scholarly journals Application of Artificial Intelligence Models for Evapotranspiration Prediction along the Southern Coast of Turkey

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Mohammed Majeed Hameed ◽  
Mohamed Khalid AlOmar ◽  
Siti Fatin Mohd Razali ◽  
Mohammed Abd Kareem Khalaf ◽  
Wajdi Jaber Baniya ◽  
...  
Keyword(s):  
Predictive Model ◽  
Hydrological Cycle ◽  
Irrigation Management ◽  
Great Influence ◽  
Resource Planning ◽  
Forecast Model ◽  
Accurate Estimation ◽  
Statistical Measures ◽  
Artificial Intelligence Models ◽  
Learning Machine

Reference evapotranspiration ET o   is one of the most significant factors in the hydrological cycle since it has a great influence on water resource planning and management, agriculture and irrigation management, and other processes in the hydrological sector. In this study, an efficient and local predictive model was established to forecast the monthly mean ET o   t over Turkey based on the data collected from 35 locations. For this purpose, twenty input combinations including hydrological and geographical parameters were introduced to three different approaches called multiple linear regression MLR , random forest RF , and extreme learning machine ELM . Moreover, in this study, large investigation was done, involving the establishment of 60 models and their assessment using ten statistical measures. The outcome of this study revealed that the ELM approach achieved high accurate estimation in accordance with the Penman–Monteith formula as compared to other models such as MLR and RF . Moreover, among the 10 statistical measures, the uncertainty at 95% U 95 indicator showed an excellent ability to select the best and most efficient forecast model. The superiority of ELM in the prediction of mean monthly ET o   over MLR and RF approaches is illustrated in the reduction of the U 95 parameter to 49.02% and 34.07% for RF and MLR models, respectively. Furthermore, it is possible to develop a local predictive model with the help of computer to estimate the ET o   using the simplest and cheapest meteorological and geographical variables with acceptable accuracy.

A Predictive Model of Intrinsic Factors Associated with Long-Stay Nursing Home Care After Hospitalization

2020 ◽  
pp. 105477382098527
Author(s):  
Jane Flanagan ◽  
Marie Boltz ◽  
Ming Ji
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Receiver Operating Characteristic ◽  
Predictive Model ◽  
Operating Characteristic ◽  
Resource Planning ◽  
Term Care ◽  
Intrinsic Factors ◽  
Patients At Risk ◽  
Receiver Operating

We aimed to build a predictive model with intrinsic factors measured upon admission to skilled nursing facilities (SNFs) post-acute care (PAC) to identify older adults transferred from SNFs to long-term care (LTC) instead of home. We analyzed data from Massachusetts in 23,662 persons admitted to SNFs from PAC in 2013. Explanatory logistic regression analysis identified single “intrinsic predictors” related to LTC placement. To assess overfitting, the logistic regression predictive model was cross-validated and evaluated by its receiver operating characteristic (ROC) curve. A 12-variable predictive model with “intrinsic predictors” demonstrated both high in-sample and out-of-sample predictive accuracy in the receiver operating characteristic ROC and area under the ROC among patients at risk of LTC placement. This predictive model may be used for early identification of patients at risk for LTC after hospitalization in order to support targeted rehabilitative approaches and resource planning.

scholarly journals POLARIMETRIC CALIBRATION OF L-BAND AIRBORNE SAR DATA

2020 ◽  
Vol V-1-2020 ◽  
pp. 369-374
Author(s):  
A. Maiti ◽  
S. Kumar ◽  
V. Tolpekin ◽  
S. Agarwal
Keyword(s):  
Accurate Estimation ◽  
Distortion Model ◽  
Propagation Of Uncertainty ◽  
Statistical Measures ◽  
Dry Lake ◽  
L Band ◽  
Airborne Sar ◽  
Optimal Kernel ◽  
Target Characteristics ◽  
The Relationship

Abstract. The PolSAR calibration ensures that the relationship between the SAR observations and the target characteristics on the ground are consistent and resembles the theoretical estimation which in turn improves the overall data quality. Essentially, calibration prevents the propagation of uncertainty into further analysis to characterise the target. In this study, the UAVSAR L-Band data of Rosamond dry lake bed has been calibrated. The calibration of amplitude and phase are carried out with the help of the corner reflector array present in the Rosamond site. The dataset is further calibrated for the crosstalk and channel imbalance using the Quegan’s distortion model. Since the crosstalk distortion model requires an accurate estimation of the covariance matrix, the optimal kernel size for the its computation is selected based on the distortion model behaviour with varying window sizes. Furthermore, the effectiveness of the calibration process has been studied using polarimetric signatures and other statistical measures.

Lithium-ion battery state of health estimation based on improved deep extreme learning machine

2021 ◽  
pp. 1-12
Author(s):  
Yu Zhang ◽  
Wanwan Zeng ◽  
Chun Chang ◽  
Qiyue Wang ◽  
Si Xu
Keyword(s):  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Lithium Ion ◽  
Accurate Estimation ◽  
Time Interval ◽  
State Of Health ◽  
Discharge Data ◽  
Ergodic Method ◽  
Discharge Temperature ◽  
Learning Machine

Abstract Accurate estimation of the state of health (SOH) is an important guarantee for safe and reliable battery operation. In this paper, an online method based on indirect health features (IHF) and sparrow search algorithm fused with deep extreme learning machine (SSA-DELM) of lithium-ion batteries is proposed to estimate SOH. Firstly, the temperature and voltage curves in the battery discharge data are acquired, and the optimal intervals are obtained by ergodic method. Discharge temperature difference at equal time intervals (DTD-ETI) and discharge time interval with equal voltage difference (DTI-EVD) are extracted as IHF. Then, the input weights and hidden layer thresholds of the DELM algorithm are optimized using SSA, and the SSA-DELM model is applied to the estimation of battery's SOH. Finally, the established model is experimentally validated using the battery data, and the results show that the method has high prediction accuracy, strong algorithmic stability and good adaptability.

scholarly journals Assessment of Spatiotemporal Variability and Trend Analysis of Reference Crop Evapotranspiration for the southern region of Peninsular India

2021 ◽  
Author(s):  
Jayashree Tenkila Ramachandra ◽  
Subba Reddy Nandanavana Veerappa ◽  
Dinesh Acharya Udupi
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Water Resource Management ◽  
Spatiotemporal Variability ◽  
Trend Test ◽  
Accurate Estimation ◽  
Peninsular India ◽  
Climatic Zones ◽  
Statistical Measures

Abstract Accurate estimation of reference evapotranspiration (ET0) is an essential requirement for water resource management and scheduling agricultural activities. Several empirical methods have been employed in estimating ET0 across diverse climate regimes over the past decades. The Python implementation for estimation of daily and monthly ET0 values of representative stations of ten agro-climatic zones of Karnataka from 1979 through 2014 using the standard FAO Penman-Monteith method was carried out. The assessment of temporal and spatial variability of monthly ET0 values across the various agro-climatic zones done by the various statistical measures revealed that the variation in spatial ET0 values was higher than temporal indicating major differentiation of ET0 values was with respect to the stations rather than years under study. The non-parametric Mann-Kendall test conducted at 1% significance level on the annual ET0 values revealed that statistically significant increasing trend was observed for all the ten stations during the study period. The trend test conducted on the climate variables like mean air temperature, wind speed, relative humidity and solar radiation signify their influence the annual ET0 values. The magnitude changes in the trends detected by the Theil Sen’s slope indicated that increasing values of mean temperature, solar radiation and decreasing values of relative humidity predominantly contributed to the annual upward trend in ET0 values for the 10 stations. A trivial impact of wind speed on annual ET0 values was observed for the stations. Kalburgi and Udupi stations exhibited positive ET0 trend with the highest and lowest annual values among ten stations.

scholarly journals Sistema web para estimativa de evapotranspiração potencial por diferentes métodos

2019 ◽  
Vol 41 ◽  
pp. 22
Author(s):  
Taison Anderson Bortolin ◽  
Lucas Moraes dos Santos ◽  
Adriano Gomes da Silva ◽  
Vania Elisabete Schneider
Keyword(s):  
Agricultural Production ◽  
Web Application ◽  
Hydrological Cycle ◽  
Reference Evapotranspiration ◽  
Great Influence ◽  
Mathematical Methods ◽  
Indirect Methods ◽  
Computational Tools ◽  
The Face ◽  
Weather Stations

One of the parameters of the hydrological cycle that has great influence in water management and agricultural production is evapotranspiration. Finding ways to restore this amount of lost water to the ground is essential, whether from rain or irrigation techniques. In general, indirect methods are used to determine evapotranspiration, especially Penman-Monteith. However, the method requires a large number of variables, requiring the use of other indirect methods, less demanding in relation to the required data. Due to a great number of methods to be compared it is interesting to make use of computational tools that allow to automate these calculations. In the face of the problem, the development of a web application that calculates the reference evapotranspiration (ETo) in an automated way, from the several mathematical methods found in the literature, was glimpsed. The system provides the user with daily ETo results (mm / month) for a given period of time, as well as the daily average (mm / month), for the month, and monthly (mm / month) for a given month during the period, using up-to-date data from INMET-operated weather stations. The application facilitates the calculation process minimizing the estimation time and contributing to the analysis of the results by the user.

scholarly journals Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Majid Niazkar ◽  
Mohammad Zakwan
Keyword(s):  
Artificial Intelligence ◽  
Conventional Method ◽  
Evaluation Criteria ◽  
Resource Planning ◽  
Optimization Techniques ◽  
Computational Techniques ◽  
Multigene Genetic Programming ◽  
Artificial Intelligence Models ◽  
Rating Curves ◽  
Artificial Neural Network Ann

Estimation of discharge flowing through rivers is an important aspect of water resource planning and management. The most common way to address this concern is to develop stage-discharge relationships at various river sections. Various computational techniques have been applied to develop discharge ratings and improve the accuracy of estimated discharges. In this regard, the present study explores the application of the novel hybrid multigene genetic programming-generalized reduced gradient (MGGP-GRG) technique for estimating river discharges for steady as well as unsteady flows. It also compares the MGGP-GRG performance with those of the commonly used optimization techniques. As a result, the rating curves of eight different rivers were developed using the conventional method, evolutionary algorithm (EA), the modified honey bee mating optimization (MHBMO) algorithm, artificial neural network (ANN), MGGP, and the hybrid MGGP-GRG technique. The comparison was conducted on the basis of several widely used performance evaluation criteria. It was observed that no model outperformed others for all datasets and metrics considered, which demonstrates that the best method may be different from one case to another one. Nevertheless, the ranking analysis indicates that the hybrid MGGP-GRG model overall performs the best in developing stage-discharge relationships for both single-value and loop rating curves. For instance, the hybrid MGGP-GRG technique improved sum of square of errors obtained by the conventional method between 4.5% and 99% for six out of eight datasets. Furthermore, EA, the MHBMO algorithm, and artificial intelligence (AI) models (ANN and MGGP) performed satisfactorily in some of the cases, while the idea of combining MGGP with GRG reveals that this hybrid method improved the performance of MGGP in this specific application. Unlike the black box nature of ANN, MGGP offers explicit equations for stream rating curves, which may be counted as one of the advantages of this AI model.

scholarly journals The Value of Meteorological Data in Optimizing the Pattern of Physical Load—A Forecast Model of Rowing Pacing Strategy

2021 ◽  
Vol 19 (1) ◽  
pp. 320
Author(s):  
Tian Yan ◽  
Xiaodong Zhu ◽  
Xuesong Ding ◽  
Liming Chen
Keyword(s):  
Meteorological Data ◽  
Great Influence ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Physical Load ◽  
Forecast Performance ◽  
Outdoor Sports ◽  
Pacing Strategy ◽  
Different Temperatures ◽  
Meteorological Features

Mastering the information of arena environment is the premise for athletes to optimize their patterns of physical load. Therefore, improving the forecast accuracy of the arena conditions is an urgent task in competitive sports. This paper excavates the meteorological features that have great influence on outdoor events such as rowing and their influence on the pacing strategy. We selected the meteorological data of Tokyo from 1979 to 2020 to forecast the meteorology during the Tokyo 2021 Olympic Games, analyzed the athletes’ pacing choice under different temperatures, humidity and sports levels, and then recommend the best pacing strategy for rowing teams of China. The model proposed in this paper complements the absence of meteorological features in the arena environment assessment and provides an algorithm basis for improving the forecast performance of pacing strategies in outdoor sports.

scholarly journals Estimation at Completion Simulation Using the Potential of Soft Computing Models: Case Study of Construction Engineering Projects

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 190 ◽  
Author(s):  
Enas Fathi Taher Al Hares ◽  
Cenk Budayan
Keyword(s):  
United Arab Emirates ◽  
Construction Projects ◽  
Harmony Search ◽  
Coupled Model ◽  
Forecast Model ◽  
Project Construction ◽  
Intelligence Models ◽  
Artificial Neural Network Ann ◽  
Learning Machine ◽  
High Level

“Estimation at completion” (EAC) is a manager's projection of a project's total cost at its completion. It is an important tool for monitoring a project's performance and risk. Executives usually make high-level decisions on a project, but they may have gaps in the technical knowledge which may cause errors in their decisions. In this current study, the authors implemented new coupled intelligence models, namely global harmony search (GHS) and brute force (BF) integrated with extreme learning machine (ELM) for modeling the project construction estimation at completion. GHS and BF were used to abstract the substantial influential attributes toward the EAC dependent variable, whereas the effectiveness of ELM as a novel predictive model for the investigated application was demonstrated. As a benchmark model, a classical artificial neural network (ANN) was developed to validate the new ELM model in terms of the prediction accuracy. The predictive models were applied using historical information related to construction projects gathered from the United Arab Emirates (UAE). The study investigated the application of the proposed coupled model in determining the EAC and calculated the tendency of a change in the forecast model monitor. The main goal of the investigated model was to produce a reliable trend of EAC estimates which can aid project managers in improving the effectiveness of project costs control. The results demonstrated a noticeable implementation of the GHS-ELM and BF-ELM over the classical and hybridized ANN models.

scholarly journals Spatio-Temporal Analysis of Wetland Changes Using a Kernel Extreme Learning Machine Approach

2018 ◽  
Vol 10 (7) ◽  
pp. 1129 ◽  
Author(s):  
Yi Lin ◽  
Jie Yu ◽  
Jianqing Cai ◽  
Nico Sneeuw ◽  
Fengting Li
Keyword(s):  
Extreme Learning Machine ◽  
Human Activities ◽  
Migratory Birds ◽  
Learning Algorithm ◽  
Great Influence ◽  
Temporal Analysis ◽  
Scientific Basis ◽  
Kernel Extreme Learning Machine ◽  
Spatio Temporal ◽  
Learning Machine

Natural wetland ecosystems provide not only important habitats for many wildlife species, but also food for migratory and resident animals. In Shanghai, the Chongming Dongtan International Wetland, located at the mouth of the Yangtze River, plays an important role in maintaining both ecosystem health and ecological security of the island. Meanwhile it provides an especially important stopover and overwintering site for migratory birds, being located in the middle of the East Asian-Australasian Flyway. However, with the increase in development intensity and human activities, this wetland suffers from increasing environmental pressure. On the other hand, biological succession in the mudflat wetland makes Chongming Dongtan a rapidly developing and rare ecosystem in the world. Therefore, studying the wetland spatio-temporal change is an important precondition for analyzing the relationship between wetland evolution processes and human activities. This paper presents a novel method for analyzing land-use/cover changes (LUCC) on Chongming Dongtan wetland using multispectral satellite images. Our method mainly takes advantages of a machine learning algorithm, named the Kernel Extreme Learning Machine (K-ELM), which is applied to distinguish between different objects and extract their information from images. In the K-ELM, the kernel trick makes it more stable and accurate. The comparison between K-ELM and three other conventional classification methods indicates that the proposed K-ELM has the highest overall accuracy, especially for distinguishing between Spartina alternflora, Scirpus mariqueter, and Phragmites australis. Meanwhile, its efficiency is remarkable as well. Then a total of eight Landsat TM series images acquired from 1986 to 2013 were used for the LUCC analysis with K-ELM. According to the classification result, the change detection and spatio-temporal quantitative analysis were performed. The specific analysis of different objects are significant for learning about the historical changes to Chongming Dongtan and obtaining the evaluation rules. Generally, the rapid speed of Chongming Dongtan’s urbanization brought about great influence with respect to natural resources and the environment. Integrating the results into the ecological analysis and ecological regional planning of Dongtan could provide a reliable scientific basis for rational planning, development, and the ecological balance and regional sustainability of the wetland area.

scholarly journals Development of a screening method to assess flood risk on Danish national roads and highway systems

2011 ◽  
Vol 63 (12) ◽  
pp. 2957-2966 ◽  
Author(s):  
N. H. Nielsen ◽  
M. R. A. Larsen ◽  
S. F. Rasmussen
Keyword(s):  
Flood Risk ◽  
Preferential Flow ◽  
Screening Method ◽  
Great Influence ◽  
Drainage System ◽  
Accurate Estimation ◽  
Volume Data ◽  
Large Area ◽  
Terrain Model ◽  
Upstream Catchment

A method to assess flood risk on Danish national roads in a large area in the middle and southern part of Jutland, Denmark, was developed for the Danish Road Directorate. Flood risk has gained renewed focus due to the climate changes in recent years and extreme rain events are expected to become more frequent in the future. The assessment was primarily based on a digital terrain model (DTM) covering 7,500 km2 in a 1.6 × 1.6 m grid. The high-resolution terrain model was chosen in order to get an accurate estimation of the potential flooding in the road area and in the immediate vicinity, but also put a high requirement on the methods, hardware and software applied. The outcome of the analysis was detailed maps (as GIS layers) illustrating the location of depressions with depths, surface area and volume data for each depression. Furthermore, preferential flow paths, catchment boundaries and ranking of each depression were calculated. The ranking was based on volume of depressions compared with upstream catchment and a sensitivity analysis of the runoff coefficient. Finally, a method for assessing flood risk at a more advanced level (hydrodynamic simulation of surface and drainage) was developed and used on a specific blue spot as an example. The case study shows that upstream catchment, depressions, drainage system, and use of hydrodynamic calculations have a great influence on the result. Upstream catchments can contribute greatly to the flooding.

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