Estimation of Evaporation from a Reservoir in Semi arid Environments Using Artificial Neural Network and Climate Based Models

Benzaghta, Mostafa Ali

doi:10.9734/bjast/2014/3557

Cited by 3 publications

(6 citation statements)

References 26 publications

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“…It is increasingly becoming the most important environmental limitation, which is limiting plant growth. According to the statistics, over 30 arid and semi-arid countries are expected to experience water deficiency in 2025 [1]. This will limit agricultural development, threaten food supplies, and inflame rural poverty.…”

Section: Introductionmentioning

confidence: 99%

“…Evaporation is described as the reduction of deposited water due to the conversion of liquid phase to steam phase, which is influenced by the climate situation, such as weather, wind velocities, relative humidity, and sunshine. According to the World Meteorological Organization (WMO), more than half of the total inflow (rainfall or any other sources) to Lake Victoria in the U.S. is lost due to evaporation, which results in relatively humid conditions [1].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Libya has built one of the largest civil engineering groundwater pumping and transferring systems to overcome water limitations and climate hindrance (high temperature and low rainfall). This project is known as the Manmade River Project (MRP) [1]. The purpose of this project was to supply the water demand of Libya by pumping underground water underneath the Sahara Desert and transfer it using a network of huge underground pipes, especially for irrigation.…”

Section: Introductionmentioning

confidence: 99%

“…The high cost of water pumping and lack of appropriate planning are the main concerns. In Egypt's Lake Nasser (located in an arid area), where the Nile's water is stored, downstream water loss, due to evaporation, is estimated to be 3 m in depth, or double that of Lake Victoria [1]. In Australia, it is calculated that around 95% of the precipitation evaporates and has no contribution to runoff [1].…”

Section: Introductionmentioning

confidence: 99%

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On the Prediction of Evaporation in Arid Climate Using Machine Learning Model

Jasmine

Mohammadian

Bonakdari

2022

MCA

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Evaporation calculations are important for the proper management of hydrological resources, such as reservoirs, lakes, and rivers. Data-driven approaches, such as adaptive neuro fuzzy inference, are getting popular in many hydrological fields. This paper investigates the effective implementation of artificial intelligence on the prediction of evaporation for agricultural area. In particular, it presents the adaptive neuro fuzzy inference system (ANFIS) and hybridization of ANFIS with three optimizers, which include the genetic algorithm (GA), firefly algorithm (FFA), and particle swarm optimizer (PSO). Six different measured weather variables are taken for the proposed modelling approach, including the maximum, minimum, and average air temperature, sunshine hours, wind speed, and relative humidity of a given location. Models are separately calibrated with a total of 86 data points over an eight-year period, from 2010 to 2017, at the specified station, located in Arizona, United States of America. Farming lands and humid climates are the reason for choosing this location. Ten statistical indices are calculated to find the best fit model. Comparisons shows that ANFIS and ANFIS–PSO are slightly better than ANFIS–FFA and ANFIS–GA. Though the hybrid ANFIS–PSO (R2= 0.99, VAF = 98.85, RMSE = 9.73, SI = 0.05) is very close to the ANFIS (R2 = 0.99, VAF = 99.04, RMSE = 8.92, SI = 0.05) model, preference can be given to ANFIS, due to its simplicity and easy operation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Prediction of Evaporation in Arid Climate Using Machine Learning Model

Jasmine

Mohammadian

Bonakdari

2022

MCA

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“…Guo et al (2016) encontraram evaporação média de 2,7 mm dia -1 , totalizando uma lâmina de 417 mm, para o maior lago do Tibete, no período de abril a setembro de 2014. A determinação da evaporação não é uma tarefa fácil de ser realizada com precisão, mas é essencial para o manejo de irrigação, gestão e planejamento dos recursos hídricos (Benzaghta, 2014;Wang et al, 2014;Aydin & Karakus, 2016;Cabrera et al, 2016).…”

Section: Introductionunclassified

Balanço De Energia E Evaporação Da Água Com Baixa Turbidez Em Reservatórios De Uso Agrícola

Borges¹,

Oliveira²,

Silva³

et al. 2017

Anais Do IV Inovagri International Meeting - 2017

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RESUMO:A disponibilidade hídrica em açudes e reservatórios de diferentes regiões do mundo tem sofrido significativa redução em função da ausência de chuvas, poluição da água, crescente consumo no campo e nas cidades e elevadas perdas por evaporação. Dessa forma, o trabalho teve como objetivo determinar a evaporação da água com baixa turbidez armazenada em reservatórios revestidos com manta impermeável destinada à irrigação da cultura do fumo para exportação. A pesquisa foi conduzida em dois reservatórios de uma Fazenda propriedade da empresa DANCO, localizada no município de Governador Mangabeira (12º37' S; 39º03' W; 218 m). Para determinação da evaporação da água por meio do método do balanço de energia com Razão de Bowen, foram utilizadas três estruturas equipadas com instrumentos para coleta dos dados; sendo duas plataformas flutuantes e uma torre meteorológica. Observa-se que na média para todo o período de 74 dias de estudo (18 de setembro a 30 de novembro de 2016), a soma dos fluxos turbulentos (calor sensível + calor latente) correspondeu a 90,3% da energia disponível (radiação líquida -calor na água). A lâmina média de evaporação encontrada foi de 4,7 mm dia -1 , variando de 0,61 a 8,0 mm, com total acumulado de 350 mm. PALAVRAS-CHAVE: calor latente, razão de Bowen, micrometeorologia ENERGY BALANCE AND EVAPORATION OF LOW TURBIDITY WATER IN AGRICULTURAL USE RESERVOIRSABSTRACT: Water availability in dams and reservoirs in different regions of the world has suffered a significant reduction due to the absence of rainfall, water pollution, increasing consumption in the countryside and cities and high losses due to evaporation. Thus, the objective of this research was to quantify the evaporation of water with low turbidity stored in reservoirs coated with Impermeable blanket and destined to the irrigation of the culture of the 1 Doutoranda em Engenharia Agrícola, UFRB, Cruz das Almas -Bahia. Profª do IF-Sertão Pernambucano.

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Predicted evaporation in Basrah using artificial neural networks

Hamza

2024

Open Engineering

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Accurate evaporation estimation is crucial for managing hydrologic, hydraulic, and agricultural systems, among many other applications of water resources. Empirical formulae for estimating evaporation exist, but their performance is not always sufficient due to the intricacy of the process and its nonlinear connection with other elements of the hydrological cycle. For this reason, a model of artificial neural networks was developed to estimate the daily potential evaporation in the southern Iraqi city of Basrah. A feedforward backpropagation (BP) network with a single hidden layer has been used to construct the mode. Different networks with various neuron counts were assessed. The developed models have been trained, tested, and validated using daily observations of the average rainfall, wind speed, average temperature, average relative humidity, and evaporation. The final evaporation was predicted using an artificial neural network (ANN) model. The proposed model was found to be more suitable to describe evaporation in any region of the world based on the values of the error analysis and the coefficient of determination, according to the ANN model. The Levenberg–Marquardt algorithm (LMA) was determined to have the lowest mean-squared error (MSE) and highest value of the coefficient of correlation (R) of the six proposed BP algorithms. The LMA’s hidden layer’s ideal neuron count was 30 neurons, with an MSE of 0.00288 and R 2 = 99. As a result, ANN displayed excellent performance in terms of evaporation prediction value. The study’s findings highlight the significance of predicting evaporation as the main metric for evaluating the effects of climate change on water resources.

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Estimation of Evaporation from a Reservoir in Semi arid Environments Using Artificial Neural Network and Climate Based Models

Cited by 3 publications

References 26 publications

On the Prediction of Evaporation in Arid Climate Using Machine Learning Model

On the Prediction of Evaporation in Arid Climate Using Machine Learning Model

Balanço De Energia E Evaporação Da Água Com Baixa Turbidez Em Reservatórios De Uso Agrícola

Predicted evaporation in Basrah using artificial neural networks

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