scholarly journals Comparing The Models SARIMA, ANFIS And ANFIS-DE In Forecasting Monthly Evapotranspiration Rates Under Heterogeneous Climatic Conditions

2021 ◽  
Author(s):  
Pouya Aghelpour ◽  
Vahid Varshavian ◽  
Zahra Hamedi
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Air Temperature ◽  
Mean Squared Error ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Climate Type ◽  
Inference System ◽  
Semi Arid ◽  
Different Climates

Abstract Reference crop evapotranspiration (ET0) is one of the most important hydro-climatological components which directly affects agricultural productions, and its forecasting is critical for water managers and irrigation planners. In this study, adaptive neuro-fuzzy inference system (ANFIS) model has been hybridized by differential evolution (DE) optimization algorithm as a novel approach to forecast monthly ET0. Furthermore, this model has been compared with the classic stochastic time series model. For this, the ET0 rates were calculated on monthly scale during 1995-2018, based on FAO-56 Penman-Monteith equation and meteorological data including: minimum air temperature, maximum air temperature, mean air temperature, minimum relative humidity, maximum relative humidity & sunshine duration. The investigation was performed on 6 stations in different climates of Iran, including: Bandar Anzali & Ramsar (per-humid), Gharakhil (sub-humid), Shiraz (semi-arid), Ahwaz (arid) and Yazd (extra-arid). The models’ performances were evaluated by the criteria percent bias (PB), root mean squared error (RMSE), normalized RMSE (NRMSE) and Nash-Sutcliff (NS) coefficient. Surveys confirm the high capability of the hybrid ANFIS-DE model in monthly ET0 forecasting; so that the DE algorithm was able to improve the accuracy of ANFIS, by 16% on average. Seasonal autoregressive integrated moving average (SARIMA) was the most suitable pattern among the time series stochastic models, and superior compared to its other competitors. Consequently, due to the simplicity and parsimony, the SARIMA was suggested more appropriate for monthly ET0 forecasting in all the climates. Comparison between the different climates confirmed that the climate type significantly affects the forecasting accuracies: it’s revealed that all the models work better in extra-arid, arid and semi-arid climates, than the humid and per-humid areas.

Empirical Equations to Estimate Evapotranspiration in Mosul City

2020 ◽  
Vol 27 (4) ◽  
pp. 98-102
Author(s):  
Haqqi Yasin ◽  
Luma Abdullah
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Reference Evapotranspiration ◽  
Sunshine Duration ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Daily Data ◽  
Daily Sunshine ◽  
Measured Wind Speed

Average daily data of solar radiation, relative humidity, wind speed and air temperature from 1980 to 2008 are used to estimate the daily reference evapotranspiration in the Mosul City, North of Iraq. ETo calculator software with the Penman Monteith method standardized by the Food and Agriculture Organization is used for calculations. Further, a nonlinear regression approach using SPSS Statistics is utilized to drive the daily reference evapotranspiration relationships in which ETo is function to one or more of the average daily air temperature, actual daily sunshine duration, measured wind speed at 2m height and relative humidity

scholarly journals Sensitivity of Precipitation Phase over the Swiss Alps to Different Meteorological Variables

2014 ◽  
Vol 15 (2) ◽  
pp. 685-696 ◽  
Author(s):  
S. Froidurot ◽  
I. Zin ◽  
B. Hingray ◽  
A. Gautheron
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Meteorological Data ◽  
Explanatory Power ◽  
Surface Air Temperature ◽  
Ground Level ◽  
Swiss Alps ◽  
Meteorological Variables ◽  
Study Region ◽  
Precipitation Phase

Abstract In most meteorological or hydrological models, the distinction between snow and rain is based only on a given air temperature. However, other factors such as air moisture can be used to better distinguish between the two phases. In this study, a number of models using different combinations of meteorological variables are tested to determine their pertinence for the discrimination of precipitation phases. Spatial robustness is also evaluated. Thirty years (1981–2010) of Swiss meteorological data are used, consisting of radio soundings from Payerne as well as present weather observations and surface measurements (mean hourly surface air temperature, mean hourly relative humidity, and hourly precipitation) from 14 stations, including Payerne. It appeared that, unlike surface variables, variables derived from the atmospheric profiles (e.g., the vertical temperature gradient) hardly improve the discrimination of precipitation phase at ground level. Among all tested variables, surface air temperature and relative humidity show the greatest explanatory power. The statistical model using these two variables and calibrated for the case study region provides good spatial robustness over the region. Its parameters appear to confirm those defined in the model presented by Koistinen and Saltikoff.

scholarly journals Evaluation of nature-based solutions implementation scenarios, using urban surface modelling

2021 ◽  
pp. 1-42
Author(s):  
Emmanuel Panagiotakis ◽  
Dionysia Kolokotsa ◽  
Nektarios Chrysoulakis
Keyword(s):  
Heat Flux ◽  
Relative Humidity ◽  
Urban Environment ◽  
Air Temperature ◽  
Green Infrastructure ◽  
Model Simulation ◽  
Meteorological Data ◽  
Sensible Heat Flux ◽  
The Impact ◽  
The City

The present paper aims to study the impact of Nature Based Solutions (NBS) on the urban environment. The Surface Urban Energy and Water balance Scheme (SUEWS) is used to quantify the impact of NBS in the city of Heraklion, Crete, Greece, a densely built urban area. Local meteorological data and data from an Eddy Covariance flux tower installed in the city center are used for the model simulation and evaluation. Five different scenarios are tested by replacing the city’s roofs and pavements with green infrastructure, i.e., trees and grass, and water bodies. The NBS impact evaluation is based on the changes of air temperature above 2m from the ground, relative humidity and energy fluxes. A decrease of the air temperature is revealed with the highest reduction (2.3%) occurring when the pavements are replaced with grass for all scenarios. The reduction of the air temperature is followed by a decrease in turbulent sensible heat flux. For almost all cases, an increase of the relative humidity is noticed, accompanied by a considerable increase of the turbulent latent heat flux. Therefore, NBS in cities change the energy balance significantly and modify the urban environment for the citizens' benefit.

scholarly journals VEGETATION CHANGES AND THE RELATIONSHIP WITH CLIMATE VARIABILITY IN THE UPPER AND MIDDLE REACHES OF THE NENJIANG RIVER BASIN, CHINA

2016 ◽  
Vol XLI-B8 ◽  
pp. 889-895
Author(s):  
F. Huang ◽  
D. H. Wen ◽  
P. Wang
Keyword(s):  
Relative Humidity ◽  
River Basin ◽  
Wind Velocity ◽  
Land Surface ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Vegetation Coverage ◽  
Spring Temperature ◽  
Rescaled Range ◽  
Nenjiang River Basin

To detect changes in vegetation is desirable for modeling and predicting interactions between land surface and atmosphere. Multitemporal series of SPOT VEGETATION NDVI dataset and meteorological data were integrated to interpret vegetation dynamics and the linkage with climate variations in the upper and middle reaches of the Nenjiang River Basin (NRB) from 1999 to 2010 using the correlation analysis and the rescaled range (R/S) analysis. The results demonstrate that annual NDVI increased slightly and 26.02% vegetation coverage of the study area significantly improved. The area of significantly decreased in vegetation cover took up 13.33% of the total land in spring. In autumn, 26.2% of the study area showed a significant vegetation increase. The improved activity of vegetation might reinforce in summer and autumn, while the decreasing tendency in spring might be persistent in the future. The yearly NDVI had significant positive linkages with precipitation and relative humidity. NDVI related significantly and negatively with temperature, sunshine hours and wind velocity, because they may have effects of increasing evapotranspiration and risk of drought and cold damage of vegetation. The variations of annual NDVI were much affected by summer temperature, relative humidity and sunshine duration in autumn and spring wind velocity. Seasonal NDVI decreased in parallel with elevated temperature, but there was no correlation between NDVI and precipitation. Spring temperature, relative humidity in summer and autumn contributed markedly to NDVI variations in the same season. The vegetation improving trend may induce by the warm-wetting climate in recent twelve years.

scholarly journals Estimation of microclimatic data in remote mountainous areas using an artificial neural network model-based approach

2013 ◽  
Vol 12 (4) ◽  
pp. 384-389
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Relative Humidity ◽  
Air Temperature ◽  
Meteorological Data ◽  
Application Site ◽  
Mountainous Area ◽  
Mountainous Areas ◽  
Model Based ◽  
Artificial Neural

An artificial neural network (ANN) model-based approach was developed and applied to estimate values of air temperature and relative humidity in remote mountainous areas. The application site was the mountainous area of the Samaria National Forest canyon (Greece). Seven meteorological stations were established in the area and ANNs were developed to predict air temperature and relative humidity for the five most remote stations of the area using data only from two stations located in the two more easily accessed sites. Measured and model-estimated data were compared in terms of the determination coefficient (R2), the mean absolute error (MAE) and residuals normality. Results showed that R2 values range from 0.7 to 0.9 for air temperature and from 0.7 to 0.8 for relative humidity whereas MAE values range from 0.9 to 1.8 oC and 5 to 9%, for air temperature and relative humidity, respectively. In conclusion, the study demonstrated that ANNs, when adequately trained, could have a high applicability in estimating meteorological data values in remote mountainous areas with sparse network of meteorological stations, based on a series of relatively limited number of data values from nearby and easily accessed meteorological stations.

scholarly journals Effect of meteorological factors on influenza-like illness from 2012 to 2015 in Huludao, a northeastern city in China

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6919 ◽  
Author(s):  
Ying-Long Bai ◽  
De-Sheng Huang ◽  
Jing Liu ◽  
De-Qiang Li ◽  
Peng Guan
Keyword(s):  
Relative Humidity ◽  
Vapor Pressure ◽  
Air Temperature ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Lag Time ◽  
Temperature Minimum ◽  
The Relationship ◽  
Maximum Air Temperature ◽  
Minimum Air Temperature

Background This study aims to describe the epidemiological patterns of influenza-like illness (ILI) in Huludao, China and seek scientific evidence on the link of ILI activity with weather factors. Methods Surveillance data of ILI cases between January 2012 and December 2015 was collected in Huludao Central Hospital, meteorological data was obtained from the China Meteorological Data Service Center. Generalized additive model (GAM) was used to seek the relationship between the number of ILI cases and the meteorological factors. Multiple Smoothing parameter estimation was made on the basis of Poisson distribution, where the number of weekly ILI cases was treated as response, and the smoothness of weather was treated as covariates. Lag time was determined by the smallest Akaike information criterion (AIC). Smoothing coefficients were estimated for the prediction of the number of ILI cases. Results A total of 29, 622 ILI cases were observed during the study period, with children ILI cases constituted 86.77%. The association between ILI activity and meteorological factors varied across different lag periods. The lag time for average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity were 2, 2, 1, 1 and 0 weeks, respectively. Average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity could explain 16.5%, 9.5%, 18.0%, 15.9% and 7.7% of the deviance, respectively. Among the temperature indexes, the minimum temperature played the most important role. The number of ILI cases peaked when minimum temperature was around −13 °C in winter and 18 °C in summer. The number of cases peaked when the relative humidity was equal to 43% and then began to decrease with the increase of relative humidity. When the humidity exceeded 76%, the number of ILI cases began to rise. Conclusions The present study first analyzed the relationship between meteorological factors and ILI cases with special consideration of the length of lag period in Huludao, China. Low air temperature and low relative humidity (cold and dry weather condition) played a considerable role in the epidemic pattern of ILI cases. The trend of ILI activity could be possibly predicted by the variation of meteorological factors.

scholarly journals FPGA-Based Implementation of a Multilayer Perceptron Suitable for Chaotic Time Series Prediction

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 90 ◽  
Author(s):  
Ana Pano-Azucena ◽  
Esteban Tlelo-Cuautle ◽  
Sheldon Tan ◽  
Brisbane Ovilla-Martinez ◽  
Luis de la Fraga
Keyword(s):  
Time Series ◽  
Multilayer Perceptron ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Prediction ◽  
Chaotic Time Series ◽  
Series Data ◽  
Support Vector ◽  
Maximum Lyapunov Exponent ◽  
Inference System

Many biological systems and natural phenomena exhibit chaotic behaviors that are saved in time series data. This article uses time series that are generated by chaotic oscillators with different values of the maximum Lyapunov exponent (MLE) to predict their future behavior. Three prediction techniques are compared, namely: artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machines (SVM). The experimental results show that ANNs provide the lowest root mean squared error. That way, we introduce a multilayer perceptron that is implemented using a field-programmable gate array (FPGA) to predict experimental chaotic time series.

Evaluating and modelling the impacts of climate change and reference datasets on river water temperatures for a hydropower system with two outlets

2020 ◽  
Author(s):  
Philippe Gatien
Keyword(s):  
Climate Change ◽  
Water Temperature ◽  
Air Temperature ◽  
River Flow ◽  
Mean Squared Error ◽  
Meteorological Data ◽  
Delta Method ◽  
Weather Data ◽  
Monthly Basis ◽  
Reanalysis Dataset

<p>Water temperature modelling has become an essential tool in the management of ectotherm species downstream of dams in North American rivers. The main objective of this project is to compare different datasets and their ability to adequately simulate water temperatures in the Nechako River, (B.C., Canada) downstream of a major dam where the flow is not managed for hydroelectric production, but spills are programmed to cool the downstream reaches. This will ultimately lead to a reassessment of water management in the context of climate change to ensure the survival of fish migrating or living in the reaches located downstream of the dam during warm periods.</p><p>Water in the Nechako River stems from the Nechako reservoir at the Skins lake spillway and flows into river through a series of lakes prior to reaching Finmoore, where federal regulations stipulate that water temperatures must be maintained below 20 °C. The river has multiple tributaries on it’s 250 km journey including the Nautley river. The river flow is simulated using a 1D unsteady flow simulation and lateral inflows using HEC-RAS.</p><p>Water temperature simulations are then conducted using different datasets. The first is a series of observed meteorological data spanning from 2017 to present day from two different weather stations near the river. The second dataset is ERA5, a reanalysis product that’s gridded every 0.25°. Eleven stations nearest to the river were extracted over the same period as the observations. Both datasets were used to calibrate five parameters (dust coefficient, three wind function parameters and the Richardson number) three times using the mean absolute error (MAE), Nash-Sutcliffe coefficient (NS) and root mean squared error (RMSE) by comparing the observed and simulated temperatures near Finmoore.</p><p>Individual calibrations were performed over each available summer from early June to late August and then validated over the rest of the data to ensure the robustness of the results.</p><p>Overall, the reanalysis dataset outperformed the available observations for thermal representation of the river.</p><p>To further understand the thermal model, a sensitivity analysis was performed on the different inputs (inflow water temperature, air temperature, wind speed, etc.). The model showed very little sensitivity to the characteristics of the inflow (temperature, volume) as the point of interest was so far downstream. In fact, environmental factors such as air temperature had a greater impact on water temperature than upstream conditions at the reservoir spillway. This effect seems to be mostly attributable to Cheslatta Lake with its long water residence time that can reach upwards of three days.</p><p>The potential effects of climate change on water temperature were then investigated by modifying existing weather data like air temperature with the delta method on a monthly basis using the RCP8.5 emission scenario. Water temperatures increased throughout by roughly 2.5°C downstream, near Finmoore.</p><p> </p>

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