Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm

2020 ◽  
Vol 11 (3) ◽  
pp. 19-29
Author(s):  
Nawaf N. Hamadneh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Water Level ◽  
Firefly Algorithm ◽  
Sea Water ◽  
Statistical Tests ◽  
Dead Sea ◽  
Water Levels ◽  
Artificial Neural ◽  
The Dead

In this study, the performance of adaptive multilayer perceptron neural network (MLPNN) for predicting the Dead Sea water level is discussed. Firefly Algorithm (FFA), as an optimization algorithm is used for training the neural networks. To propose the MLPNN-FFA model, Dead Sea water levels over the period 1810–2005 are applied to train MLPNN. Statistical tests evaluate the accuracy of the hybrid MLPNN-FFA model. The predicted values of the proposed model were compared with the results obtained by another method. The results reveal that the artificial neural network (ANN) models exhibit high accuracy and reliability for the prediction of the Dead Sea water levels. The results also reveal that the Dead Sea water level would be around -450 until 2050.

Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm

2022 ◽  
pp. 1118-1129
Author(s):  
Nawaf N. Hamadneh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Water Level ◽  
Firefly Algorithm ◽  
Sea Water ◽  
Dead Sea ◽  
Water Levels ◽  
Artificial Neural ◽  
The Dead ◽  
Predicted Values

In this study, the performance of adaptive multilayer perceptron neural network (MLPNN) for predicting the Dead Sea water level is discussed. Firefly Algorithm (FFA), as an optimization algorithm is used for training the neural networks. To propose the MLPNN-FFA model, Dead Sea water levels over the period 1810–2005 are applied to train MLPNN. Statistical tests evaluate the accuracy of the hybrid MLPNN-FFA model. The predicted values of the proposed model were compared with the results obtained by another method. The results reveal that the artificial neural network (ANN) models exhibit high accuracy and reliability for the prediction of the Dead Sea water levels. The results also reveal that the Dead Sea water level would be around -450 until 2050.

Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks

2010 ◽  
Vol 36 (5) ◽  
pp. 620-627 ◽  
Author(s):  
Mohammad Ali Ghorbani ◽  
Rahman Khatibi ◽  
Ali Aytek ◽  
Oleg Makarynskyy ◽  
Jalal Shiri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Water Level ◽  
Sea Water ◽  
Artificial Neural

Gypsum saturation degrees and precipitation potentials from Dead Sea - seawater mixtures

2009 ◽  
Vol 6 (5) ◽  
pp. 416 ◽  
Author(s):  
Itay J. Reznik ◽  
Jiwchar Ganor ◽  
Assaf Gal ◽  
Ittai Gavrieli
Keyword(s):  
Surface Water ◽  
Water Level ◽  
Water Column ◽  
Sea Water ◽  
Theoretical Calculations ◽  
Dead Sea ◽  
Gypsum Precipitation ◽  
The Dead ◽  
Rate Of Decline ◽  
The 1960S

Environmental context. Since the 1960s the Dead Sea water level has dropped by nearly 30 m and over the last decade the rate of decline accelerated to over 1 m per year. Conveying seawater to the Dead Sea to stabilise or even raise its water level is currently being considered but may result in ‘whitening’ of the surface water through the formation of minute gypsum crystals that will remain suspended in the water column for a prolonged period of time. This paper is a first step in attaining the relevant physical and chemical parameters required to assess the potential for such whitening of the Dead Sea. Abstract. Introduction of seawater to the Dead Sea (DS) to stabilise its level raises paramount environmental questions. A major concern is that massive nucleation and growth of minute gypsum crystals will occur as a result of mixing between the SO42–-rich Red Sea (RS) water and Ca2+-rich DS brine. If the gypsum will not settle quickly to the bottom it may influence the general appearance of the DS by ‘whitening’ the surface water. Experimental observations and theoretical calculations of degrees of saturation with respect to gypsum (DSG) and gypsum precipitation potentials (PPT) were found to agree well, over the large range but overall high ionic strength of DS–RS mixtures. The dependency of both DSG and PPT on temperature was examined as well. Based on our thermodynamic insights, slow discharge of seawater to the DS will result in a relatively saline upper water column which will lead to enhanced gypsum precipitation.

scholarly journals MODEL PREDIKSI LEVEL AIR DI LAHAN PERKEBUNAN KELAPA SAWIT DENGAN JARINGAN SARAF TIRUAN BERDASARKAN PENGUKURAN SENSOR RAIN GAUGE DAN ULTRASONIK

2021 ◽  
Vol 10 (1) ◽  
pp. 104
Author(s):  
Hasan Al Banna ◽  
Bayu Dwi Apri Nugroho
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Water Level ◽  
Rain Gauge ◽  
Water Levels ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Artificial Neural ◽  
Water Level Prediction

Monitoring and regulating water levels in oil palm swamps has an essential role in providing sufficient water for crops and conserving the land to not easily or quickly deteriorate. Presently, water level is still manual and has weaknesses, one of which is the accuracy of the data taken depending on the observer. Technology such as sensors integrated with artificial neural network is expected to observe and regulate water levels. This study aims to build a prediction model of water levels in oil palm plantations with artificial neural networks based on the rain gauge and ultrasonic sensors installed on Automatic Weather Station (AWS). The obtained results showed that the prediction model runs well with an R2 value of 0,994 and RMSE 1,16 cm. The water level prediction model in this research then tested for accuracy to prove the model's success rate. Testing the water level prediction model's accuracy in the dry season obtained an R2 value of 0,96 and an RMSE of 1,99 cm. Testing the water level prediction model's accuracy in the rainy season obtained an R2 value of 0,85 and an RMSE value of 4,2 cm. Keywords : artificial neural network, automatic weather station, palm oil, water level

scholarly journals Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower system

RBRH ◽  
2021 ◽  
Vol 26 ◽  
Author(s):  
João Paulo Lyra Fialho Brêda ◽  
Rodrigo Cauduro Dias de Paiva ◽  
Olavo Corrêa Pedrollo ◽  
Otávio Augusto Passaia ◽  
Walter Collischonn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Balance ◽  
Water Level ◽  
Integrated Approach ◽  
Water Levels ◽  
Ann Model ◽  
Storage Reservoirs ◽  
Ann Models ◽  
Artificial Neural

ABSTRACT Reservoirs considerably affect river streamflow and need to be accurately represented in environmental impact studies. Modeling reservoir outflow represents a challenge to hydrological studies since reservoir operations vary with flood risk, economic and demand aspects. The Brazilian Interconnected Energy System (SIN) is an example of a unique and complex system of coordinated operation composed by more than 160 large reservoirs. We proposed and evaluated an integrated approach to simulate daily outflows from most of the SIN reservoirs (138) using an Artificial Neural Network (ANN) model, distinguishing run-of-the-river and storage reservoirs and testing cases whether outflow and level data were available as input. Also, we investigated the influence of the proposed input features (14) on the simulated outflow, related to reservoir water balance, seasonality, and demand. As a result, we verified that the outputs of the ANN model were mainly influenced by local water balance variables, such as the reservoir inflow of the present day and outflow of the day before. However, other features such as the water level of 4 large reservoirs that represent different regions of the country, which infers about hydropower demand through water availability, seemed to influence to some extent reservoirs outflow estimates. This result indicates advantages in using an integrated approach rather than looking at each reservoir individually. In terms of data availability, it was tested scenarios with (WITH_Qout) and without (NO_Qout and SIM_Qout) observed outflow and water level as input features to the ANN model. The NO_Qout model is trained without outflow and water level while the SIM_Qout model is trained with all input features, but it is fed with simulated outflows and water levels rather than observations. These 3 ANN models were compared with two simple benchmarks: outflow is equal to the outflow of the day before (STEADY) and the outflow is equal to the inflow of the same day (INFLOW). For run-of-the-river reservoirs, an ANN model is not necessary as outflow is virtually equal to inflow. For storage reservoirs, the ANN estimates reached median Nash-Sutcliffe efficiencies (NSE) of 0.91, 0.77 and 0.68 for WITH_, NO_ and SIM_Qout respectively, compared to a median NSE of 0.81 and 0.29 for the STEADY and INFLOW benchmarks respectively. In conclusion, the ANN models presented satisfactory performances: when outflow observations are available, WITH_Qout model outperforms STEADY; otherwise, NO_Qout and SIM_Qout models outperform INFLOW.

The Role of Neural Networks and Metaheuristics in Agile Software Development Effort Estimation

2022 ◽  
pp. 306-328
Author(s):  
Anupama Kaushik ◽  
Devendra Kumar Tayal ◽  
Kalpana Yadav
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Software Development ◽  
Statistical Tests ◽  
Agile Software Development ◽  
Effort Estimation ◽  
Software Development Effort ◽  
Artificial Neural ◽  
Agile Software

In any software development, accurate estimation of resources is one of the crucial tasks that leads to a successful project development. A lot of work has been done in estimation of effort in traditional software development. But, work on estimation of effort for agile software development is very scant. This paper provides an effort estimation technique for agile software development using artificial neural networks (ANN) and a metaheuristic technique. The artificial neural networks used are radial basis function neural network (RBFN) and functional link artificial neural network (FLANN). The metaheuristic technique used is whale optimization algorithm (WOA), which is a nature-inspired metaheuristic technique. The proposed techniques FLANN-WOA and RBFN-WOA are evaluated on three agile datasets, and it is found that these neural network models performed extremely well with the metaheuristic technique used. This is further empirically validated using non-parametric statistical tests.

scholarly journals Observations of positive sea surface temperature trends in the steadily shrinking Dead Sea

2018 ◽  
Author(s):  
Pavel Kishcha ◽  
Rachel T. Pinker ◽  
Isaac Gertman ◽  
Boris Starobinets ◽  
Pinhas Alpert
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Water ◽  
Dead Sea ◽  
Water Levels ◽  
Sea Surface ◽  
Surface Warming ◽  
Temperature Trends ◽  
The Dead

Abstract. The steadily shrinking Dead Sea followed by sea surface warming compensates surface water cooling due to increasing evaporation, and even causes the observed positive Dead Sea surface temperature trends. Using observations from Moderate Resolution Imaging Spectroradiometer (MODIS), positive trends were detected in both daytime (0.06 °C year−1) and nighttime (0.04 °C year−1) Dead Sea surface temperature (SST) over the period of 2000–2016. These positive SST trends were observed in the absence of positive trends in surface solar radiation measured by the Dead Sea buoy pyranometer. Neither changes in water mixing in the Dead Sea nor changes in evaporation could explain surface temperature trends. There is a positive feedback loop between the shrinking of the Dead Sea and positive SST trends, which leads to the accelerating decrease in Dead Sea water levels during the period under study. Note that there are two opposite processes based on available measurements: on the one hand, the measured accelerating rate of Dead Sea water levels suggests a long-term increase in Dead Sea evaporation which is expected to be accompanied by a long-term decrease in sea surface temperature. On the other hand, the positive feedback loop leads to the observed shrinking of the Dead Sea area followed by sea surface warming year on year. The total result of these two opposite processes is the statistically significant positive sea surface temperature trends in both daytime (0.06 °C year−1) and nighttime (0.04 °C year−1) during the period under investigation, observed by the MODIS instrument. Our results shed light on the continuing hazard to the Dead Sea and possible disappearance of this unique site.

scholarly journals Application of artificial neural network for predicting water levels in Hooghly estuary, India

2020 ◽  
Vol 3 (1) ◽  
pp. 401-415 ◽  
Author(s):  
Kalyan Kumar Bhar ◽  
Susmita Bakshi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Tidal Cycle ◽  
Water Levels ◽  
Hooghly Estuary ◽  
Tidal Water ◽  
Level Data ◽  
Water Level Data ◽  
Artificial Neural

Abstract Hydrodynamic models for morphodynamic studies in estuaries require continuous tidal water level data as boundary conditions. However, for the Hooghly estuary in India, measurement of continuous tidal water elevation data at the most downstream point is a very difficult task because of the remote location and the confluence with the deep sea. The tidal water level data at this station are measured for a half tidal cycle which is not useful for hydrodynamic modeling. However, at other upstream stations, tide water level data are measured continuously. Accordingly, in this study, an attempt is made to generate continuous tidal water level data at the remote station, using the data of the neighboring stations as input to an artificial neural network (ANN) model. A three-layered feed-forward backpropagation (FFBP) network with two hidden layers is selected and five different combinations of input vectors are used. Simulated water level data obtained from each model are compared with the observed data graphically as well as by estimating the standard error parameters. The best model suitable for prediction of continuous tidal elevation during any time of the tidal cycle and applicable throughout the year is then identified. It is found that tidal data from the nearest neighboring station are more suitable for training.

The Role of Neural Networks and Metaheuristics in Agile Software Development Effort Estimation

2022 ◽  
pp. 947-969
Author(s):  
Anupama Kaushik ◽  
Devendra Kumar Tayal ◽  
Kalpana Yadav
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Software Development ◽  
Statistical Tests ◽  
Agile Software Development ◽  
Effort Estimation ◽  
Software Development Effort ◽  
Artificial Neural ◽  
Agile Software

In any software development, accurate estimation of resources is one of the crucial tasks that leads to a successful project development. A lot of work has been done in estimation of effort in traditional software development. But, work on estimation of effort for agile software development is very scant. This paper provides an effort estimation technique for agile software development using artificial neural networks (ANN) and a metaheuristic technique. The artificial neural networks used are radial basis function neural network (RBFN) and functional link artificial neural network (FLANN). The metaheuristic technique used is whale optimization algorithm (WOA), which is a nature-inspired metaheuristic technique. The proposed techniques FLANN-WOA and RBFN-WOA are evaluated on three agile datasets, and it is found that these neural network models performed extremely well with the metaheuristic technique used. This is further empirically validated using non-parametric statistical tests.

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