IMPROVED MAPPING OF GEOMORPHIC FEATURES THROUGH MACHINE LEARNING IN THE BUFFALO RIVER WATERSHED, ARKANSAS

Abstract Chironomids from three rivers within the Niagara River Watershed were sampled and divided into two groups: those with ligulae and those with labial plates. The latter group of chironomids (i.e. the Chironomini and Tanytarsini) were classified as either normal (no deformities in their labial plates), slightly deformed (slight asymmetries due to teeth which were missing or chipped) or grossly deformed (those with extra teeth, fused teeth, crossed teeth or large gaps between their teeth). Among the 1,062 chironomids taken from the 15 sample sites, 699 possessed labial plates and 14% of these (97) displayed gross deformities in the structure of their labial plate. Most of the chironomids displaying gross deformities came from areas in the Niagara River watershed where sediments were contaminated with a number synthetic teratogens. The highest frequency of chironomid labial plate deformities (47% gross deformities) occurred at the B.F. Goodrich Company’s discharge pipe. In 1986, 33 kg of vinyl chloride, (C2H3Cl), a known carcinogen, was released into the Welland River at this site. This substance is suspected of causing the high frequency of deformities observed at this site. The second highest frequency of chironomids with gross labial plate deformities occurred in the heavily industrialized section of the Buffalo River, (Buffalo, New York). Thirty-five percent of the Chironomini and Tanytarsini at the five sampling sites in the Buffalo River displayed gross deformities as compared to 9% at the “control” site. It was concluded that the frequency of chironomid labial plate deformities provides researchers with a useful index for evaluating sediments

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Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran

Remote Sensing ◽

10.3390/rs12030475 ◽

2020 ◽

Vol 12 (3) ◽

pp. 475 ◽

Cited By ~ 19

Author(s):

Alireza Arabameri ◽

Sunil Saha ◽

Jagabandhu Roy ◽

Wei Chen ◽

Thomas Blaschke ◽

...

Keyword(s):

Machine Learning ◽

Landslide Susceptibility ◽

Learning Methods ◽

Validation Data ◽

Area Index ◽

Study Region ◽

River Watershed ◽

Machine Learning Methods ◽

Natural Break ◽

Very High

This analysis aims to generate landslide susceptibility maps (LSMs) using various machine learning methods, namely random forest (RF), alternative decision tree (ADTree) and Fisher’s Linear Discriminant Function (FLDA). The results of the FLDA, RF and ADTree models were compared with regard to their applicability for creating an LSM of the Gallicash river watershed in the northern part of Iran close to the Caspian Sea. A landslide inventory map was created using GPS points obtained in a field analysis, high-resolution satellite images, topographic maps and historical records. A total of 249 landslide sites have been identified to date and were used in this study to model and validate the LSMs of the study region. Of the 249 landslide locations, 70% were used as training data and 30% for the validation of the resulting LSMs. Sixteen factors related to topographical, hydrological, soil type, geological and environmental conditions were used and a multi-collinearity test of the landslide conditioning factors (LCFs) was performed. Using the natural break method (NBM) in a geographic information system (GIS), the LSMs generated by the RF, FLDA, and ADTree models were categorized into five classes, namely very low, low, medium, high and very high landslide susceptibility (LS) zones. The very high susceptibility zones cover 15.37% (ADTree), 16.10% (FLDA) and 11.36% (RF) of the total catchment area. The results of the different models (FLDA, RF, and ADTree) were explained and compared using the area under receiver operating characteristics (AUROC) curve, seed cell area index (SCAI), efficiency and true skill statistic (TSS). The accuracy of models was calculated considering both the training and validation data. The results revealed that the AUROC success rates are 0.89 (ADTree), 0.92 (FLDA) and 0.97 (RF) and predication rates are 0.82 (ADTree), 0.79 (FLDA) and 0.98 (RF), which justifies the approach and indicates a reasonably good landslide prediction. The results of the SCAI, efficiency and TSS methods showed that all models have an excellent modeling capability. In a comparison of the models, the RF model outperforms the boosted regression tree (BRT) and ADTree models. The results of the landslide susceptibility modeling could be useful for land-use planning and decision-makers, for managing and controlling the current and future landslides, as well as for the protection of society and the ecosystem.

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Spatial and temporal characterization of land-use in the Buffalo National River Watershed

Environmental Conservation ◽

10.1017/s0376892999000144 ◽

1999 ◽

Vol 26 (2) ◽

pp. 94-101 ◽

Cited By ~ 4

Author(s):

H.D. SCOTT ◽

T.H. UDOUJ

Keyword(s):

Land Use ◽

Environmental Changes ◽

Average Rate ◽

Land Use Changes ◽

Gis Technology ◽

Buffer Zones ◽

River Watershed ◽

Buffalo River ◽

Forest Lands

Today there is much concern about the potential contamination, overuse and development of scenic rivers in more or less pristine environments. The objective of this work was to quantify the spatial and temporal changes in land-use occurring in a watershed draining a nationally protected river. The Buffalo National River of Arkansas was chosen to serve as an example of how Geographic Information System (GIS) technology can rapidly assess the environmental changes that have occurred within a watershed. GIS was used to develop a spatial database of the watershed and to describe differences in land-use data from five sample years during 1965–92. Over this time span, approximately 40 000 ha of forest were lost and converted primarily to pasture. The average rate of loss of forest was 1480 ha/yr. During this same period, the average rate of gain of pasture was 1381 ha/yr. Buffer analyses showed that pasture increased at a higher percentage rate in the buffer zones surrounding the Buffalo River than in the tributaries of the Buffalo River, and a large proportion of the increase was on higher slopes. Land-use changes were dynamic with a greater area converted to pasture than area of pasture converted to forest in the watershed. The cleared forest lands were mostly near older pastures and along streams. The reforested lands tended to occur in the more isolated areas. The Buffalo River Watershed has undergone changes in land-use that may have had impact on the water quality of the region.

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Water resources of the Buffalo River Watershed, West-central Minnesota

10.3133/ha307 ◽

1969 ◽

Keyword(s):

Water Resources ◽

River Watershed ◽

West Central ◽

Buffalo River

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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