Newsletter on Watershed Research: September- 2018

First Rain On World’s Largest Artificial Watershed

Rain in Southern Arizona is scarce and precious to start with, however, the afternoon shower that soaked the soil twenty-five miles north of Tucson on Gregorian calendar month. twenty-nine was uncommon in many ways in which.

Spouting from a network of pipes, thousands of gallons of water drizzled down onto the world’s solely and largest manmade experimental watershed, recently completed at the University of Arizona’s part two. [1]

Time Series Analysis and Statistical Model Development for Food and Water Availability in the Grand River Watershed

Imminent threats of climate change, population growth, and associated anthropogenic impacts can have severe implications for food and water resources around the globe. In order to properly manage these resources in the future, it is necessary to understand how they are influenced by stressors. This thesis characterizes recent, historical trends for food and water availability within the Southern Ontario Grand River Watershed (GRW). It then discusses the implications of trends for local food and water security and develops a statistical framework to model crop production in the GRW. [2]

Watershed management using neuroevolution

Neuroevolution refers to evolving neural networks victimization organic process strategies. These algorithms are applied to several drawback domains, from the game taking part into AI, that motivates this analysis. the matter of watershed management is self-addressed here during this analysis victimization the foremost distinguished neuroevolution algorithms, i.e. NeuroEvolution of Augmenting Topologies (NEAT), neuro differential evolution and implemented SubPopulations. [3]

Algebraic Multi-Grid based Multi-Focus Image Fusion Using Watershed Algorithm

This paper proposes a brand new multi-focus image fusion technique named AMGW, and it’s supported pure mathematics multi-grid (AMG) rule and watershed segmentation technique. within the implementation, the coarse grids of the supply pictures area unit initial extracted with the affinity matrix, and with an abstraction interpolation operate the approximation of the supplied image will be reconstructed from the coarse grids. a substantial quantity of edge and textural info remains preserved in such an approximation. the 2 supply pictures area unit compared with their corresponding approximation block by block severally by using the mean sq. error (MSE) as a sharpness criterion. [4]

Water Quality Assessment of the Los Angeles River Watershed, California, the USA in Wet and Dry Weather Periods

River runoff in semi-arid urban watersheds might consist entirely of treated effluent (dry-weather) and/or urban nonpoint supply runoff (wet-weather), which may be a supply of nutrients, bacteria, and metals to receiving waters. the aim of this study is to spot sources of potential pollutants and to characterize urban water quality on the la (LA) watercourse from its head to the mouth throughout dry and wet weather seasons. The LA watercourse is AN effluent-dominated water body throughout the time of year. The 3 effluent treatment plants (WWTP), as well as the Tillman, Burbank, and Glendale waste water treatment plants, discharge the bulk of the amount flowing within the LA watercourse throughout the dry and wet amount. The WWTP discharge chemicals admire chloride, nitrate, and sulfate to the watercourse. The metals are additional doubtless attributed to street runoff. In each case, the contamination is spread through numerous water channels that carry semi-treated effluent from numerous sources ending up into the ocean. [5]

Reference

[1] First Rain On World’s Largest Artificial Watershed

December 4, 2012 (web link)

[2] Time Series Analysis and Statistical Model Development for Food and Water Availability in the Grand River Watershed

McNeill K. Time Series Analysis and Statistical Model Development for Food and Water Availability in the Grand River Watershed (Doctoral dissertation). (web link)

[3] Watershed management using neuroevolution

Mason K, Duggan J, Howley E. Watershed management using neuroevolution. Modeling Earth Systems and Environment. 2018:1-4. (web link)

[4] Algebraic Multi-Grid based Multi-Focus Image Fusion Using Watershed Algorithm

Huang Y, Li W, Gao M, Liu Z. Algebraic Multi-Grid based Multi-Focus Image Fusion Using Watershed Algorithm. IEEE Access. 2018 Aug 23. (web link)

[5] Water Quality Assessment of the Los Angeles River Watershed, California, the USA in Wet and Dry Weather Periods

Mohammad Hassan Rezaie Boroon1* and Carl Brian Von L. Coo1

1Geosciences and Environment Department, California State University, Los Angeles, USA. (web link)

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