Liwan Liyanage

Globally, hundreds of millions of wildlife are killed on roads and railways annually. Exclusion fencing is the most effective method to reduce the rate of wildlife-vehicle collisions (WVC). However, fencing is limited by installation and maintenance costs, topographical and design constraints, aesthetics, and the ability of many species to traverse fencing. Numerous alternative technological solutions, such as wildlife deterrents, warning systems that detect wildlife and/or vehicles, and signage, are therefore being developed and tested. However, there is no globally consistent framework to classify and select different mitigation measures, causing confusion and limiting adoption of effective solutions. In this synthesis, we provide a schema that classifies all current and foreseeable future solutions based on whether they are physical or signal-based measures, the intended receiver (wildlife or vehicle operator) and, if signal-based, the signal(s) source. We illustrate the schema with case studies and a framework to identify and select WVC mitigation measures.

With the advancement of technology, time series data are automatically collected without human intervention. As the data collection process becomes effortless, the next change encountered is to identify the best method to forecast time series data with high accuracies. In this regard, hybrid approaches have gained much attention where the strengths of two approaches can be combined to lessen the weaknesses of each individual approach. When exploring the features of time series data, some depict repetitive patterns and also data can be observed at several levels. The repeating curves can be considered as the higher level, whereas each individual observation can be considered as the lower level. Thus, in order to handle the data in a more effective way, the series can be handled at two levels by giving prominence to the features of each level separately. This paper proposes a novel algorithm named two level neuro-functional forecaster, which is capable of handling data at two levels, hybridizing a statistical approach with an artificial intelligence approach, to gain high accuracy levels. In addition, as this approach handles data at two levels, data sparsity at a particular level can be accommodated at the other level. To apply this algorithm to a real world dataset, electricity demand data in Sri Lanka was considered where the series consisted of daily load curves with repetitive pattern across the days. The proposed hybrid algorithm, outperforms the two approaches when individually used, with a MAPE of 3.324% for a year.

Air quality data sets are widely used in numerous analyses. Missing values are ubiquitous in air quality data sets as the data are collected through sensors. Recovery of missing data is a challenging task in the data preprocessing stage. This task becomes more challenging in time series data as time is an implicit variable that cannot be ignored. Even though existing methods to deal with missing data in time series perform well in situations where the percentage of missing values is relatively low and the gap size is small, their performances are reasonably lower when it comes to large gaps. This paper presents a novel algorithm based on seasonal decomposition and elastic net regression to impute large gaps of time series data when there exist correlated variables. This method outperforms several other existing univariate approaches namely Kalman smoothing on ARIMA models, Kalman smoothing on structural time series models, linear interpolation, and mean imputation in imputing large gaps. However, this is applicable only when there exists one or more correlated variables with the time series with large gaps.

Spatio-temporal data is voluminuous, and its analysis is complex. This review of the strengths and weaknesses of selected widely applicable algorithms aims to assist the reader with the choice of methods such as clustering and prediction of real time spatio-temporal data for different types of applications.