In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1745-4557.2005.00029.x

Modeling microbial responses in food

Abstract Assessments of the implications of soil erosion require quantification of soil erosion rates (SE) and sediment yield (SSY) at regional scales under present and future climate and land use scenarios. A range of models is available to predict SE and SSY, but a critical evaluation of these models is lacking. Here, we evaluate 14 models based on 32 published studies and over 700 selected catchments. Evaluation criteria include: (1) prediction accuracy, (2) knowledge gain on dominant soil erosion processes, (3) data and calibration requirements, and (4) applicability in global change scenario studies. Results indicate that modelling of SE and SSY strongly depends on the spatial and temporal scales considered. In large catchments (> 10,000 km2), most accurate predictions of suspended sediment yield are obtained by nonlinear regression models like BQART, WBMsed, or Pelletier's model. For medium-sized catchments, best results are obtained by factorial scoring models like PSIAC, FSM and SSY Index, which also support identification of dominant erosion processes. Most other models (e.g., WATEM–SEDEM, AGNPS, LISEM, PESERA, and SWAT) represent only a selection of erosion and sediment transport processes. Consequently, these models only provide reliable results where the considered processes are indeed dominant. Identification of sediment sources and sinks requires spatially distributed models, which, on average, have lower model accuracy and require more input data and calibration efforts than spatially lumped models. Of these models, most accurate predictions with least data requirements were provided by SPADS and WATEM–SEDEM. Priorities for model development include: (1) simulation of point sources of sediment, (2) balancing model complexity and the quality of input data, (3) simulation of the impact of soil and water conservation measures, and (4) incorporation of dynamic land use and climate scenarios. Prediction of the impact of global change on SE and SSY in medium sized catchments is one of the main challenges in future model development. No single model fulfils all modelling objectives; a further integration of field observations and different model concepts is needed to obtain better contemporary and future predictions of SE and SSY.

Predicting soil erosion and sediment yield at regional scales: Where do we stand? (vol 127, pg 16, 2013)

Water Management causes increment of reservoir silting and reduction of water yield in the semiarid State of Ceará, Brazil

Abstract Reservoirs have been installed as long-term assets to guarantee water and energy security for decades, if not centuries. However, the effect of siltation undermines reservoirs' sustainability because it significantly reduces the reservoirs' original capacity. Extreme events such as typhoons, floods and droughts are posited to have extreme impacts on sediment inflow and deposition in reservoirs. The same holds true for ISMTs (implemented sediment management technologies), such as dredging, spilling and bypassing. However, the large-scale analysis of their effects on reservoir sedimentation progression, recovery and development was not feasible due to data scarcity and technological restrictions. The present paper closes this information gap by conducting a GRU (gated recurrent unit) neural network analysis of 1,224 Japanese reservoirs, for which the sedimentation, local precipitation, extreme events and ISMTs were monitored between 2000 and 2017. The network reveals the beneficial impacts of dredging, spilling and bypassing. The results also demonstrate the potential of smart management and improved monitoring for sedimentation threat abatement. Thus, foresighted engineering and dedicated governance action in flood and drought scenarios can significantly strengthen the sustainable behavior of key infrastructure elements such as reservoirs. 

Demonstration of the impacts of anti-sedimentation techniques on Japanese reservoir siltation via mass data ANN analysis

Reservoirs are installed as long-term assets to guarantee water and energy security for decades, if not centuries. However, the effect of siltation undermines reservoirs’ sustainability because it ...

Mitsuteru Irie

Papers

The effect of optimism bias and governmental action on siltation management within Japanese reservoirs surveyed via artificial neural network