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


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.

Diffusion Models Made Easy


In the recent past, I have talked about GANs and VAEs as two important Generative Models that have found a lot of success and recognition. GANs work great for multiple applications however, they are difficult to train, and their output lack diversity due to several challenges such as mode collapse and vanishing gradients to name a few. Although VAEs have the most solid theoretical foundation however, the modelling of a good loss function is a challenge in VAEs which makes their output to be suboptimal. There is another set of techniques which originate from probabilistic likelihood estimation methods and take inspiration from physical phenomenon; it is called, Diffusion Models. The central idea behind Diffusion Models comes from the thermodynamics of gas molecules whereby the molecules diffuse from high density to low density areas.

How to model uncertainty with Dempster-Shafer's theory?


Dempster-Shafer's theory models the uncertainty present in the data and model and helps in building robust machine learning models.

9 Completely Free Statistics Courses for Data Science


This is a complete Free course for statistics. In this course, you will learn how to estimate parameters of a population using sample statistics, hypothesis testing and confidence intervals, t-tests and ANOVA, correlation and regression, and chi-squared test. This course is taught by industry professionals and you will learn by doing various exercises.

Bayesian Machine Learning - DataScienceCentral.com


As a data scientist, I am curious about knowing different analytical processes from a probabilistic point of view. There are two most popular ways of looking into any event, namely Bayesian and Frequentist . When Frequentist researchers look at any event from frequency of occurrence, Bayesian researchers focus more on probability of events happening. I will try to cover as much theory as possible with illustrative examples and sample codes so that readers can learn and practice simultaneously. As we all know Baye's rule is one of the most popular probability equation, which is defined as: P(a given b) P(a intersection b) / P(b) ….. (1) Here a and b are events that have taken place.

Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography


Photoacoustic tomography (PAT) is a hybrid biomedical imaging modality based on the photoacoustic effect [6, 44, 32]. In PAT, the imaged target is illuminated with a short pulse of light. Absorption of light creates localized areas of thermal expansion, resulting in localized pressure increases within the imaged target. This pressure distribution, called the initial pressure, relaxes as broadband ultrasound waves that are measured on the boundary of the imaged target. In the inverse problem of PAT, the initial pressure distribution is estimated from a set of measured ultrasound data.

The Application of Machine Learning Techniques for Predicting Match Results in Team Sport: A Review

Journal of Artificial Intelligence Research

Predicting the results of matches in sport is a challenging and interesting task. In this paper, we review a selection of studies from 1996 to 2019 that used machine learning for predicting match results in team sport. Considering both invasion sports and striking/fielding sports, we discuss commonly applied machine learning algorithms, as well as common approaches related to data and evaluation. Our study considers accuracies that have been achieved across different sports, and explores whether evidence exists to support the notion that outcomes of some sports may be inherently more difficult to predict. We also uncover common themes of future research directions and propose recommendations for future researchers. Although there remains a lack of benchmark datasets (apart from in soccer), and the differences between sports, datasets and features makes between-study comparisons difficult, as we discuss, it is possible to evaluate accuracy performance in other ways. Artificial Neural Networks were commonly applied in early studies, however, our findings suggest that a range of models should instead be compared. Selecting and engineering an appropriate feature set appears to be more important than having a large number of instances. For feature selection, we see potential for greater inter-disciplinary collaboration between sport performance analysis, a sub-discipline of sport science, and machine learning.

Bayesian Estimation of Nelson-Siegel model using rjags R package


To leave a comment for the author, please follow the link and comment on their blog: K & L Fintech Modeling. R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job. Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. To leave a comment for the author, please follow the link and comment on their blog: K & L Fintech Modeling.

Mathematics for Deep Learning (Part 7)


In the road so far, we have talked about MLP, CNN, and RNN architectures. These are discriminative models, that is models that can make predictions. Discriminative models essentially learn to estimate a conditional probability distribution p( x); that is, given a value, they try to predict the outcome based on what they learned about the probability distribution of x. Generative models are architectures of neural networks that learn the probability distribution of the data and learn how to generate data that seems to come from that probability distribution. Creating synthetic data is one use of generative models, but is not the only one.