Classification of Kelantan watershed using mapwindow GIS integrated with soil and water analysis tool (MWSWAT)

Erosion in the watershed generally occurs due to land use that ignores the rules of soil and water conservation. There is much activity carried out by people living on land in the Yeh Leh watershed area, which makes the level of dependence is very enormous.The erosion forecast is using the USLE (Universal Soil Loss Equation) to estimate the erosion swift occurs and to obtain illustration in determining the precise soil and water measures in a region. The determination of land capability classification is using Arsyad’s method (1989) in which to classify the land ability by classifying the land ability class based on the value of land limiting factors, which then adjusted to the criteria of classification of land capability. The land use directional determination is applying the scoring method where combining field slope factor, soil sensitivity to erosion and daily rainfall intensity. The erosion level of the YehLeh watershed area is categorized as mild to very severe. A very light erosion level as large as 515 ha (21.01%), with the land use in the form of irrigated rice field and forest. The severe erosion level as large as 990.02 ha (40.40%) with land use in the form of plantations. The very heavy erosion level as large as 945.82 ha (38.59%) with land use in the form of plantations. The classification of land capability in the YehLeh watershed area consists of 5 classes of land abilities: class II of 115, 22 ha (4.70%), class III of 533.95 ha (21.79%), class IV of 423.61 (17.28%), Class VI of 1,102.03 ha (44.97%), and Class VII of 276.03 ha (11.26%), with some limiting factors for instance, soil texture, erosion and drainage. Proposed land use in the YehLeh watershed area use for forest areas is as protected forest of 456.49 ha (18.63%). Proposed land use outside of the forest area consist of 58.51 ha (2.39%) of seasonal crops, annual cultivation area of 990.02 ha (40.40%) and buffer area of 945.82 (38.59%). Keywords: watershed, erosion, land capability classification, proposed land use.

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Classification of Tropical Oceanic Precipitation using High-Altitude Aircraft Microwave and Electric Field Measurements

Journal of the Atmospheric Sciences ◽

10.1175/jas3606.1 ◽

2006 ◽

Vol 63 (1) ◽

pp. 218-233 ◽

Cited By ~ 16

Author(s):

Robbie E. Hood ◽

Daniel J. Cecil ◽

Frank J. LaFontaine ◽

Richard J. Blakeslee ◽

Douglas M. Mach ◽

...

Keyword(s):

Electric Field ◽

High Altitude ◽

Field Measurements ◽

Analysis Tool ◽

Western Atlantic ◽

Lower Altitude ◽

Field Information ◽

Uninhabited Aerial Vehicles ◽

Electric Field Measurements

Abstract During the 1998 and 2001 hurricane seasons of the western Atlantic Ocean and Gulf of Mexico, the Advanced Microwave Precipitation Radiometer (AMPR), the ER-2 Doppler (EDOP) radar, and the Lightning Instrument Package (LIP) were flown aboard the NASA ER-2 high-altitude aircraft as part of the Third Convection and Moisture Experiment (CAMEX-3) and the Fourth Convection and Moisture Experiment (CAMEX-4). Several hurricanes, tropical storms, and other precipitation systems were sampled during these experiments. An oceanic rainfall screening technique has been developed using AMPR passive microwave observations of these systems collected at frequencies of 10.7, 19.35, 37.1, and 85.5 GHz. This technique combines the information content of the four AMPR frequencies regarding the gross vertical structure of hydrometeors into an intuitive and easily executable precipitation mapping format. The results have been verified using vertical profiles of EDOP reflectivity and lower-altitude horizontal reflectivity scans collected by the NOAA WP-3D Orion radar. Matching the rainfall classification results with coincident electric field information collected by the LIP readily identifies convective rain regions within the precipitation fields. This technique shows promise as a real-time research and analysis tool for monitoring vertical updraft strength and convective intensity from airborne platforms such as remotely operated or uninhabited aerial vehicles. The technique is analyzed and discussed for a wide variety of precipitation types using the 26 August 1998 observations of Hurricane Bonnie near landfall.

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Time Series Analysis Using Digital Classification of Data on Land Use and Land Cover in Dohuk Governorate

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/910/1/012124 ◽

2021 ◽

Vol 910 (1) ◽

pp. 012124

Author(s):

Mohammed Younis Salim ◽

Narmin Abduljaleel Ibrahim

Keyword(s):

Land Cover ◽

Classification Accuracy ◽

Urban Areas ◽

Bare Soil ◽

Error Matrix ◽

Ndvi Index ◽

Open Forests ◽

Soil And Water

Abstract This study deals with the analysis and detection of changes in land cover patterns and land uses, especially forests in Amadiya district in Dohuk Governorate. It carred out in northern of Iraq by area is (2775.21) km2 and the district is located astronomically between longitudes (01/04 ° 43), (17/08 ° 44), it extends between two circles of latitude, which are (16/50 ° 36) and ('30.'21 ° 37) north, during the periods (1999-2006-2013-2019). Application of the Supervised Classification and the detection of change over time in a comparative manner and by relying on the satellite images of the Land sat ETM satellite were used. The Landsat OLI satellite with a distinctive capacity of 30 meters in the Arc map 10.6.1 program, and one of the indicators of environmental degradation in the land cover patterns, which is the NDVI index for all study periods, was used to reveal the role of natural and human factors that lead to changes in the land cover patterns in the study area. The classification revealed the existence of five types of common land cover, which included dense forests, open forests, urban areas, bare soil and water, which showed clear changes in these land coverings during the period from 1999 to 2019, which were represented by a decrease in forests, bare soil and water by a percentage of (54.76601%), (5.212329%), (2.149469%) respectively, while the Dense and urban areas by (16.35919%) and (21.51301%) in 2019, respectively. The classification accuracy of the Spatial indication was estimated based on the error matrix from there we found that the accuracy was (93.29%) this indicates that the classification accuracy is very good It is acceptable and can relied upon and recommended for classification.

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Soils and Fertilizers for Master Gardeners: The Soil Profile and Soil Classification

EDIS ◽

10.32473/edis-mg445-2008 ◽

2008 ◽

Vol 2008 (6) ◽

Author(s):

Amy L. Shober

Keyword(s):

Soil Profile ◽

Soil Classification ◽

Natural Conditions ◽

Fact Sheet ◽

Water Science ◽

Soil And Water

SL-260, a 3-page illustrated fact sheet by Amy L. Shober, provides information about the characteristics and classification of soils as found in the landscape under natural conditions. It is part of a series entitled Soils and Fertilizers for Master Gardeners. Includes references. Published by the UF Department of Soil and Water Science, June 2008.

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KDClassifier: A urinary proteomic spectra analysis tool based on machine learning for the classification of kidney diseases

Aging Pathobiology and Therapeutics ◽

10.31491/apt.2021.09.064 ◽

2021 ◽

Vol 3 (3) ◽

pp. 63-72

Author(s):

Wanjun Zhao ◽

Keyword(s):

Kidney Disease ◽

Kidney Diseases ◽

Confusion Matrix ◽

Gradient Boosting ◽

Support Vector ◽

Diagnostic Model ◽

Analysis Tool ◽

Data Set ◽

Extreme Gradient Boosting

Background: We aimed to establish a novel diagnostic model for kidney diseases by combining artificial intelligence with complete mass spectrum information from urinary proteomics. Methods: We enrolled 134 patients (IgA nephropathy, membranous nephropathy, and diabetic kidney disease) and 68 healthy participants as controls, with a total of 610,102 mass spectra from their urinary proteomic profiles. The training data set (80%) was used to create a diagnostic model using XGBoost, random forest (RF), a support vector machine (SVM), and artificial neural networks (ANNs). The diagnostic accuracy was evaluated using a confusion matrix with a test dataset (20%). We also constructed receiver operating-characteristic, Lorenz, and gain curves to evaluate the diagnostic model. Results: Compared with the RF, SVM, and ANNs, the modified XGBoost model, called Kidney Disease Classifier (KDClassifier), showed the best performance. The accuracy of the XGBoost diagnostic model was 96.03%. The area under the curve of the extreme gradient boosting (XGBoost) model was 0.952 (95% confidence interval, 0.9307–0.9733). The Kolmogorov-Smirnov (KS) value of the Lorenz curve was 0.8514. The Lorenz and gain curves showed the strong robustness of the developed model. Conclusions: The KDClassifier achieved high accuracy and robustness and thus provides a potential tool for the classification of kidney diseases

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The Type of Coliform Bacilli Prevalent in Urine and their Significance, with Special Reference to the Sanitary Aspects

Epidemiology and Infection ◽

10.1017/s0022172400018854 ◽

1933 ◽

Vol 33 (4) ◽

pp. 510-515 ◽

Cited By ~ 6

Author(s):

H. J. O'D. Burke-Gaffney

Keyword(s):

Water Sample ◽

Environmental Conditions ◽

Coliform Bacteria ◽

Methyl Red ◽

Biological Behaviour ◽

Large Numbers ◽

Predominant Type ◽

Principal Value ◽

Soil And Water

1. 1000 urinary and 500 faecal cultures of coliform bacteria were studied by means of the methyl-red, citrate and indol tests and also by their fermentation reactions upon saccharose and dulcite.2. 8 per cent, of the faecal strains and 52 per cent, of the urinary strains were of theaerogenestype,i.e.methyl-red negative and citrate positive. Indol was produced in 94 per cent, and 43 per cent, of cases respectively.3. This would suggest additional evidence that thecoligroup are quickly outnumbered by theaerogenesgroup once they leave the faeces, and also that the presence of theaerogenestype is not confined even in large numbers to an extra-corporeal habitat.4. In water bacteriology the constancy or change in the relative proportions ofcoliandaerogenesstrains present is the goal at which to aim. Theaerogenesvariety cannot of itself be regarded as non-excretal or even non-faecal.5. From a sanitary standpoint, the classification of coliform bacteria by means of the methyl-red and citrate tests cannot be regarded as entirely free from error so long as (a) theaerogenesstrains are found in faeces, in however small numbers, (b) these strains occur as the predominant type in urine, and (c) intermediate strains are found in the faeces, urine, soil and water.6. The principal value of these tests lies in their comparative specificity in identifyingB. coliof immediate faecal origin. The presence of such an organism, interpreted together with the indol test, may be said to suggest dangerous faecal pollution if found prominently in a water sample. The presence of other types identified by the same tests cannot be regarded as having the same significant value in regard to a negative opinion.7. A plea is made for a further study of coliform bacteria by means of the tests described in relation to the biological behaviour of the bacteria under different environmental conditions.

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Visual Analysis of Sorting and Classification of Multidimensional Data

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800142155003x ◽

2020 ◽

pp. 2155003

Author(s):

Dongsheng Yang ◽

Shidong Yu ◽

Ying Hao

Keyword(s):

Visual Analysis ◽

Visual Display ◽

Complete Classification ◽

Multidimensional Data ◽

Analysis Tool ◽

Interactive Analysis ◽

Attribute Weights ◽

Correlation Structures ◽

Better Than

An important work of data analysis is to identify correlation structures and classify the data in unlabeled high-dimensional data, which usually requires iterative experiments on clustering parameters, attribute weights and instances. For a large dataset, the number of clusters may be huge, and it is a great challenge to explore in this huge space. People usually have a more comprehensive understanding of some data. For example, they think that data A is better than data B, but they do not know which attributes are important. Therefore, a powerful interactive analysis tool can help people greatly improve the effectiveness of exploratory clustering analysis. This paper provides a visual analysis method for sorting and classifying multivariate data. It can determine the weight of each attribute through user’s interaction, thus, generating sorting, and then complete classification according to sorting results. Through visual display, users can understand the characteristics of data as well as category characteristics intuitively and quickly, and it helps users improve sorting and classification results.

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