scholarly journals Simulation of Pan-Evaporation Using Penman and Hamon Equations and Artificial Intelligence Techniques

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 793
Author(s):  
Abdul Razzaq Ghumman ◽  
Mohammed Jamaan ◽  
Afaq Ahmad ◽  
Md. Shafiquzzaman ◽  
Husnain Haider ◽  
...  
Keyword(s):  
Wind Speed ◽  
Water Resources ◽  
Arid Regions ◽  
Fuzzy Inference ◽  
Sunshine Duration ◽  
Pan Evaporation ◽  
Estimation Of Parameters ◽  
Inference System ◽  
Daunting Task ◽  
The Impact

The evaporation losses are very high in warm-arid regions and their accurate evaluation is vital for the sustainable management of water resources. The assessment of such losses involves extremely difficult and original tasks because of the scarcity of data in countries with an arid climate. The main objective of this paper is to develop models for the simulation of pan-evaporation with the help of Penman and Hamon’s equations, Artificial Neural Networks (ANNs), and the Artificial Neuro Fuzzy Inference System (ANFIS). The results from five types of ANN models with different training functions were compared to find the best possible training function. The impact of using various input variables was investigated as an original contribution of this research. The average temperature and mean wind speed were found to be the most influential parameters. The estimation of parameters for Penman and Hamon’s equations was quite a daunting task. These parameters were estimated using a state of the art optimization algorithm, namely General Reduced Gradient Technique. The results of the Penman and Hamon’s equations, ANN, and ANFIS were compared. Thirty-eight years (from 1980 to 2018) of manually recorded pan-evaporation data regarding mean daily values of a month, including the relative humidity, wind speed, sunshine duration, and temperature, were collected from three gauging stations situated in Al Qassim, Saudi Arabia. The Nash and Sutcliffe Efficiency (NSE) and Mean Square Error (MSE) evaluated the performance of pan-evaporation modeling techniques. The study shows that the ANFIS simulation results were better than those of ANN and Penman and Hamon’s equations. The findings of the present research will help managers, engineers, and decision makers to sustainability manage natural water resources in warm-arid regions.

scholarly journals Using fuzzy logic for estimating monthly pan evaporation from meteorological data in Emara/ South of Iraq

2012 ◽  
Vol 9 (1) ◽  
pp. 133-140 ◽  
Author(s):  
Baghdad Science Journal
Keyword(s):  
Fuzzy Logic ◽  
Wind Speed ◽  
Relative Humidity ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Hydrological Cycle ◽  
Meteorological Data ◽  
Accurate Estimation ◽  
Pan Evaporation ◽  
Inference System

Evaporation is one of the major components of the hydrological cycle in the nature, thus its accurate estimation is so important in the planning and management of the irrigation practices and to assess water availability and requirements. The aim of this study is to investigate the ability of fuzzy inference system for estimating monthly pan evaporation form meteorological data. The study has been carried out depending on 261 monthly measurements of each of temperature (T), relative humidity (RH), and wind speed (W) which have been available in Emara meteorological station, southern Iraq. Three different fuzzy models comprising various combinations of monthly climatic variables (temperature, wind speed, and relative humidity) were developed to evaluate effect of each of these variables on estimation process. Two error statistics namely root mean squared error and coefficient of determination were used to measure the performance of the developed models. The results indicated that the model, whose input variables are T, W, and RH, perform the best for estimating evaporation values. In addition, the model which is dominated by (T) is significantly and distinctly helps to prove the predictive ability of fuzzy inference system. Furthermore, agreements of the results with the observed measurements indicate that fuzzy logic is adequate intelligent approach for modeling the dynamic of evaporation process.

Aridity Changes and Related Climatic Drivers in the Drylands of China during 1960–2019

2021 ◽  
Vol 60 (4) ◽  
pp. 607-617
Author(s):  
Jinqin Xu ◽  
Yan Zeng ◽  
Xinfa Qiu ◽  
Yongjian He ◽  
Guoping Shi ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Land Surface ◽  
Arid Regions ◽  
Climatic Factors ◽  
Sunshine Duration ◽  
Aridity Index ◽  
Maximum Temperature ◽  
The Impact ◽  
Precipitation And Temperature

AbstractDrylands cover about one-half of the land surface in China and are highly sensitive to climate change. Understanding climate change and its impact drivers on dryland is essential for supporting dryland planning and sustainable development. Using meteorological observations for 1960–2019, the aridity changes in drylands of China were evaluated using aridity index (AI), and the impact of various climatic factors [i.e., precipitation P; sunshine duration (SSD); relative humidity (RH); maximum temperature (Tmax); minimum temperature (Tmin); wind speed (WS)] on the aridity changes was decomposed and quantified. Results of trend analysis based on Sen’s slope estimator and Mann–Kendall test indicated that the aridity trends were very weak when averaged over the whole drylands in China during 1960–2019 but exhibited a significant wetting trend in hyperarid and arid regions of drylands. The AI was most sensitive to changes in water factors (i.e., P and RH), followed by SSD, Tmax, and WS, but the sensitivity of AI to Tmin was very small and negligible. Interestingly, the dominant climatic driver to AI change varied in the four dryland subtypes. The significantly increased P dominated the increase in AI in the hyperarid and arid regions. The significantly reduced WS and the significantly increased Tmax contributed more to AI changes than the P in the semiarid and dry subhumid regions of drylands. Previous studies emphasized the impact of precipitation and temperature on the global or regional dry–wet changes; however, the findings of this study suggest that, beyond precipitation and temperature, the impact of wind speed on aridity changes of drylands in China should be given equal attention.

Adaptive Neuro-fuzzy Inference System (ANFIS) Modelling in Energy System and Water Resources

2019 ◽  
pp. 117-133 ◽  
Author(s):  
P. A. Adedeji ◽  
S. O. Masebinu ◽  
S. A. Akinlabi ◽  
N. Madushele
Keyword(s):  
Water Resources ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Energy System ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals Simulating impact of over-grazing on grassland degradation using GIS: a case study in the Heihe River Basin, China

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Bingyu Wang ◽  
Takashi Oguchi ◽  
Lin Zhang
Keyword(s):  
Sustainable Development ◽  
Water Resources ◽  
Human Activities ◽  
Arid Regions ◽  
River Basins ◽  
Heihe River ◽  
Grassland Degradation ◽  
Inland River ◽  
The Impact ◽  
The Heihe River Basin

<p><strong>Abstract.</strong> Inland river basins in arid to semi-arid regions are widely distributed in Northwest China, Central Asia, Central Australia, and North Africa, and are often subject to significant human activities. The most distinctive natural feature of such basins is the shortage of water resources, and the pivotal reasons involve less precipitation and heavy evapotranspiration (ET). In recent years, intensive human activities also damage the natural environment of the basins. They result in many problems especially the deterioration of ecological environment which will lead to severe consequences such as desertification, sandstorm, the disappearance of wetlands, reduction of forest and grassland degradation. They prevent us from achieving the goal of sustainable development. How to balance economic development and ecosystem conservation and to realize the sense of sustainability in inland river basins will be vitally important.</p><p>The Heihe River is the second largest inland river in the Northwest of China with a long history development by human (Figure 1). Water resources from the river are crucial not only for the ecosystem but also for local human societies. The Heihe River Basin (HRB) is divided into three zones with different landscapes and natural environments. The upstream of HRB is the headstream which generates water resources mainly from glaciers and snow in Qilian Mountain. A large population of nomadic national minorities inhabits here and keeps animal husbandry as the primary production activity. In the early times, the Chinese government encouraged production activities to stimulate economic growth, and significant over-grazing and resultant severe grassland degradation occurred. Grassland is crucial for maintaining water resources especially in arid regions, without grasses most water will quickly evaporate into the air. Therefore, land resource management about grassland and the impact of human activities on the natural environment are of high research value in the HRB.</p><p>This research aims to investigate the impact of over-grazing on grassland degradation in the inland ecosystem of the HRB. The changes of grassland distribution were simulated under different over-grazing scenarios to provide a reference for resource management and the related decision-making process and to contribute to the sustainable development of the region.</p>

scholarly journals Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1958 ◽  
Author(s):  
Lilin Cheng ◽  
Haixiang Zang ◽  
Tao Ding ◽  
Rong Sun ◽  
Miaomiao Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Recurrent Neural Network ◽  
Fuzzy Inference ◽  
Ensemble Model ◽  
Probabilistic Forecasting ◽  
Inference System ◽  
Wind Speed Forecasting ◽  
Wind Power Integration ◽  
Neuro Fuzzy

Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as submodels for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deeplearningbased submodels. Lastly, variances are obtained from submodels and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting.

scholarly journals Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1548
Author(s):  
Marjana Čubranić-Dobrodolac ◽  
Libor Švadlenka ◽  
Svetlana Čičević ◽  
Aleksandar Trifunović ◽  
Momčilo Dobrodolac
Keyword(s):  
Traffic Accidents ◽  
Driving Simulator ◽  
Fuzzy Inference ◽  
Road Traffic ◽  
Space Perception ◽  
Road Traffic Accidents ◽  
Inference System ◽  
Interval Type ◽  
The Impact

A constantly increasing number of deaths on roads forces analysts to search for models that predict the driver’s propensity for road traffic accidents (RTAs). This paper aims to examine a relationship between the speed and space assessment capabilities of drivers in terms of their association with the occurrence of RTAs. The method used for this purpose is based on the implementation of the interval Type-2 Fuzzy Inference System (T2FIS). The inputs to the first T2FIS relate to the speed assessment capabilities of drivers. These capabilities were measured in the experiment with 178 young drivers, with test speeds of 30, 50, and 70 km/h. The participants assessed the aforementioned speed values from four different observation positions in the driving simulator. On the other hand, the inputs of the second T2FIS are space assessment capabilities. The same group of drivers took two types of space assessment tests—2D and 3D. The third considered T2FIS sublimates of all previously mentioned inputs in one model. The output in all three T2FIS structures is the number of RTAs experienced by a driver. By testing three proposed T2FISs on the empirical data, the result of the research indicates that the space assessment characteristics better explain participation in RTAs compared to the speed assessment capabilities. The results obtained are further confirmed by implementing a multiple regression analysis.

scholarly journals Lag Variables in Nitrogen Oxide Concentration Modelling: A Case Study in Wrocław, Poland

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1293
Author(s):  
Joanna A. Kamińska ◽  
Fernando Jiménez ◽  
Estrella Lucena-Sánchez ◽  
Guido Sciavicco ◽  
Tomasz Turek
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Traffic Flow ◽  
Sunshine Duration ◽  
Delayed Effect ◽  
Hourly Data ◽  
Random Forest Models ◽  
Flow Intensity ◽  
Lag Variables ◽  
The Impact

Due to the unwavering interest of both residents and authorities in the air quality of urban agglomerations, we pose the following question in this paper: What impact do current and past meteorological factors and traffic flow intensity have on air quality? What is the impact of lagged variables on the fit of an explanation model, and how do they affect its ability to predict? We focused on NO2 and NOx concentrations, and conducted this research using hourly data from the city of Wrocław (western Poland) from 2015 to 2017; we used multi-objective optimization to determine the optimal delays. It turned out that for both NO2 and NOx, the past values for traffic flow, wind speed, and sunshine duration are more important than the current ones. We built random forest models on each of the pollutants for both the current and past values and discovered that including a lagged variable increases the resulting R2 from 0.51 to 0.56 for NO2 and from 0.46 to 0.52 for NOx. We also analyzed the feature importance in each model, and found that for NO2, a wind speed delay of more than three hours causes a significant decrease, while the importance of relative humidity increases with a seven-hour delay; likewise, wind speed increases the importance for NOx prediction with a two-hour delay. We concluded that, in pollutant concentration modeling, the possibility of a delayed effect of the independent variables should always be considered, because it can significantly increase the performance of the model and suggest unexpected relationships or dependencies.

scholarly journals Exploring the Influence of Truck Proportion on Freeway Traffic Safety Using Adaptive Network-Based Fuzzy Inference System

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Shiwen Zhang ◽  
Yingying Xing ◽  
Jian Lu ◽  
H. Michael Zhang
Keyword(s):  
Traffic Safety ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Traffic Volume ◽  
Learning Ability ◽  
Adaptive Network ◽  
Freeway Traffic ◽  
Inference System ◽  
Traffic Conflicts ◽  
The Impact

The truck operation of freeway has an impact on traffic safety. In particular, the gradually increasing in truck proportion will inevitably affect the freeway traffic operation of different traffic volume. In this paper, VISSIM simulation is used to supply the field data and orthogonal experimental is designed for calibrate the simulation data. Then, SSAM modeling is combined to analyze the impact of truck proportion on traffic flow parameters and traffic conflicts. The serious and general conflict prediction model based on the Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed to determine the impact of the truck proportion on freeway traffic safety. The results show that when the truck proportion is around 0.4 under 3200 veh/h and 0.6 under 2600 veh/h, there are more traffic conflicts and the number of serious conflicts is more than the number of general conflicts, which also reflect the relationship between truck proportion and traffic safety. Under 3000 veh/h, travel time and average delay increasing while mean speed and mean speed of small car decreases with truck proportion increases. The mean time headway rises largely with the truck proportion increasing above 3000 veh/h. The speed standard deviation increases initially and then fall with truck proportion increasing. The lane-changing decreases while truck proportion increasing. In addition, ANFIS can accurately determine the impact of truck proportion on traffic conflicts under different traffic volume, and also validate the learning ability of ANFIS.

Sign in / Sign up

Export Citation Format

Share Document