Deep Learning for Improved Agricultural Risk Management

Deep learning provides many benefits, including automation, speed, accuracy, and intelligence, and it is delivering competitive performance now across a wide range of real-world operational applications - from credit card fraud detection to recommender systems and customer segmentation. Its potential in actuarial sciences and agricultural insurance/risk management, however, remains largely untapped. In this pilot study, we investigate deep learning in predicting agricultural yield in time and space under weather/climate uncertainty. We evaluate the predictive power of deep learning, benchmarking its performance against more conventional approaches alongside both weather station and climate. Our findings reveal that deep learning offers the highest predictive accuracy, outperforming all the other approaches. We infer that it also has great potential to reduce underwriting inefficiencies and insurance coverage costs associated with using more imprecise yield-based metrics of real risk exposure. Future work aims to further evaluate its performance, from municipal area-yield, to finer-scale crop-specific producer-scale yield

A Multi-Stage Machine Learning Approach to Predict Dengue Incidence: A Case Study in Mexico

© 2013 IEEE. The mosquito-borne dengue fever is a major public health problem in tropical countries, where it is strongly conditioned by climate factors such as temperature. In this paper, we formulate a holistic machine learning strategy to analyze the temporal dynamics of temperature and dengue data and use this knowledge to produce accurate predictions of dengue, based on temperature on an annual scale. The temporal dynamics are extracted from historical data by utilizing a novel multi-stage combination of auto-encoding, window-based data representation and trend-based temporal clustering. The prediction is performed with a trend association-based nearest neighbour predictor. The effectiveness of the proposed strategy is evaluated in a case study that comprises the number of dengue and dengue hemorrhagic fever cases collected over the period 1985-2010 in 32 federal states of Mexico. The empirical study proves the viability of the proposed strategy and confirms that it outperforms various state-of-the-art competitor methods formulated both in regression and in time series forecasting analysis

Integrating machine learning with otolith isoscapes: Reconstructing connectivity of a marine fish over four decades.

Stable isotopes are an important tool to uncover animal migration. Geographic natal assignments often require categorizing the spatial domain through a nominal approach, which can introduce bias given the continuous nature of these tracers. Stable isotopes predicted over a spatial gradient (i.e., isoscapes) allow a probabilistic and continuous assignment of origin across space, although applications to marine organisms remain limited. We present a new framework that integrates nominal and continuous assignment approaches by (1) developing a machine-learning multi-model ensemble classifier using Bayesian model averaging (nominal); and (2) integrating nominal predictions with continuous isoscapes to estimate the probability of origin across the spatial domain (continuous). We applied this integrated framework to predict the geographic origin of the Northwest Atlantic mackerel (Scomber scombrus), a migratory pelagic fish comprised of northern and southern components that have distinct spawning sites off Canada (northern contingent) and the US (southern contingent), and seasonally overlap in the US fished regions. The nominal approach based on otolith carbon and oxygen stable isotopes (δ13C/δ18O) yielded high contingent classification accuracy (84.9%). Contingent assignment of unknown-origin samples revealed prevalent, yet highly varied contingent mixing levels (12.5-83.7%) within the US waters over four decades (1975-2019). Nominal predictions were integrated into mackerel-specific otolith oxygen isoscapes developed independently for Canadian and US waters. The combined approach identified geographic nursery hotspots in known spawning sites, but also detected geographic shifts over multi-decadal time scales. This framework can be applied to other marine species to understand migration and connectivity at a high spatial resolution, relevant to management of unit stocks in fisheries and other conservation assessments

The recurring impact of storm disturbance on black sea bass (Centropristis striata) movement behaviors in the Mid-Atlantic Bight.

Storm events are a significant source of disturbance in the Middle Atlantic Bight, in the Northwest Atlantic, that cause rapid destratification of the water column during the late summer and early fall. Storm-driven mixing can be considered as a seasonal disturbance regime to demersal communities, characterized by the recurrence of large changes in bottom water temperatures. Black sea bass are a model ubiquitous demersal species in the Middle Atlantic Bight, as their predominantly sedentary behavior makes them ideal for tagging studies while also regularly exposing them to summer storm disturbances and the physiological stresses associated with thermal destratification. To better understand the responsiveness of black sea bass to storm impacts, we coupled biotelemetry with a high-resolution Finite Volume Community Ocean Model (FVCOM). During the summers of 2016-2018, 8-15 black sea bass were released each year with acoustic transponders at three reef sites, which were surrounded by data-logging receivers. Data were analyzed for activity levels and reef departures of black sea bass, and fluctuations in temperature, current velocity, and turbulent kinetic energy. Movement rates were depressed with each consecutive passing storm, and late-season storms were associated with permanent evacuations by a subset of tagged fish. Serial increases in bottom temperature associated with repeated storm events were identified as the primary depressor of local movement. Storm-driven increases in turbulent kinetic energy and current velocity had comparatively smaller, albeit significant, effects. Black sea bass represents both an important fishery resource and an indicator species for the impact of offshore wind development in the United States. Their availability to fisheries surveys and sensitivity to wind turbine impacts will be biased during periods of high storm activity, which is likely to increase with regional climate change

Data from: Year-round spatiotemporal distribution of harbour porpoises within and around the Maryland wind energy area

Offshore windfarms provide renewable energy, but activities during the construction phase can affect marine mammals. To understand how the construction of an offshore windfarm in the Maryland Wind Energy Area (WEA) off Maryland, USA, might impact harbour porpoises (Phocoena phocoena), it is essential to determine their poorly understood year-round distribution. Although habitat-based models can help predict the occurrence of species in areas with limited or no sampling, they require validation to determine the accuracy of the predictions. Incorporating more than 18 months of harbour porpoise detection data from passive acoustic monitoring, generalized auto-regressive moving average and generalized additive models were used to investigate harbour porpoise occurrence within and around the Maryland WEA in relation to temporal and environmental variables. Acoustic detection metrics were compared to habitat-based density estimates derived from aerial and boat-based sightings to validate the model predictions. Harbour porpoises occurred significantly more frequently during January to May, and foraged significantly more often in the evenings to early mornings at sites within and outside the Maryland WEA. Harbour porpoise occurrence peaked at sea surface temperatures of 5°C and chlorophyll a concentrations of 4.5 to 7.4 mg m-3. The acoustic detections were significantly correlated with the predicted densities, except at the most inshore site. This study provides insight into previously unknown fine-scale spatial and temporal patterns in distribution of harbour porpoises offshore of Maryland. The results can be used to help inform future monitoring and mitigate the impacts of windfarm construction and other human activities

Year-round spatiotemporal distribution of harbour porpoises within and around the Maryland wind energy area

<div><p>Offshore windfarms provide renewable energy, but activities during the construction phase can affect marine mammals. To understand how the construction of an offshore windfarm in the Maryland Wind Energy Area (WEA) off Maryland, USA, might impact harbour porpoises <i>(Phocoena phocoena</i>), it is essential to determine their poorly understood year-round distribution. Although habitat-based models can help predict the occurrence of species in areas with limited or no sampling, they require validation to determine the accuracy of the predictions. Incorporating more than 18 months of harbour porpoise detection data from passive acoustic monitoring, generalized auto-regressive moving average and generalized additive models were used to investigate harbour porpoise occurrence within and around the Maryland WEA in relation to temporal and environmental variables. Acoustic detection metrics were compared to habitat-based density estimates derived from aerial and boat-based sightings to validate the model predictions. Harbour porpoises occurred significantly more frequently during January to May, and foraged significantly more often in the evenings to early mornings at sites within and outside the Maryland WEA. Harbour porpoise occurrence peaked at sea surface temperatures of 5°C and chlorophyll <i>a</i> concentrations of 4.5 to 7.4 mg m^-3. The acoustic detections were significantly correlated with the predicted densities, except at the most inshore site. This study provides insight into previously unknown fine-scale spatial and temporal patterns in distribution of harbour porpoises offshore of Maryland. The results can be used to help inform future monitoring and mitigate the impacts of windfarm construction and other human activities.</p></div

The mean (± SE) number of minutes per day during which harbour porpoises were acoustically detected at each site offshore of Maryland.

<p>There were no data in March at site 4 due to instrument loss.</p