materials & Methods: This study was conducted in the Harat-Marvast basin of Yazd Province, Iran using Landsat imagery of 2016 (paths 161 and 162, row 39). The hierarchical image classification method was tested for land use/cover mapping. A statistical comparison between three algorithms, namely pixel-based, object-oriented and hierarchical image classification was performed using the McNemar test. An intensive field survey was also accomplished to obtain training and test samples.Findings: The kappa coefficients for

Appropriate investigation of soil erosion triggering/controlling factors is of great importance for natural resources management. Usually, soil erosion assessment is time consuming and impose high costs to researchers and managers. Therefore, design of experiments methods can be good approaches to achieve the purpose to which adequate attention has not been paid yet. This study was aimed to assess two response surface methods of Box-Behnken (BBM) and Face-centered central composite (FCCM) as well as a fractional factorial design of experiment Taguchi method (TM) for modeling and optimization of soil loss process at plot scale. So far, no study?has been conducted to evaluate and compare the ability of BBM, FCCM, and TM in soil erosion modeli

Dams construction as one of the methods in water resources management, are among the most important man-made structures along the river that can make major changes in the river regime and ultimately in the entire drainage basin and surface flow adjustment that hydrologic alterations are one of the most important alterations. Therefore, the quantity evaluations of hydrological changes and the deviation magnitude of natural flow regime affected by human activities such as dam construction need to be considered, that one of these methods is the indicators of hydrologic alterations (IHA). Therefore, the purpose of the present study is the statistical analysis of the IHA affected by the dam Bostan, Golestan and voshmgir (pre- and post-dam constr

As one of the most significant natural resources, land is the basis for life activities. Land use shows the way in which the human uses land in addition to the natural cover of lands. Land use/cover information is essential for planners, stakeholders, those who mage land resources [1]. Assessment of land use/cover changes is also very important to study their effect on different aspects of human life eg land degradation, erosion, dust storms, etc. Proper land management needs an understanding of the existing status of the land. Having knowledge about current land use/cover in conjunction with a correct means of monitoring change over time, is critical for land management. Remote sensing can be a good tool for producing land use/cover maps.