scholarly journals A fuzzy hybrid clustering method for identifying hydrologic homogeneous regions

2018 ◽  
Vol 20 (6) ◽  
pp. 1367-1386 ◽  
Author(s):  
S. Saeid Mousavi Nadoushani ◽  
Naser Dehghanian ◽  
Bahram Saghafian
Keyword(s):  
Clustering Algorithm ◽  
Curve Number ◽  
Optimum Number ◽  
Runoff Generation ◽  
Study Region ◽  
Surface Features ◽  
Management Measures ◽  
Number Of Factors ◽  
Walnut Gulch ◽  
Homogeneous Regions

Abstract Identification of hydrologic homogeneous regions (HHR) facilitates prioritization of watershed management measures. In this study, a new methodology involving a combination of self-organizing features maps (SOFM) method and fuzzy C-means algorithm (FCM), designated as SOMFCM, is presented to identify HHRs. The case study region is Walnut Gulch Experimental Watershed (WGEW) located in Arizona. The input data consisted of a number of factors that influence runoff generation processes, including ten surface features as well as various rainfall values corresponding to 25, 50, and 100 years return periods. Factor analysis (FA) was applied for the selection of effective surface features along with rainfall value, used in the clustering algorithm. Validation procedure indicated that the best clustering scenario was achieved through merging three layers including TPI (topographic position index), CN (curve number), and P50 (50-year rainfall). The optimum number of clusters turned out to be six while the fuzzification parameter became 1.6. The presented methodology may be proposed as a simple approach for identifying HHRs.

scholarly journals Spatial Management Measures Alter Vessel Behavior in the Aleutian Archipelago

2021 ◽  
Vol 7 ◽  
Author(s):  
Benjamin K. Sullender ◽  
Kelly Kapsar ◽  
Aaron Poe ◽  
Martin Robards
Keyword(s):  
North Pacific ◽  
Risk Mitigation ◽  
Spatial Scales ◽  
Great Circle ◽  
Ecological Impacts ◽  
Study Region ◽  
The North ◽  
Management Measures ◽  
Before And After ◽  
The North Pacific

The Aleutian Archipelago and surrounding waters have enormous ecological, cultural, and commercial significance. As one of the shortest routes between North American and Asian ports, the North Pacific Great Circle Route, which crosses through the Aleutian Archipelago, is traveled by thousands of large cargo ships and tanker vessels every year. To reduce maritime risks and enhance navigational safety, the International Maritime Organization built upon earlier offshore routing efforts by designating five Areas To Be Avoided (ATBAs) in the Aleutian Islands in 2016. The ATBAs are designed to keep large vessels at least 50 nautical miles (93 km) from shore unless calling at a local port or transiting an authorized pass between islands. However, very few studies have examined the effectiveness of ATBAs as a mechanism for changing vessel behavior and thereby reducing the ecological impacts of maritime commerce. In this study, we use 4 years of satellite-based vessel tracking data to assess the effectiveness of the Aleutian ATBAs since their implementation in 2016. We determined whether vessels transiting the North Pacific Great Circle Route changed behavior after ATBA implementation, both in terms of overall route selection and in terms of compliance with each ATBA boundary. We found a total of 2,252 unique tankers and cargo vessels >400 gross tons transited the study region, completing a total of 8,794 voyages. To quantify routing changes of individual vessels, we analyzed the 767 vessels that transited the study region both before and after implementation. The percentage of voyages transiting through the boundaries of what would become ATBAs decreased from 76.3% in 2014–2015 (prior to ATBA designation) to 11.8% in 2016–2017 (after implementation). All five Aleutian ATBAs had significant increases in compliance, with the West ATBA showing the most dramatic increase, from 32.1% to 95.0%. We discuss the framework for ATBA enforcement and highlight the value of local institutional capacity for real-time monitoring. Overall, our results indicate that ATBAs represent a viable strategy for risk mitigation in sensitive ecological areas and that through monitoring, spatial protections influence vessel route decisions on multiple spatial scales.

scholarly journals Spatio-temporal modelling for non-stationary point referenced data

2021 ◽  
Author(s):  
Lindsay Morris
Keyword(s):  
Random Forest ◽  
Goodness Of Fit ◽  
Clustering Algorithm ◽  
Dependence Structure ◽  
Geostatistical Model ◽  
Study Region ◽  
Global Estimate ◽  
Covariance Functions ◽  
Power Of The Test ◽  
Spatio Temporal

<p><b>Spatial and spatio-temporal phenomena are commonly modelled as Gaussian processes via the geostatistical model (Gelfand & Banerjee, 2017). In the geostatistical model the spatial dependence structure is modelled using covariance functions. Most commonly, the covariance functions impose an assumption of spatial stationarity on the process. That means the covariance between observations at particular locations depends only on the distance between the locations (Banerjee et al., 2014). It has been widely recognized that most, if not all, processes manifest spatially nonstationary covariance structure Sampson (2014). If the study domain is small in area or there is not enough data to justify more complicated nonstationary approaches, then stationarity may be assumed for the sake of mathematical convenience (Fouedjio, 2017). However, relationships between variables can vary significantly over space, and a ‘global’ estimate of the relationships may obscure interesting geographical phenomena (Brunsdon et al., 1996; Fouedjio, 2017; Sampson & Guttorp, 1992). </b></p> <p>In this thesis, we considered three non-parametric approaches to flexibly account for non-stationarity in both spatial and spatio-temporal processes. First, we proposed partitioning the spatial domain into sub-regions using the K-means clustering algorithm based on a set of appropriate geographic features. This allowed for fitting separate stationary covariance functions to the smaller sub-regions to account for local differences in covariance across the study region. Secondly, we extended the concept of covariance network regression to model the covariance matrix of both spatial and spatio-temporal processes. The resulting covariance estimates were found to be more flexible in accounting for spatial autocorrelation than standard stationary approaches. The third approach involved geographic random forest methodology using a neighbourhood structure for each location constructed through clustering. We found that clustering based on geographic measures such as longitude and latitude ensured that observations that were too far away to have any influence on the observations near the locations where a local random forest was fitted were not selected to form the neighbourhood. </p> <p>In addition to developing flexible methods to account for non-stationarity, we developed a pivotal discrepancy measure approach for goodness-of-fit testing of spatio-temporal geostatistical models. We found that partitioning the pivotal discrepancy measures increased the power of the test.</p>

scholarly journals Evaluation of coupled ANN-GA model to prioritize flood source areas in ungauged watersheds

2020 ◽  
Vol 51 (3) ◽  
pp. 423-442
Author(s):  
Naser Dehghanian ◽  
S. Saeid Mousavi Nadoushani ◽  
Bahram Saghafian ◽  
Morteza Rayati Damavandi
Keyword(s):  
Flood Control ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Ann Model ◽  
Runoff Generation ◽  
Source Areas ◽  
Walnut Gulch ◽  
Artificial Neural Network Ann ◽  
Semi Arid ◽  
Flood Source

Abstract An important step in flood control planning is identification of flood source areas (FSAs). This study presents a methodology for identifying FSAs. Unit flood response (UFR) approach has been proposed to quantify FSAs at subwatershed and/or cell scale. In this study, a distributed ModClark model linked with Muskingum flow routing was used for hydrological simulations. Furthermore, a fuzzy hybrid clustering method was adopted to identify hydrological homogenous regions (HHRs) resulting in clusters involving the most effective variables in runoff generation as selected through factor analysis (FA). The selected variables along with 50-year rainfall were entered into an artificial neural network (ANN) model optimized via genetic algorithm (GA) to predict flood index (FI) at cell scale. The case studies were two semi-arid watersheds, Tangrah in northeastern Iran and Walnut Gulch Experimental Watershed in Arizona. The results revealed that the predicted values of FI via ANN-GA were slightly different from those derived via UFR in terms of mean squared error (MSE), mean absolute error (MAE), and relative error (RE). Also, the prioritized FSAs via ANN-GA were almost similar to those of UFR. The proposed methodology may be applicable in prioritization of HHRs with respect to flood generation in ungauged semi-arid watersheds.

scholarly journals Improved Curve Number Estimation in SWAT by Reflecting the Effect of Rainfall Intensity on Runoff Generation

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 163 ◽  
Author(s):  
Dejian Zhang ◽  
Qiaoyin Lin ◽  
Xingwei Chen ◽  
Tian Chai
Keyword(s):  
Rainfall Intensity ◽  
Assessment Tool ◽  
Flow Simulation ◽  
Daily Rainfall ◽  
Curve Number ◽  
Base Flow ◽  
Data Sets ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
The Relationship

Determining the amount of rainfall that will eventually become runoff and its pathway is a crucial process in hydrological modelling. We proposed a method to better estimate curve number by adding an additional component (AC) to better account for the effects of daily rainfall intensity on rainfall-runoff generation. This AC is determined by a regression equation developed from the relationship between the AC series derived from fine-tuned calibration processes and observed rainfall series. When incorporated into the Soil and Water Assessment Tool and tested in the Anxi Watershed, it is found, overall, the modified SWAT (SWAT-ICN) outperformed the original SWAT (SWAT-CN) in terms of stream flow, base flow, and annual extreme flow simulation. These models were further evaluated with the data sets of two adjacent watersheds. Similar results were achieved, indicating the ability of the proposed method to better estimate curve number.

scholarly journals Cluster Analysis of North Atlantic–European Circulation Types and Links with Tropical Pacific Sea Surface Temperatures

2008 ◽  
Vol 21 (15) ◽  
pp. 3687-3703 ◽  
Author(s):  
D. R. Fereday ◽  
J. R. Knight ◽  
A. A. Scaife ◽  
C. K. Folland ◽  
A. Philipp
Keyword(s):  
Cluster Analysis ◽  
North Atlantic ◽  
El Niño ◽  
Clustering Algorithm ◽  
El Nino ◽  
Tropical Pacific ◽  
Sea Surface ◽  
Optimum Number ◽  
Apparent Lack ◽  
Circulation Types

Abstract Observed atmospheric circulation over the North Atlantic–European (NAE) region is examined using cluster analysis. A clustering algorithm incorporating a “simulated annealing” methodology is employed to improve on solutions found by the conventional k-means technique. Clustering is applied to daily mean sea level pressure (MSLP) fields to derive a set of circulation types for six 2-month seasons. A measure of the quality of this clustering is defined to reflect the average similarity of the fields in a cluster to each other. It is shown that a range of classifications can be produced for which this measure is almost identical but which partition the days quite differently. This lack of a unique set of circulation types suggests that distinct weather regimes in NAE circulation do not exist or are very weak. It is also shown that the stability of the clustering solution to removal of data is not maximized by a suitable choice of the number of clusters. Indeed, there does not appear to be any robust way of choosing an optimum number of circulation types. Despite the apparent lack of preferred circulation types, cluster analysis can usefully be applied to generate a set of patterns that fully characterize the different circulation types appearing in each season. These patterns can then be used to analyze NAE climate variability. Ten clusters per season are chosen to ensure that a range of distinct circulation types that span the variability is produced. Using this classification, the effect of forcing of NAE circulation by tropical Pacific sea surface temperature (SST) anomalies is analyzed. This shows a significant influence of SST in this region on certain circulation types in almost all seasons. A tendency for a negative correlation between El Niño and an anomaly pattern resembling the positive winter North Atlantic Oscillation (NAO) emerges in a number of seasons. A notable exception is November–December, which shows the opposite relationship, with positive NAO-like patterns correlated with El Niño.

Reproductive biology of the lane snapper, Lutjanus synagris, and recommendations for its management on the Abrolhos Shelf, Brazil

2014 ◽  
Vol 94 (8) ◽  
pp. 1711-1720 ◽  
Author(s):  
Matheus Oliveira Freitas ◽  
Gecely Rodrigues Alves Rocha ◽  
Paulo De Tarso Da Cunha Chaves ◽  
Rodrigo Leão De Moura
Keyword(s):  
Reproductive Biology ◽  
Gonadosomatic Index ◽  
Size Composition ◽  
Mean Value ◽  
Study Region ◽  
Fishery Resource ◽  
North West ◽  
The North ◽  
Management Measures ◽  
Size Limits

The reproductive biology of the lane snapper, Lutjanus synagris, was evaluated from 770 specimens (434 females and 336 males) obtained on the Abrolhos Bank, eastern Brazil, between May 2005 and October 2007. Total length ranged from 14.7 to 56.0 cm for females and from 16.5 to 54.3 cm for males, with size composition not varying significantly between sexes. Five distinct maturity stages were identified based on macroscopic and histological examination of the gonads. Mean value of the gonadosomatic index (GSI) for females peaked in September and October, with a secondary peak in February and March. Histological analyses confirmed the reproductive cycle inferred by GSI variation. Asynchronous-type ovarian development was observed, and batch fecundity ranged from less than 104,743 oocytes for a 25.5 cm female of to 568,400 oocytes for a 56.0 cm female (250.0 and 2260 g, respectively), with an average of 345,700 oocytes. The reproductive parameters obtained for L. synagris in the Abrolhos Bank were similar to those reported in studies in northern Brazil and the north-west Atlantic. The species is an important fishery resource in the study region, and management measures are needed before the species becomes overfished. Exploitation occurs largely during spawning aggregations, a situation that has caused other lane snapper populations (and congeners) to decline acutely elsewhere. Our results provide support for size limits and seasonal spawning closures on the Abrolhos Bank, a region that sustains artisanal fisheries involving >20,000 fishermen.

scholarly journals Hebbian Self-Organizing Integrate-and-Fire Networks for Data Clustering

2010 ◽  
Vol 22 (1) ◽  
pp. 273-288 ◽  
Author(s):  
Florian Landis ◽  
Thomas Ott ◽  
Ruedi Stoop
Keyword(s):  
Data Clustering ◽  
Time Complexity ◽  
Arbitrary Shape ◽  
Clustering Algorithm ◽  
Hebbian Learning ◽  
Spiking Neurons ◽  
Integrate And Fire ◽  
Background Data ◽  
Homogeneous Regions ◽  
Noisy Background

We propose a Hebbian learning-based data clustering algorithm using spiking neurons. The algorithm is capable of distinguishing between clusters and noisy background data and finds an arbitrary number of clusters of arbitrary shape. These properties render the approach particularly useful for visual scene segmentation into arbitrarily shaped homogeneous regions. We present several application examples, and in order to highlight the advantages and the weaknesses of our method, we systematically compare the results with those from standard methods such as the k-means and Ward's linkage clustering. The analysis demonstrates that not only the clustering ability of the proposed algorithm is more powerful than those of the two concurrent methods, the time complexity of the method is also more modest than that of its generally used strongest competitor.

scholarly journals Pengaruh Kepuasan Kerja Terhadap Peningkatan Kinerja Karyawan

2019 ◽  
Author(s):  
Anggun Susanti
Keyword(s):  
Job Satisfaction ◽  
Performance Appraisal ◽  
Employee Performance ◽  
Effective Management ◽  
Management Measures ◽  
Number Of Factors ◽  
A Company

This study aims to analyze the effect of job satisfaction on employee performance in a company. The problem raised is whether job satisfaction has an effect on improving employee performance in a company. The results of employee performance appraisal generally differ from one employee to another. This is certainly influenced by a number of factors, so company managers must be truly able to identify and understand these factors, which are then followed by effective management measures.

scholarly journals Assessment of Precipitation Error Propagation in Multi-Model Global Water Resources Reanalysis

2018 ◽  
Author(s):  
Md Abul Ehsan Bhuiyan ◽  
Efthymios I. Nikolopoulos ◽  
Emmanouil N. Anagnostou ◽  
Clement Albergel ◽  
Emanuel Dutra ◽  
...  
Keyword(s):  
Water Resources ◽  
Surface Runoff ◽  
Land Surface ◽  
Runoff Generation ◽  
Surface Exchange ◽  
Hydrologic Modelling ◽  
Study Region ◽  
Precipitation Characteristics ◽  
Model Structures ◽  
Global Water

Abstract. This study focuses on the Iberian Peninsula and investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modelling, in global water resources reanalysis. Analysis is based on ensemble hydrologic simulations for a period spanning 11 years (2000–2010). To simulate the hydrological variables of surface runoff, subsurface runoff, and evapotranspiration, we used four land surface models—JULES (Joint UK Land Environment Simulator), ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems), SURFEX (Surface Externalisée), and HTESSEL (Hydrology-Tiled ECMWF Scheme for Surface Exchange over Land)—and one global hydrological model, WaterGAP3 (Water–Global Assessment and Prognosis). Simulations were carried out for five precipitation products—CMORPH, PERSIANN, 3B42 (V7), ECMWF reanalysis, and a machine learning-based blended product. As reference, we used a ground-based observation-driven precipitation dataset, named SAFRAN, available at 5 km/1  h resolution. We present relative performances of hydrologic variables for the different multi-model/multi-forcing scenarios. Overall, results reveal the complexity of the interaction between precipitation characteristics and different modelling schemes and show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure. Surface runoff is strongly sensitive to precipitation uncertainty and the degree of sensitivity depends significantly on the runoff generation scheme of each model examined. Evapotranspiration fluxes are comparatively less sensitive for this study region. Finally, our results suggest that there is no single model/forcing combination that can outperform all others consistently for all variables examined and thus reinforce the fact that there are significant benefits in exploring different model structures as part of the overall modelling approaches used for water resources applications.

scholarly journals GIS-Based Hydrological Modelling in the Toronto Region

2021 ◽  
Author(s):  
Riuqiu Li
Keyword(s):  
Land Cover ◽  
Curve Number ◽  
Impervious Surface ◽  
Land Cover Classification ◽  
The United States ◽  
Classification Systems ◽  
Future Research ◽  
Runoff Generation ◽  
Weight Variation ◽  
The Impact

The urbanization changes a watershed's response to precipitation. The most common effects include the reduced infiltration and the decreased travel time, which significantly increase runoff and peak discharges. This study attempts to analyze the impact of land use on runoff in the Toronto Region. In this report, the focus is on two aspects: (1) generating watershed boundaries using digital elevation model (DEM) data with the help of HEC-HMS, and (2) calculating runoff in the study area using the United States Soil Conservation Service (SCS) curve number method for the early 1990s and 2003. The study is based on the watershed boundaries generated from DEM data with 10 m resolution. Because of the flat surface in the south of the Toronto Region, the areas of the watersheds generated in this study are slightly less than the real ones, but the difference is within acceptable range. As a crucial parameter in the SCS method for runoff calculation, curve number is difficult to obtain. In this project, curve numbers for each watershed are calculated by using the land cover and soil data of the early 1990s and 2003 respectively. According to the theory, the higher the curve number is, the higher the potential of runoff generation in the area is. Unlike what is expected, the curve numbers have changed little from the early 1990s to 2003, although the impervious surface has increased. This is because the variation of the land cover is too little to increase the curve numbers. The curve number for each watershed is a weighted one. If the area of a specific lot which has changed from pervious to impervious surface is small, the weight variation of such area is also small. The other reason for the little change of curve numbers is that the land cover data sets of the early 1990s and 2003 used different classification systems. To eliminate the discrepancy resulting from those land cover classification systems, the curve numbers in 2003 were calculated by referring both classification schemes of the early 1990s and 2003. Because the land cover classification in this study is reasonable, the curve number of the Toronto Region in 2003, 80.4 can be used in the future research.

Sign in / Sign up

Export Citation Format

Share Document