Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Insurability Challenges Under Uncertainty: An Attempt to Use the Artificial Neural Network for the Prediction of Losses from Natural Disasters

The main difficulty for natural disaster insurance derives from the uncertainty of an event’s damages. Insurers cannot precisely appreciate the weight of natural hazards because of risk dependences. Insurability under uncertainty first requires an accurate assessment of entire damages. Insured and insurers both win when premiums calculate risk properly. In such cases, coverage will be available and affordable. Using the artificial neural network – a technique rooted in artificial intelligence - insurers can predict annual natural disaster losses. There are many types of artificial neural network models. In this paper we use the multilayer perceptron neural network, the most accommodated to the prediction task. In fact, if we provide the natural disaster explanatory variables to the developed neural network, it calculates perfectly the potential annual losses for the studied country.Natural disaster losses, Insurability, Uncertainty, Multilayer perceptron neural network, Prediction.

SEABEM: An Artificial Intelligence Powered Web Application To Predict Cover Crop Biomass

SEABEM, the Stacked Ensemble Algorithms Biomass Estimator Model, is a web application with a stacked ensemble of Machine Learning (ML) algorithms running on the backend to predict cover crop biomass for locations in Sub-Saharan. The SEABEM model was developed using a previously developed database of crop growth and yield that included site characteristics such as latitude, longitude, soil texture (sand, silt, and clay percentages), temperature, and precipitation. The goal of SEABEM is to provide global farmers, mainly small-scale African farmers, the knowledge they need before practicing and benefiting from cover crops while avoiding the expensive and time-consuming operations that come with blind on-site experimentation. The results were derived from comparing ten different ML algorithms, demonstrating the dominance of ensemble models. The top-performing models - Gradient Boost Regressor, Extra Trees Regressor, and Random Forest Regressor - were stacked together into one model to power the SEABEM web application. As the project is open-sourced on a GitHub repository, the GitHub community is available for others to improve the project. The SEABEM web application is also accessible and valuable to anyone worldwide as its development came from global data

Predicting Groundwater Fluctuations in Hard Rock Watersheds – An Application of Data Visualizations and Machine Learning Algorithms

Groundwater sustainability is critical to the future of agriculture and food security. The challenges are not only technical but have important social, economic, institutional and policy implications. The objective of this research is to predict groundwater levels in rural wells, allowing farmers to use their groundwater more sustainably. Data visualizations and machine learning algorithms are used to examine data collected over a five-year period from rural rock water basins in the northwestern part of India. Preliminary examination shows that the weekly collected time variable proved to be the single most valuable predictor of groundwater level, as it included implied seasonal changes in weather patterns and pumping patterns. However, due to limited rainfall outside of the monsoon season, it proved a less potent variable than previously expected

Divergences for prototype-based classification and causal structure discovery:Theory and application to natural datasets

Dit proefschrift bestaat uit twee delen. In het eerste deel beschrijven we hoe de op prototypen gebaseerde classificator LVQ uitgebreid kan worden door gebruik te maken van maten uit de informatie theorie. Daarnaast vergelijken we verschillende manieren van datarepresentatie in deze LVQ configuratie, in dit geval histogrammen van foto’s, SIFT- en SURF-kenmerken. We tonen hoe hiervoor een enkele gecombineerde afstandsmaat kan worden geformuleerd, door de afzonderlijke afstandsmaten samen te nemen. In het tweede deel onderzoeken we het vinden van causale verbanden en toepassingen op problemen die uit het leven zijn gegrepen. Daarnaast verkennen we de combinatie met relevantie leren in LVQ en tonen we enkele toepassingen

Subnational Map Of Poverty Generated From Remote-Sensing Data In Africa: Using Machine Learning Models And Advanced Regression Methods For Poverty Estimation

According to the 2020 poverty estimates from the World Bank, it is estimated that 9.1% - 9.4% of the global population lived on less than $1.90 per day. It is estimated that the Covid-19 pandemic further aggravated the issue by pushing more than 1$1.90 per day (WorldBank, 2020). To provide help and formulate effective measures, poverty needs to be located as exact as possible. For this purpose, it was investigated whether regression methods with aggregated remote-sensing data could be used to estimate poverty in Africa. Therefore, five distinct regression frameworks were compared regarding their R2 value and the mean absolute relative percentage error when estimating poverty from aggregated remote-sensing data in continental Africa. A total of 12 regression models were developed at the three poverty rates at the $1.90,$3.20, and $5.50 income level per day and can be divided into direct models, two-step models, and ensemble models. It was found that ensemble methods perform better than simpler models, with an R2 value of 0.74 for the ensemble neural net and 0.80 for the ensemble xgboost model. The best performing one step model is the kernel ridge regression with an R2 of 0.72, while the remaining frameworks of this type all perform worse. Bayesian ridge regression models consistently performed the worst compared to the other frameworks under investigation. It was found that it the model estimations were most stable at the daily income level of$1.90 and $3.20, which can be explained by the increasingly skewed distribution of target values for higher poverty thresholds. Overall, it was found that xgboost, kernel ridge regression and artificial neural networks perform better than the other models

Satellite Data and Supervised Learning to Prevent Impact of Drought on Crop Production: Meteorological Drought

Reiterated and extreme weather events pose challenges for the agricultural sector. The convergence of remote sensing and supervised learning (SL) can generate solutions for the problems arising from climate change. SL methods build from a training set a function that maps a set of variables to an output. This function can be used to predict new examples. Because they are nonparametric, these methods can mine large quantities of satellite data to capture the relationship between climate variables and crops, or successfully replace autoregressive integrated moving average (ARIMA) models to forecast the weather. Agricultural indices (AIs) reflecting the soil water conditions that influence crop conditions are costly to monitor in terms of time and resources. So, under certain circumstances, meteorological indices can be used as substitutes for AIs. We discuss meteorological indexes and review SL approaches that are suitable for predicting drought based on historical satellite data. We also include some illustrative case studies. Finally, we will survey rainfall products existing at the web and some alternatives to process the data: from high-performance computing systems able to process terabyte-scale datasets to open source software enabling the use of personal computers