Woody-biomass is an important indicator for good vegetation cover, and a healthy environment that provides ecosystems services such as supplying, provisioning, regulating, esthetic, and cultural values. Moreover, wood...
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Woody-biomass is an important indicator for good vegetation cover, and a healthy environment that provides ecosystems services such as supplying, provisioning, regulating, esthetic, and cultural values. Moreover, woody-biomass stores a very good amount of carbon dioxide as about fifty percent of living organisms are built from carbon. The objective of this study is to estimate the above and belowground woody biomass and carbon stock of Kunzila watershed, Northwest Ethiopia as a piece of baseline information so as to gage the changes after the intervention. The watershed covers about 11,200 ha of land with undulating topographic landscapes. More than 230 sample plots of 30 m by 30 m were surveyed and every woody plant (shrub/bush and tree) was measured. Allometric equations were applied to calculate biomass and carbon stock potential at the plot level. The random forest algorithm was applied to upscale plot values to watershed level. The results indicate that Kunzila watershed offers maximum biomass of 1,756(+/- 249) Mt year(-1)/watershed and corresponding carbon dioxide equivalent (CO(2)e) of 3,022(+/- 429) Mt C year(-1) /watershed with the current vegetation status;while the average woody biomass and CO(2)e were estimated about 244(+/- 249) Mt ha(-1) year(-1) and 425(+/- 429) Mt C ha(-1) year(-1), respectively. These results indicate that there are high variabilities among different ecosystem types and also the watershed generally offers below its potential. However, there is a high potential to achieve up to triples of biomass and CO(2)e by implementing recommended intervention plans developed out of the current study. The current approach using machine learning algorithm to upscale from sample plot-level to watershed scale is a very good one and can be applied to other watersheds at scale.
Wireless sensor networks (WSNs) are widely utilized in various applications due to their compact size, cost-effectiveness, and ease of deployment. Nonetheless, one of the biggest problems in WSNs is getting a reasonab...
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In the Northern highlands of Ethiopia, Eucalyptus plantations have been widely established since the 1970s. However, there have been debates and concerns about the impact of these plantations on ecosystem services. Th...
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In the Northern highlands of Ethiopia, Eucalyptus plantations have been widely established since the 1970s. However, there have been debates and concerns about the impact of these plantations on ecosystem services. Therefore, the study aimed to assess the impact of monoculture Eucalyptus plantations on ecosystem service values (ESVs) in the upper Blue Nile part of Ethiopia. We used Landsat satellite images (captured in 1993, 2004, 2014, and 2023) for land use/cover assessment. The images were classified using the randomforest (RF) algorithm in the R open-source software. For ecosystem services valuation, we used the benefit transfer method, which allowed us to estimate ESVs by applying both global and local value transfer coefficients. The results show a significant expansion of Eucalyptus plantations, with the highest net change (180%) between 1991 and 2023. The estimated global ESVs ranged from US$206 million in 1991 to US$208 million in 2023, with croplands contributing the largest share (75%). However, specific ESVs related to nutrient cycling, habitat refuge, pollination, and culture declined due to decreasing ESVs of forest land, shrub land, and grazing land over the study period (1991-2023). In addition, ESVs associated with food production declined in the latter period (2014-2023) due to the conversion of croplands into Eucalyptus plantations and shrub lands. This study highlights the need for informed decision-making in land use systems, considering the trade-off between increasing productivity and the loss of other ecosystem functions and services.
Evapotranspiration (ET) is a critical element of the hydrological cycle, and its proper assessment is essential for irrigation scheduling, agricultural and hydro-meteorological studies, and water budget estimation. It...
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Evapotranspiration (ET) is a critical element of the hydrological cycle, and its proper assessment is essential for irrigation scheduling, agricultural and hydro-meteorological studies, and water budget estimation. It is computed for most applications as a product of reference crop evapotranspiration (ET0) and crop coefficient, notably using the well-known two-step method. Accurate predictions of reference evapotranspiration (ET0) using limited meteorological inputs are critical in data-constrained circumstances. Due to the unavailability and heterogeneity of broad parameters of the FAO PM method, it becomes a major constraint for accurately estimating ET0. To overcome the complexity of calculation, the present study was focused on developing a randomforest-based (RF) ET0 model to estimate the crop ET for the semi-arid region of northwest India. The RF-based model was developed by focusing on the easily available data at the farm level. For comparative study existing models like Hargreaves-Samani, Modified Penman and modified Hargreaves-Samani were used to estimate the ET0. The models' calibration and validation were done using meteorological data collected from the weather station of Punjab Agricultural University for 21 years (1990-2010) and nine years (2011-2019), respectively, and the FAO PM model was taken as a standard. The mean absolute error (MAE) and root-mean-square error (RMSE) were found to be least as 0.95 mm/d and 1.32 mm/d, respectively for the developed RF model, with an r(2) value of 0.92. The seasonal ET0 estimated by modified Hargreaves-Samani (MHS) and RF were found as 498.3, 482.1 mm in rabi season and 755, 744.8 mm in kharif season respectively, whereas the annual ET0 was 1380.2 and 1355.7 mm respectively. The predicted ET0 values by RF-based model were used for irrigation scheduling of two growing seasons (2020-2021) of maize and wheat crops. The outcome of the field trial also demonstrates that there was no appreciable yield drop in the crop
Sports analytics has benefited immensely from the growth and popularity of Machine Learning algorithms. Machine Learning and Data Mining advances have enabled sports analysts to evaluate a player's performance mor...
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This paper presents Utility of Sentiment Analysis on E-Commerce Application. Opinion(sentiment) mining is one of the most significant tasks of natural language processing. Used to identify about what people have an im...
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This paper presents Utility of Sentiment Analysis on E-Commerce Application. Opinion(sentiment) mining is one of the most significant tasks of natural language processing. Used to identify about what people have an impression about their services and *** mining techniques use, transform, tokenize, removing of stopword, removing special characters, classification, etc. In this research, we propose different machine learning algorithms to analyses customer *** this project we use logistic regression, naive bayes, random forest algorithm. to evaluate the proposed sentiment analysis system, we perform an experiment using 31,662 product review. Evaluation metrics is also used to support in making comparison of Logistic Regression, Naive Bayes Classifier and randomforest approaches Using technique of Bag of Word and TFIDF feature extraction integrated with n-grams model to find the best accuracy. The highest result of this study is the logistic regression approach by using Bag of Word feature extraction with unigram model with a precision of 99%, recall 100% and F1 score of 99 of 90%-10% data splitting ratio.
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