Drought Estimation-and-Projection Using Standardized Supply-Demand-Water Index and Artificial Neural Networks for Upper Tana River Basin in Kenya

2022 ◽  
pp. 1098-1117
Author(s):  
Raphael Muli Wambua
Keyword(s):  
Neural Networks ◽  
River Basin ◽  
Meteorological Data ◽  
Kriging Interpolation ◽  
Drought Severity ◽  
Water Index ◽  
Tana River ◽  
Artificial Neural ◽  
Effects Of Drought ◽  
Artificial Neural Network Ann

Drought occurrence, frequency and severity in the Upper Tana River basin (UTaRB) have critically affected water resource systems. To minimize the undesirable effects of drought, there is a need to quantify and project the drought trend. In this research, the drought was estimated and projected using Standardized Supply-Demand-Water Index (SSDI) and an Artificial Neural Network (ANN). Field meteorological data was used in which interpolated was conducted using kriging interpolation technique within ArcGIS environment. The results indicate those moderate, severe and extreme droughts at varying magnitudes as detected by the SSDI during 1972-2010 at different meteorological stations, with SSDI values equal or less than -2.0. In a spatial domain, the areas in south-eastern parts of the UTaRB exhibit the highest drought severity. Time-series forecasts and projection show that the best networks for SSDI exhibit respective ANNs architecture. The projected extreme droughts (values less than -2.00) and abundant water availability (SSDI values ³ 2.00) were estimated using Recursive Multi-Step Neural Networks (RMSNN). The findings can be integrated into planning the drought-mitigation-adaptation and early-warning systems in the UTaRB.

Drought Estimation-and-Projection Using Standardized Supply-Demand-Water Index and Artificial Neural Networks for Upper Tana River Basin in Kenya

2019 ◽  
Vol 10 (4) ◽  
pp. 11-27
Author(s):  
Raphael Muli Wambua
Keyword(s):  
Neural Networks ◽  
River Basin ◽  
Meteorological Data ◽  
Early Warning Systems ◽  
Kriging Interpolation ◽  
Drought Severity ◽  
Water Index ◽  
Tana River ◽  
Artificial Neural ◽  
Effects Of Drought

Drought occurrence, frequency and severity in the Upper Tana River basin (UTaRB) have critically affected water resource systems. To minimize the undesirable effects of drought, there is a need to quantify and project the drought trend. In this research, the drought was estimated and projected using Standardized Supply-Demand-Water Index (SSDI) and an Artificial Neural Network (ANN). Field meteorological data was used in which interpolated was conducted using kriging interpolation technique within ArcGIS environment. The results indicate those moderate, severe and extreme droughts at varying magnitudes as detected by the SSDI during 1972-2010 at different meteorological stations, with SSDI values equal or less than -2.0. In a spatial domain, the areas in south-eastern parts of the UTaRB exhibit the highest drought severity. Time-series forecasts and projection show that the best networks for SSDI exhibit respective ANNs architecture. The projected extreme droughts (values less than -2.00) and abundant water availability (SSDI values ≥ 2.00) were estimated using Recursive Multi-Step Neural Networks (RMSNN). The findings can be integrated into planning the drought-mitigation-adaptation and early-warning systems in the UTaRB.

Ozone Concentration Prediction using Artificial Neural Networks

2017 ◽  
Vol 68 (10) ◽  
pp. 2224-2227 ◽  
Author(s):  
Camelia Gavrila
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Time Data ◽  
Marquardt Algorithm ◽  
Sensitivity Tests ◽  
Artificial Neural ◽  
The Relationship

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.

scholarly journals Mapping and Estimation of Monthly Global Solar Irradiation in Different Zones in Souss-Massa Area, Morocco, Using Artificial Neural Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
O. Nait Mensour ◽  
S. Bouaddi ◽  
B. Abnay ◽  
B. Hlimi ◽  
A. Ihlal
Keyword(s):  
Solar Energy ◽  
Solar Irradiation ◽  
Kriging Interpolation ◽  
Solar Pv ◽  
Energy Potential ◽  
Global Solar Irradiation ◽  
Interpolation Technique ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Solar radiation data play an important role in solar energy research. However, in regions where the meteorological stations providing these data are unavailable, strong mapping and estimation models are needed. For this reason, we have developed a model based on artificial neural network (ANN) with a multilayer perceptron (MLP) technique to estimate the monthly average global solar irradiation of the Souss-Massa area (located in the southwest of Morocco). In this study, we have used a large database provided by NASA geosatellite database during the period from 1996 to 2005. After testing several models, we concluded that the best model has 25 nodes in the hidden layer and results in a minimum root mean square error (RMSE) equal to 0.234. Furthermore, almost a perfect correlation coefficient R=0.988 was found between measured and estimated values. This developed model was used to map the monthly solar energy potential of the Souss-Massa area during a year as estimated by the ANN and designed with the Kriging interpolation technique. By comparing the annual average solar irradiation between three selected sites in Souss-Massa, as estimated by our model, and six European locations where large solar PV plants are deployed, it is apparent that the Souss-Massa area is blessed with higher solar potential.

Fundamental Categories of Artificial Neural Networks

2021 ◽  
pp. 30-64
Author(s):  
Arunaben Prahladbhai Gurjar ◽  
Shitalben Bhagubhai Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Operation ◽  
Artificial Neural ◽  
Classification Prediction ◽  
Artificial Neural Network Ann ◽  
Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

Fundamental Categories of Artificial Neural Networks

2022 ◽  
pp. 1-30
Author(s):  
Arunaben Prahladbhai Gurjar ◽  
Shitalben Bhagubhai Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Operation ◽  
Artificial Neural ◽  
Classification Prediction ◽  
Artificial Neural Network Ann ◽  
Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

Artificial Neural Networks and Learning Techniques

2016 ◽  
pp. 89-112
Author(s):  
Pushpendu Kar ◽  
Anusua Das
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Research Work ◽  
The Neural Network ◽  
Learning Techniques ◽  
Learning Procedure ◽  
Processing Component ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralized computer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an Artificial Neural Network (ANN) is called as ‘Neuron'. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure. This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.

Development of a Non-Linear Integrated Drought Index (NDI) for Managing Drought and Water Resources Forecasting in the Upper Tana River Basin-Kenya

2020 ◽  
Vol 11 (1) ◽  
pp. 15-33
Author(s):  
Raphael Muli Wambua
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Lead Time ◽  
Drought Index ◽  
Meteorological Data ◽  
Principal Component ◽  
Drought Forecasting ◽  
Tropical River ◽  
Tana River ◽  
Non Linear

This article uses the non-linear integrated drought index (NDI) for managing drought and water resources forecasting in a tropical river basin. The NDI was formulated using principal component analysis (PCA). The NDI used hydro-meteorological data and forecasted using recursive multi-step neural networks. In this article, drought forecasting and projection is adopted for planning ahead for mitigation and for the adaptation of adverse effects of droughts and food insecurity in the river basin. Results that forecasting ability of NDI model using ANNs decreased with increase in lead time. The formulated NDI as a tool for projecting into the future.

scholarly journals Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening

Molecules ◽  
2020 ◽  
Vol 25 (24) ◽  
pp. 5942
Author(s):  
Maciej Przybyłek
Keyword(s):  
Neural Networks ◽  
Molecular Descriptors ◽  
Matthews Correlation Coefficient ◽  
Model Development ◽  
Operating Characteristics ◽  
Glucosidase Inhibitors ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Bioassay Data ◽  
Beta Glucosidase

Beta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural Networks, SANN). In order to evaluate the models’ accuracy and select the best classifiers among automatically generated SANNs, the Matthews correlation coefficient (MCC) was used. The application of the combination of maxHBint3 and SpMax8_Bhs descriptors leads to the highest predicting abilities of SANNs, as evidenced by the averaged test set prediction results (MCC = 0.748) calculated for ten different dataset splits. Additionally, the models were analyzed employing receiver operating characteristics (ROC) and cumulative gain charts. The thirteen final classifiers obtained as a result of the model development procedure were applied for a natural compounds collection available in the BIOFACQUIM database. As a result of this beta-glucosidase inhibitors screening, eight compounds were univocally classified as active by all SANNs.

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