Socio-hydrological framework for investigating farmers’ activities affecting the shrinkage of Urmia Lake; hybrid data mining and agent-based modelling     

Parsa Pouladi  Amir Molajou  Mohammad Hadi Afshar 《水文科学杂志》2020,65(8):1249-1261

ABSTRACT

Developing a general framework to capture the complexities associated with the non-linear and adaptive nature of farmers facing water resources scarcity is a challenging problem. This paper integrates agent-based modelling (ABM) and a data mining method to develop a hybrid socio-hydrological framework to provide future insights for policy-makers. The data associated with the farmers’ main characteristics were collected through field surveys and interviews. Afterwards, the association rule was employed to discover the main patterns representing the farmers’ agricultural decisions. The discovered patterns were then used as the behavioural rules in ABM to simulate the agricultural activities. The proposed framework has been was applied to explore the interactions between agricultural activities and the main river feeding the Urmia-Lake, Iran. The outcomes indicate that farmers’ acquisitive traits and belongings have significant impacts on their socio-hydrological interactions. The reported values of the efficiency criteria may support the satisfactory performance of the proposed framework.  相似文献   

Time-frequency decomposition of seismic signals via quantum swarm evolutionary matching pursuit     

Amir Semnani  Liang Wang  Mehdi Ostadhassan  Majid Nabi-Bidhendi  Babak Nadjar Araabi 《Geophysical Prospecting》2019,67(7):1701-1719

Matching pursuit belongs to the category of spectral decomposition approaches that use a pre-defined discrete wavelet dictionary in order to decompose a signal adaptively. Although disengaged from windowing issues, matching point demands high computational costs as extraction of all local structure of signal requires a large size dictionary. Thus in order to find the best match wavelet, it is required to search the whole space. To reduce the computational cost of greedy matching pursuit, two artificial intelligence methods, (1) quantum inspired evolutionary algorithm and (2) particle swarm optimization, are introduced for two successive steps: (a) initial estimation and (b) optimization of wavelet parameters. We call this algorithm quantum swarm evolutionary matching pursuit. Quantum swarm evolutionary matching pursuit starts with a small colony of population at which each individual, is potentially a transformed form of a time-frequency atom. To attain maximum pursuit of the potential candidate wavelets with the residual, the colony members are adjusted in an evolutionary way. In addition, the quantum computing concepts such as quantum bit, quantum gate, and superposition of states are introduced into the method. The algorithm parameters such as social and cognitive learning factors, population size and global migration period are optimized using seismic signals. In applying matching pursuit to geophysical data, typically complex trace attributes are used for initial estimation of wavelet parameters, however, in this study it was shown that using complex trace attributes are sensitive to noisy data and would have lower rate of convergence. The algorithm performance over noisy signals, using non-orthogonal dictionaries are investigated and compared with other methods such as orthogonal matching pursuit. The results illustrate that quantum swarm evolutionary matching pursuit has the least sensitivity to noise and higher rate of convergence. Finally, the algorithm is applied to both modelled seismograms and real data for detection of low frequency anomalies to validate the findings.  相似文献   

Assessment of geomorphological bank evolution of the alluvial threshold rivers based on entropy concept parameters     

Azadeh Gholami  Majid Mohammadian  Amir Hossein Zaji  Bahram Gharabaghi 《水文科学杂志》2019,64(7):856-872

The complex stream bank profiles in alluvial channels and rivers that are formed after reaching equilibrium has been a popular topic of research for many geomorphologists and river engineers. The entropy theory has recently been successfully applied to this problem. However, the existing methods restrict the further application of the entropy parameter to determine the cross-section slope of the river banks. To solve this limitation, we introduce a novel approach in the extraction of the equation based on the calculation of the entropy parameter (λ) and the transverse slope of the bank profile at threshold channel conditions. The effects of different hydraulic and geometric parameters are evaluated on a variation of the entropy parameter. Sensitivity analysis on the parameters affecting the entropy parameter shows that the most effective parameter on the λ-slope multiplier is the maximum slope of the bank profile and the dimensionless lateral distance of the river banks.  相似文献   

Boundary Condition Nomenclature Confusion in Groundwater Flow Modeling     

Amir Jazayeri  Adrian D. Werner 《Ground water》2019,57(5):664-668

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Latitudinal and Longitudinal Variations of Earth’s Magnetic Force on Electrified Hydrometeors     

Sadeghi  Babak  Taghavi  Farahnaz  Akmal  Amir Abbas Shayegani 《Geomagnetism and Aeronomy》2019,59(6):770-781

Geomagnetism and Aeronomy - In this study, a hypothesis is proposed about the possible effect of Geomagnetic field (GMF) on the charge structure of a thundercloud based on Lorentz force equation...  相似文献   

Spatially-Weighted Factor Analysis for Extraction of Source-Oriented Mineralization Feature in 3D Coordinates of Surface Geochemical Signal     

Esmaeiloghli  Saeid  Tabatabaei  Seyed Hassan  Carranza  Emmanuel John M.  Hosseini  Shahram  Deville  Yannick 《Natural Resources Research》2021,30(6):3925-3953

Natural Resources Research - This contribution proposes a spatially weighted factor analysis (SWFA) to recognize effectively the underlying mineralization-related feature(s) in geochemical signals....  相似文献   

Direct CPT and CPTu methods for determining bearing capacity of helical piles     

Amir Mansour Askari Fateh  Ahmad Fahimifar 《Marine Georesources & Geotechnology》2017,35(2):193-207

Helical piles are structural deep foundation elements, which can be categorized as torque-driven piles without any limitations to implement in marine situations. Different methods are used to predict the axial capacity of helical piles, such as static analysis, but have some limitation for this type of piles on marine conditions. In situ testing methods as supplement of static analysis have been rarely used for helical piles. In geotechnical engineering practice, the most common in situ tests particularly applicable for coastal or offshore site investigation are cone penetration test (CPT) and piezocone penetration test (CPTu). The CPT is simple, repeatable, and prepares the continuous records of soil layers. In this paper, a data bank has been compiled by collecting the results of static pile load tests on thirty-seven helical piles in ten different sites including CPT or CPTu data. Axial capacities of thirty-seven helical piles in different sites were predicted by direct CPT methods and static analysis. Accuracy estimation of ten direct CPT methods to predict the axial capacity of helical piles was investigated in this study. Comparisons have been made among predicted values and measured capacity from the pile load tests. Results indicated that the recently developed methods such as NGI-05 (2005), ICP-05 (2005), and UWA-05 (2005) predicted axial capacity of helical piles more accurately than the other methods such as Meyerhof (1983), Schmertmann (1978), Dutch (1979), LCPC (1982), or Unicone (1997). However, more investigations are required to establish better correlation between CPT data and axial capacity of helical piles.  相似文献   

Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features   总被引：1，自引：1，他引：0  

Seyed Amir Naghibi  Mostafa Moradi Dashtpagerdi 《Hydrogeology Journal》2017,25(1):169-189

One important tool for water resources management in arid and semi-arid areas is groundwater potential mapping. In this study, four data-mining models including K-nearest neighbor (KNN), linear discriminant analysis (LDA), multivariate adaptive regression splines (MARS), and quadric discriminant analysis (QDA) were used for groundwater potential mapping to get better and more accurate groundwater potential maps (GPMs). For this purpose, 14 groundwater influence factors were considered, such as altitude, slope angle, slope aspect, plan curvature, profile curvature, slope length, topographic wetness index (TWI), stream power index, distance from rivers, river density, distance from faults, fault density, land use, and lithology. From 842 springs in the study area, in the Khalkhal region of Iran, 70 % (589 springs) were considered for training and 30 % (253 springs) were used as a validation dataset. Then, KNN, LDA, MARS, and QDA models were applied in the R statistical software and the results were mapped as GPMs. Finally, the receiver operating characteristics (ROC) curve was implemented to evaluate the performance of the models. According to the results, the area under the curve of ROCs were calculated as 81.4, 80.5, 79.6, and 79.2 % for MARS, QDA, KNN, and LDA, respectively. So, it can be concluded that the performances of KNN and LDA were acceptable and the performances of MARS and QDA were excellent. Also, the results depicted high contribution of altitude, TWI, slope angle, and fault density, while plan curvature and land use were seen to be the least important factors.  相似文献   

Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters     

Mohammad Isazadeh  Seyed Mostafa Biazar  Afshin Ashrafzadeh 《Environmental Earth Sciences》2017,76(17):610

The present study attempts to model the spatial variability of three groundwater qualitative parameters in Guilan Province, northern Iran, using artificial neural networks (ANNs) and support vector machines (SVMs). Data collected from 140 observation wells for the years 2002–2014 were used. Five variables, X and Y coordinates of the observation well, distance of the observation well from the shoreline, areal average 6-month rainfall depth, and groundwater level at the day of water quality sampling, were considered as primary input variables. In addition, nine qualitative variables were also considered as auxiliary input variables. Electrical conductivity (EC), sodium concentration (Na⁺), and sulfate concentration (SO₄ ^2?) of the groundwater in the region were estimated using ANNs and SVMs with different input combinations. The results showed that both ANNs and SVMs work well when the only primary input variable is the well location. The ANN yielded an RMSE of 1.03 mEq/l for SO₄ ^2?, 1.05 mEq/l for Na⁺, and 203.17 μS/cm for EC, using the X and Y coordinates of the observation wells in the study area. In the case of SVM, these values were, respectively, 0.87, 0.87, and 176.68. Considering the auxiliary input variables (pH, EC, and the concentrations of Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^?, SO₄ ^2?, and HCO₃ ^?) resulted in a significant decrease in the RMSE of both ANNs (0.22, 0.30, and 33.04) and SVMs (0.26, 0.34, and 36.23). Comparing these RMSE values with those of cokriging interpolation technique (0.59, 0.98, and 177.59) indicated that ANNs and SVMs produced more accurate estimates of the three qualitative parameters. The relative importance of auxiliary input variables was also determined using Gamma test. The output uncertainty of ANNs and SVMs were determined using p-factor and d-factor. The results showed that SVMs have less uncertainty than ANNs.  相似文献   

Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models     

Tianfang Xu  Albert J. Valocchi  Jaesik Choi  Eyal Amir 《Ground water》2014,52(3):448-460

Quantitative analyses of groundwater flow and transport typically rely on a physically‐based model, which is inherently subject to error. Errors in model structure, parameter and data lead to both random and systematic error even in the output of a calibrated model. We develop complementary data‐driven models (DDMs) to reduce the predictive error of physically‐based groundwater models. Two machine learning techniques, the instance‐based weighting and support vector regression, are used to build the DDMs. This approach is illustrated using two real‐world case studies of the Republican River Compact Administration model and the Spokane Valley‐Rathdrum Prairie model. The two groundwater models have different hydrogeologic settings, parameterization, and calibration methods. In the first case study, cluster analysis is introduced for data preprocessing to make the DDMs more robust and computationally efficient. The DDMs reduce the root‐mean‐square error (RMSE) of the temporal, spatial, and spatiotemporal prediction of piezometric head of the groundwater model by 82%, 60%, and 48%, respectively. In the second case study, the DDMs reduce the RMSE of the temporal prediction of piezometric head of the groundwater model by 77%. It is further demonstrated that the effectiveness of the DDMs depends on the existence and extent of the structure in the error of the physically‐based model.  相似文献