scholarly journals Modelling and forecasting reservoir sedimentation of irrigation dams in the Guinea Savannah Ecological Zone of Ghana

2021 ◽  
Author(s):  
Thomas Apusiga Adongo ◽  
Felix K. Abagale ◽  
Wilson A. Agyare
Keyword(s):  
Control Measures ◽  
Annual Rainfall ◽  
Reservoir Sedimentation ◽  
Back Propagation Algorithm ◽  
Ecological Zone ◽  
Exponential Regression ◽  
Sediment Volume ◽  
Feed Forward Back Propagation ◽  
Input Parameters ◽  
Annual Sediment

Abstract Effective management of reservoir sedimentation requires models which can predict sedimentation of the reservoirs. In this study, linear regression, non-linear exponential regression and artificial neural network models have been developed for the forecasting of annual storage capacity loss of reservoirs in the Guinea Savannah Ecological Zone (GSEZ) of Ghana. Annual rainfall, inflows, trap efficiency and reservoir age were input parameters for the models whilst the output parameter was the annual sediment volume in the reservoirs. Twenty (20) years of reservoirs data with 70% data used for model training and 30% used for validation. The ANN model, the feed-forward, back-propagation algorithm Multi-Layer Perceptron model structure which best captured the pattern in the annual sediment volumes retained in the reservoirs ranged from 4-6-1 at Karni to 4-12-1 at Tono. The linear and nonlinear exponential regression models revealed that annual sediment volume retention increased with all four (4) input parameters whilst the rate of sedimentation in the reservoirs is a decreasing function of time. All the three (3) models developed were noted to be efficient and suitable for forecasting annual sedimentation of the studied reservoirs with accuracies above 76%. Forecasted sedimentation up to year 2038 (2019–2038) using the developed models revealed the total storage capacities of the reservoirs to be lost ranged from 13.83 to 50.07%, with 50% of the small and medium reservoirs filled with sediment deposits if no sedimentation control measures are taken to curb the phenomenon.

scholarly journals Modeling of Rainfall-Runoff Correlations Using Artificial Neural Network-A Case Study of Dharoi Watershed of a Sabarmati River Basin, India

2017 ◽  
Vol 3 (2) ◽  
pp. 78-87 ◽  
Author(s):  
Ajaykumar Bhagubhai Patel ◽  
Geeta S. Joshi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
River Basin ◽  
Annual Rainfall ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Feed Forward Back Propagation ◽  
Sabarmati River ◽  
Artificial Neural

The use of an Artificial Neural Network (ANN) is becoming common due to its ability to analyse complex nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. This capability could efficiently be employed for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in nature. Artificial Neural Networks (ANN) can be used in cases where the available data is limited. The present work involves the development of an ANN model using Feed-Forward Back Propagation algorithm for establishing monthly and annual rainfall runoff correlations. The hydrologic variables used were monthly and annual rainfall and runoff for monthly and annual time period of monsoon season. The ANN model developed in this study is applied to Dharoi reservoir watersheds of Sabarmati river basin of India. The hydrologic data were available for twenty-nine years at Dharoi station at Dharoi dam project. The model results yielding into the least error is recommended for simulating the rainfall-runoff characteristics of the watersheds. The obtained results can help the water resource managers to operate the reservoir properly in the case of extreme events such as flooding and drought.

AN ARTIFICIAL INTELLIGENT APPROACH TO TRAFFIC ACCIDENT ESTIMATION: MODEL DEVELOPMENT AND APPLICATION

Transport ◽  
2009 ◽  
Vol 24 (2) ◽  
pp. 135-142 ◽  
Author(s):  
Ali Payıdar Akgüngör ◽  
Erdem Doğan
Keyword(s):  
Genetic Algorithm ◽  
Model Development ◽  
Back Propagation ◽  
Linear Functions ◽  
Model Parameters ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Estimation Model ◽  
Average Growth ◽  
Feed Forward Back Propagation

This study proposes an Artificial Neural Network (ANN) model and a Genetic Algorithm (GA) model to estimate the number of accidents (A), fatalities (F) and injuries (I) in Ankara, Turkey, utilizing the data obtained between 1986 and 2005. For model development, the number of vehicles (N), fatalities, injuries, accidents and population (P) were selected as model parameters. In the ANN model, the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. In the GA approach, two forms of genetic algorithm models including a linear and an exponential form of mathematical expressions were developed. The results of the GA model showed that the exponential model form was suitable to estimate the number of accidents and fatalities while the linear form was the most appropriate for predicting the number of injuries. The best fit model with the lowest mean absolute errors (MAE) between the observed and estimated values is selected for future estimations. The comparison of the model results indicated that the performance of the ANN model was better than that of the GA model. To investigate the performance of the ANN model for future estimations, a fifteen year period from 2006 to 2020 with two possible scenarios was employed. In the first scenario, the annual average growth rates of population and the number of vehicles are assumed to be 2.0 % and 7.5%, respectively. In the second scenario, the average number of vehicles per capita is assumed to reach 0.60, which represents approximately two and a half‐fold increase in fifteen years. The results obtained from both scenarios reveal the suitability of the current methods for road safety applications.

scholarly journals Epidemiology of Epizootic Lymphangitis Among Carthorses in Ethiopia

2021 ◽  
Vol 8 ◽  
Author(s):  
Musse G. Abdela ◽  
Sori Teshale ◽  
Mesfin M. Gobena ◽  
Aboma Zewde ◽  
Hawi Jaleta ◽  
...  
Keyword(s):  
Histoplasma Capsulatum ◽  
Neck Region ◽  
Cross Sectional Study ◽  
Control Measures ◽  
Annual Rainfall ◽  
Economic Losses ◽  
Mean Annual Temperature ◽  
Cross Sectional ◽  
Effective Prevention ◽  
Increased Risk

Epizootic lymphangitis caused by Histoplasma capsulatum variety farciminosum is a debilitating disease incurring considerable economic losses and affecting the welfare of carthorses. Understanding of its epidemiology is important for devising effective prevention and control measures. A cross-sectional study was conducted on 4,162 carthorses in 17 towns in Ethiopia between October 2018 and June 2019. Clinical and microscopic examinations, fungal culturing, and polymerase chain reaction were used to undertake this study. The overall prevalence of epizootic lymphangitis was 16.67% (95% CI: 15.55–17.84) in carthorses. Epizootic lymphangitis was detected in carthorses found in 16 of the 17 towns included in the study. The highest prevalence was recorded at Kombolcha Town (33.33; 95% CI: 27.54–39.52) whereas the lowest was recorded at Debre Birhan Town (0.00; 95% CI: 0.00–1.27). The results of univariable firth logistic regression analysis showed that the difference between the prevalence of Kombolcha and the prevalences of all the other towns except Holota and Shashemene were statistically significant. Statistically significantly lower prevalence was observed in other towns. Classification of the cases into different clinical forms showed that 87.18, 4.33, and 0.58% were cutaneous, ocular, and respiratory forms respectively, while the remaining 7.93% (55/694; 95% CI: 6.03–10.19) were classified as mixed form. In terms of the severity of the disease, 28.67, 60.52, and 0.81% were mild, moderate, and severe cases, respectively. The majority of the lesions (43.95%) were observed in the skin followed by forelimbs (14.55%) and neck region (14.27%). Higher mean annual temperature, lower annual rainfall, and higher humidity of the study towns were statistically significantly associated with an increased risk of epizootic lymphangitis. In conclusion this study revealed widespread occurrence of epizootic lymphangitis in carthorses yet a heterogeneous prevalence between towns. The veterinary and livestock authorities should take this into account while devising disease control.

scholarly journals Sediment balance of a cascade of alpine reservoirs based on multi-decadal data records

2018 ◽  
Vol 40 ◽  
pp. 03012
Author(s):  
Sebastián Guillén Ludeña ◽  
Pedro Manso ◽  
Anton J. Schleiss ◽  
Benno Schwegler ◽  
Jan Stamm ◽  
...  
Keyword(s):  
Sedimentation Rate ◽  
Reservoir Sedimentation ◽  
Sediment Fluxes ◽  
Fine Sediments ◽  
Sediment Balance ◽  
Operational Management ◽  
Human Use ◽  
Bottom Outlets ◽  
Total Sedimentation ◽  
Annual Sediment

Reservoir sedimentation is a major concern in the operational management of dams and appurtenant structures. The increasing volume of sediments deposited in reservoirs leads to a loss of water storage, undermining the purpose itself of the dam for human use or protection. The deposition of sediments (mostly fine) in the vicinity of the dam’s operational structures, such as bottom outlets and power intakes, may result in partial or total blockage of these structures. To cope with these problems, it is essential to determine the sediment balance of the reservoirs, by assessing the origin and quantity of the in- and out-fluxes of sediments. This paper presents a methodology to determine the annual sediment balance of a system of interlinked reservoirs across several decades, as well as its application to the alpine hydropower cascade formed by the Oberaar, Grimsel and Räterichsboden reservoirs located in Switzerland. At that aim, the annual sediment fluxes and the sedimentation rates of each reservoir were characterized. Also, the percentage of fine sediments (dm < 10 μm) included in the total sedimentation rate was estimated. The results reveal that the annual sedimentation rate of the lowermost reservoir of the system (Räterichsboden) is highly altered by the flushing operations of the reservoir upstream (Grimsel). Also, for the uppermost reservoir of the system (Oberaar), the volume of fine sediments deposited annually can reach up to 46% of the total sedimentation rate.

Application of Back Propagation Neural Network Algorithms on Modeling Failure of B-737 Bleed Air System Valves in Desert Conditions

2012 ◽  
Vol 225 ◽  
pp. 505-510 ◽  
Author(s):  
Wael G. Abdelrahman ◽  
Ahmed Z. Al-Garni ◽  
Waheed Al-Wadiee
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Aircraft Engine ◽  
Weibull Model ◽  
Back Propagation Neural Network ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Failure Data ◽  
Feed Forward Back Propagation ◽  
Air System

Accurate life prediction of aircraft engine components is very critical because it has a direct impact on aircraft safety and on operators’ profits. The engine bleed air system valves have considerably high failure rates when the engines are operated in desert conditions because of sand particles erosion and blockage. In this work, an Artificial Neural Network (ANN) model for the prediction of failure rate of the most important of these valves in Boeing 737 engines is developed and validated. A previously developed feed-forward back-propagation algorithm is implemented to train the ANN. The effects of changing the number of neurons in the input layer, the number of neurons in the hidden layer, the rate of learning, and the momentum constant are investigated. The model results are validated using comparisons with actual valves failure data from a local operator in Saudi Arabia, as well as comparisons with classical Weibull model results.

scholarly journals FORECASTING DEMAND FOR LOW COST CARRIERS IN AUSTRALIA USING AN ARTIFICIAL NEURAL NETWORK APPROACH

Aviation ◽  
2015 ◽  
Vol 19 (2) ◽  
pp. 90-103 ◽  
Author(s):  
Panarat Srisaeng ◽  
Glenn S. Baxter ◽  
Graham Wild
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Model Parameters ◽  
Back Propagation Algorithm ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Real Gdp ◽  
Feed Forward Network ◽  
Artificial Neural

This study focuses on predicting Australia‘s low cost carrier passenger demand and revenue passenger kilometres performed (RPKs) using traditional econometric and artificial neural network (ANN) modelling methods. For model development, Australia‘s real GDP, real GDP per capita, air fares, Australia‘s population and unemployment, tourism (bed spaces) and 4 dummy variables, utilizing quarterly data obtained between 2002 and 2012, were selected as model parameters. The neural network used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. The ANN was applied during training, testing and validation and had 11 inputs, 9 neurons in the hidden layers and 1 neuron in the output layer. When comparing the predictive accuracy of the two techniques, the ANNs provided the best prediction and showed that the performance of the ANN model was better than that of the traditional multiple linear regression (MLR) approach. The highest R-value for the enplaned passengers ANN was around 0.996 and for the RPKs ANN was round 0.998, respectively.

scholarly journals Morphodynamic development and sediment budget of the Dutch Wadden Sea over the last century

2012 ◽  
Vol 91 (3) ◽  
pp. 293-310 ◽  
Author(s):  
E.P.L. Elias ◽  
A.J.F. van der Spek ◽  
Z.B. Wang ◽  
J. de Ronde
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Wadden Sea ◽  
Control Measures ◽  
Tidal Basin ◽  
Sediment Volume ◽  
Tidal Basins ◽  
Present Rate ◽  
Continuous Sedimentation ◽  
Equilibrium Morphology

AbstractThe availability of nearly 100 years of bathymetric measurements allows the analysis of the morphodynamic evolution of the Dutch Wadden Sea under rising sea level and increasing human constraint. The historically observed roll-over mechanisms of landward barrier and coastline retreat cannot be sustained naturally due to numerous erosion control measures that have fixed the tidal basin and barrier dimensions. Nevertheless, the large continuous sedimentation in the tidal basins (nearly 600 million m3), the retained inlets and the similar channel-shoal characteristics of the basins during the observation period indicate that the Wadden Sea is resilient to anthropogenic influence, and can import sediment volumes even larger than those needed to compensate the present rate of sea-level rise. The largest sedimentation occurs in the Western Wadden Sea, where the influence of human intervention is dominant. The large infilling rates in closed-off channels, and along the basin shoreline, rather than a gradual increase in channel flat heights, render it likely that this sedimentation is primarily a response to the closure of the Zuiderzee and not an adaptation to sea-level rise. Most of the sediments were supplied by the ebb-tidal deltas. It is, however, unlikely that the sediment volume needed to reach a new equilibrium morphology in the Western Wadden Sea can be delivered by the remaining ebb-tidal deltas alone.

scholarly journals Future impacts of climate change on sediment influx rate in hydropower reservoir using SWAT

2021 ◽  
Vol 880 (1) ◽  
pp. 012024
Author(s):  
A Z Abdul Razad ◽  
S H Shamsuddini ◽  
A Setu ◽  
L Mohd Sidek
Keyword(s):  
Climate Change ◽  
Sediment Yield ◽  
Sediment Load ◽  
Assessment Tool ◽  
Annual Rainfall ◽  
Historical Period ◽  
Catchment Management ◽  
Influx Rate ◽  
Control Plan ◽  
Annual Sediment

Abstract Climate change causes more frequent and intense rainfall events, leading to severe erosion in the catchment and sediment transferred into rivers and reservoirs. This study focus on long term sediment load in major rivers in Cameron Highlands and prediction of annual sediment inflow into Ringlet Reservoir from 2000 to 2030. Soil Water Assessment Tool (SWAT) is used as the simulation tool, utilising future gridded rainfall 2017 to 2030 under CCSM and future land use 2030. Future annual rainfall is minimum at 1551 mm (in 2030) and maximum at 3150 mm (in 2029). The future projected annual sediment load into Ringlet Reservoir from 2017 to 2030 is averaged at 354,013 m3/year, ranging from 216,981 to 461,886 m3/year. Comparing between the historical period of 2000 to 2016 and future projection (2017–2030), annual sediment load shows an increase of 12 %. To combat the increase sediment yield, catchment management such as erosion control plan, drainage and runoff control must be developed to minimise sediment yield and subsequent effect of high sediment load transport via rivers and drainage network.

scholarly journals Prediction of the thermo-physical properties of deep-fat frying plantain chips (ipekere) using artificial neural network

2021 ◽  
Vol 25 (2) ◽  
pp. 253-260
Author(s):  
James Abiodun Adeyanju ◽  
John Oluranti Olajide ◽  
Emmanuel Olusola Oke ◽  
Jelili Babatunde Hussein ◽  
Chiamaka Jane Ude
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Physical Properties ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Deep Fat Frying ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Thermo Physical Properties

Abstract This study uses artificial neural network (ANN) to predict the thermo-physical properties of deep-fat frying plantain chips (ipekere). The frying was conducted with temperature and time ranged of 150 to 190 °C and 2 to 4 minutes using factorial design. The result revealed that specific heat was most influenced by temperature and time with the value 2.002 kJ/kg°C at 150 °C and 2.5 minutes. The density ranged from 0.997 – 1.005 kg/m3 while thermal diffusivity and conductivity were least affected with 0.192 x 10−6 m2/s and 0.332 W/m°C respectively at 190 °C and 4 minutes. The ANN architecture was developed using Levenberg–Marquardt (TRAINLM) and Feed-forward back propagation algorithm. The experimentation based on the ANN model produced a desirable prediction of the thermo-physical properties through the application of diverse amount of neutrons in the hidden layer. The predictive experimentation of the computational model with R2 ≥ 0.7901 and MSE ≤ 0.1125 does not only show the validity in anticipating the thermo-physical properties, it also indicates the capability of the model to identify a relevant association between frying time, frying temperatures and thermo-physical properties. Hence, to avoid a time consuming and expensive experimental tests, the developed model in this study is efficient in prediction of the thermo-physical properties of deep-fat frying plantain chips.

scholarly journals Analysis of Hydro-Meteorological Variables and Runoff Characteristics in the Sudano-Sahelian Ecological Zone of Nigeria

2020 ◽  
pp. 331-341
Author(s):  
Samsideen Ojoye ◽  
I. P. Ifabiyi ◽  
Ishiaku Ibrahim
Keyword(s):  
Influencing Factor ◽  
Annual Rainfall ◽  
Ecological Zone ◽  
Wet Season ◽  
Upward Trend ◽  
Significant Relationships ◽  
Rivers And Lakes ◽  
Hydrological Services ◽  
The Impact ◽  
The Relationship

This study examines the impact of climate change on hydrologic resources of selected rivers and lakes in the Sudano- Sahelian Ecological Zone of Nigeria. Climatologically data acquired were rainfall, temperature and evaporation from Nigeria Meteorological Agency, Oshodi, Lagos. Similarly, the hydrological data of river discharge and lake levels were obtained from Nigeria Hydrological Services, Kaduna. We used the Standardised Anomaly Index to test for fluctuations in rainfall, temperature, runoff and water level in lakes. Mann Kendall statistics were used to examine the trends in the climate variables. Pearman correlation was adopted to test the relationship between runoff and the rainfall variables. The findings revealed a general downward trend in rainfall amounts in the 1970s and 1980s. The findings also detected an upward trend in the amount of rainfall from 1990 to 2019. The correlation results of rainfall attributes and runoff showed significant relationships in annual rainfall (r= 0.61), annual rain-days (r=0.61), rain days of heavy rainfall (r= 0.57) and wet season rainfall (r=0.54). These attributed when combined, revealed a 51% contribution to the overall regression with (r=0.51) at 95% probability level. The study concluded that the Sudano-Sahelian Ecological Zone of Nigeria experiencing an increase in the annual rainfall. The increase in rainfall point to the recovery of the rainfall from the great Sahelian drought of the 1970s and 1980s. The rise in the annual rainfall is a possible influencing factor to the frequent occurrences of flooding in recent time across the ecological zone.

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