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.

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.

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.

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.

Large-scale Groundwater Simulation using Artificial Neural Networks in the Danube River Basin

2020 ◽  
Author(s):  
Illias Landros ◽  
Ioannis Trichakis ◽  
Emmanouil Varouchakis ◽  
George P. Karatzas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
River Basin ◽  
Danube River ◽  
Bayesian Regularization ◽  
Data Set ◽  
The Danube River ◽  
Precipitation Temperature ◽  
Artificial Neural ◽  
Danube River Basin

<p>In recent years, Artificial Neural Networks (ANNs) have proven their merit in being able to simulate the changes in groundwater levels, using as inputs other parameters of the water budget, e.g. precipitation, temperature, etc.. In this study, ANNs have been used to simulate hydraulic head in a large number of wells throughout the Danube River Basin, taking as inputs, precipitation, temperature, and evapotranspiration data in the region. Different ANN architectures have been examined, to minimize the simulation error of the testing data-set. Among the different training algorithms, Levenberg-Marquardt and Bayesian Regularization are used to train the ANNs, while the different activation functions of the neurons that were deployed include tangent sigmoid, logarithmic sigmoid and linear. The initial application comprised of data from 128 wells between 1 January 2000 and 31 October 2014. The best performance was achieved by the algorithm Bayesian Regularization with a error of the order  based on all observation wells. A second application, compared the results of the first one, with the results of an ANN used to simulate a single well. The pros and cons of the two approaches, and the synergies of using both of them is further discussed in order to distinguish the differences, and guide researchers in the field for further applications.</p>

Monthly flow forecast for Mississippi River basin using artificial neural networks

2013 ◽  
Vol 24 (7-8) ◽  
pp. 1785-1793 ◽  
Author(s):  
C. Sivapragasam ◽  
S. Vanitha ◽  
Nitin Muttil ◽  
K. Suganya ◽  
S. Suji ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
River Basin ◽  
Mississippi River ◽  
Mississippi River Basin ◽  
Artificial Neural ◽  
Flow Forecast

scholarly journals DESENVOLVIMENTO DE REDES NEURAIS ARTIFICIAIS PARA ESTIMATIVA DAS VAZÕES DIÁRIAS NA BACIA DO RIO PIRACICABA5

Irriga ◽  
2018 ◽  
Vol 23 (4) ◽  
pp. 756-772
Author(s):  
Eduardo Morgan Uliana ◽  
Demetrius David da Silva ◽  
Michel Castro Moreira ◽  
Donizete Dos Reis Pereira ◽  
Silvio Bueno Pereira ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
River Basin ◽  
Hydrologic Modeling ◽  
Minas Gerais ◽  
Mato Grosso ◽  
Artificial Neural ◽  
E Mail ◽  
Daily Flows ◽  
Piracicaba River

DESENVOLVIMENTO DE REDES NEURAIS ARTIFICIAIS PARA ESTIMATIVA DAS VAZÕES DIÁRIAS NA BACIA DO RIO PIRACICABA5     EDUARDO MORGAN ULIANA1; DEMETRIUS DAVID DA SILVA2; MICHEL CASTRO MOREIRA3; DONIZETE DOS REIS PEREIRA4; SILVIO BUENO PEREIRA2 E FREDERICO TERRA DE ALMEIDA1   1Universidade Federal de Mato Grosso (UFMT), Instituto de Ciências Agrárias e Ambientais (ICAA), Avenida Alexandre Ferronato, CEP.: 78557-267, Sinop – MT, Brasil, e-mail: [email protected], [email protected]. 2Universidade Federal de Viçosa (UFV), Departamento de Engenharia Agrícola, Avenida Peter Henry Rolfs, CEP.: 36570-900, Viçosa – MG, Brasil, e-mail: [email protected]. 3Universidade Federal do Oeste da Bahia, Centro das Ciências Exatas e das Tecnologias, Rua Professor José Seabra de Lemos, CEP.: 47808-021, Barreiras – BA, Brasil, e-mail: [email protected]. 4Universidade Federal de Viçosa (UFV), Instituto de Ciências Agrárias, Rodovia LMG 818, km 06, Florestal – MG, Brasil, e-mail: [email protected]. 5O artigo é referente ao capítulo 3 da tese de doutorado do primeiro autor.     1 RESUMO   As Redes Neurais Artificiais (RNAs) são uma alternativa na modelagem hidrológica para a estimativa das vazões dos cursos de água a partir de dados hidrometeorológicos. O objetivo do trabalho foi desenvolver Redes Neurais Artificiais para estimar as vazões diárias na bacia hidrográfica do rio Piracicaba, Minas Gerais. O estudo foi realizado em três seções de monitoramento de vazão da bacia do rio Piracicaba, localizada no Estado de Minas Gerais - Brasil. No desenvolvimento das RNAs foram realizados a coleta e seleção dos dados; a definição da arquitetura da rede; e o treinamento e validação das redes desenvolvidas. A maior parte das RNAs desenvolvidas apresentou coeficiente de Nash-Sutcliffe maior que 0,80 o que permitiu classificar os modelos como bons para a estimativa das vazões. Com base nos resultados, pode-se concluir que as RNAs são adequadas para a estimativa das vazões diárias na bacia do rio Piracicaba e podem ser empregadas na estimativa de eventos extremos e no gerenciamento dos recursos hídricos.   Palavras-Chave: modelo empírico inteligência artificial, modelagem hidrológica.     ULIANA, E. M.; SILVA, D. D.; MOREIRA, M. C.; PEREIRA, D. R.; PEREIRA, S. B.; ALMEIDA, F. T. ARTIFICIAL NEURAL NETWORKS FOR DAILY FLOW ESTIMATES IN THE PIRACICABA RIVER BASIN     2 ABSTRACT   Artificial neural networks (ANNs) have been used alternatively in hydrologic modeling to estimate accurately watercourse flows based on hydrometeorological data. This study developed artificial neural networks to estimate daily flows in Piracicaba river basin, in Minas Gerais state (Brazil). For this, we used three runoff-monitoring sections of the Piracicaba river basin, with an area of 5,304.0 km2, and located in the State of Minas Gerais – Brazil. For designing the ANNs to estimate daily flows, we adopted the following steps: data collection and selection, network architecture definition, training and validation of results. The results showed that ANNs are adequate to estimate daily flows in Piracicaba river basin.   Keywords: empirical model, artificial intelligence, hydrologic modeling.

Development of an application to determine quality indexes in rivers through artificial neural networks applied to data from the Bogota river

2017 ◽  
pp. 1041-1053 ◽  
Author(s):  
Luis Enrique Mendez Lopez ◽  
Octavio Jose Salcedo Parra ◽  
Miguel J. Espitia R.
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
Water Index ◽  
Quality Of Water ◽  
Artificial Neural ◽  
Quality Indexes

The quality of water in a river is a factor that must be influenced by the system that surrounds it, in this work we try to determine through a historical set of measurements on the Bogota river between years 2008 to 2015 supplied by the Autonomous Regional Corporation of Cundinamarca (CAR). We want to know the variables with the greatest impact on changes in the water index of the ICA and with them to build a model using artificial neural networks to predict water quality indexes in any type of river in the same Bogota river.

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