32 research outputs found
Evaluation of random forests and Prophet for daily streamflow forecasting
We assess the performance of random forests and Prophet in
forecasting daily streamflow up to seven days ahead in a river in the US.
Both the assessed forecasting methods use past streamflow observations, while
random forests additionally use past precipitation information. For
benchmarking purposes we also implement a naïve method based on the
previous streamflow observation, as well as a multiple linear regression
model utilizing the same information as random forests. Our aim is to
illustrate important points about the forecasting methods when implemented
for the examined problem. Therefore, the assessment is made in detail at a
sufficient number of starting points and for several forecast horizons. The
results suggest that random forests perform better in general terms, while
Prophet outperforms the naïve method for forecast horizons longer than
three days. Finally, random forests forecast the abrupt streamflow
fluctuations more satisfactorily than the three other methods.</p
Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow
We assess the performance of the recently introduced Prophet model in
multi-step ahead forecasting of monthly streamflow by using a large dataset.
Our aim is to compare the results derived through two different approaches.
The first approach uses past information about the time series to be
forecasted only (standard approach), while the second approach uses exogenous
predictor variables alongside with the use of the endogenous ones. The
additional information used in the fitting and forecasting processes includes
monthly precipitation and/or temperature time series, and their forecasts
respectively. Specifically, the exploited exogenous (observed or forecasted)
information considered at each time step exclusively concerns the time of
interest. The algorithms based on the Prophet model are in total four. Their
forecasts are also compared with those obtained using two classical
algorithms and two benchmarks. The comparison is performed in terms of four
metrics. The findings suggest that the compared approaches are equally
useful.</p
Predicting Fishing Effort and Catch Using Semantic Trajectories and Machine Learning
In this paper we explore a unique, high-value spatio-temporal dataset that results from the fusion of three data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed), the corresponding fish catch reports (i.e., the quantity and type of fish caught), and relevant environmental data. The result of that fusion is a set of semantic trajectories describing the fishing activities in Northern Adriatic Sea over two years. We present early results from an exploratory analysis of these semantic trajectories, as well as from initial predictive modeling using Machine Learning. Our goal is to predict the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation useful for fisheries management. Our predictive results are preliminary in both the temporal data horizon that we are able to explore and in the limited set of learning techniques that are employed on this task. We discuss several approaches that we plan to apply in the near future to learn from such data, evidence, and knowledge that will be useful for fisheries management. It is likely that other centers of intense fishing activities are in possession of similar data and could use the methods similar to the ones proposed here in their local context
Twenty-three unsolved problems in hydrology (UPH) – a community perspective
This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through on-line media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focussed on process-based understanding of hydrological variability and causality at all space and time scales.
Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come
Supplementary material for the paper "Variable selection in time series forecasting using random forests"
Data and code to reproduce the paper "Variable selection in time series forecasting using random forests
Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS
Post-processing of hydrological model simulations using machine learning algorithms can be applied to quantify the uncertainty of hydrological predictions. Combining multiple diverse machine learning algorithms (referred to as base-learners) using stacked generalization (stacking, i.e. a type of ensemble learning) is considered to improve predictions relative to the base-learners. Here we propose stacking of quantile regression and quantile regression forests. Stacking is performed by minimising the interval score of the quantile predictions provided by the ensemble learner, which is a linear combination of quantile regression and quantile regression forests. The proposed ensemble learner post-processes simulations of the GR4J hydrological model for 511 basins in the contiguous US. We illustrate its significantly improved performance relative to the base-learners used and a less prominent improvement relative to the “hard to beat in practice” equal-weight combiner. © 2019 Elsevier B.V