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  1. Article ; Online: Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.

    Greenhill, Simon / Druckenmiller, Hannah / Wang, Sherrie / Keiser, David A / Girotto, Manuela / Moore, Jason K / Yamaguchi, Nobuhiro / Todeschini, Alberto / Shapiro, Joseph S

    Science (New York, N.Y.)

    2024  Volume 383, Issue 6681, Page(s) 406–412

    Abstract: We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the ... ...

    Abstract We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the Army Corps of Engineers, each deciding regulation for one water resource. Under a 2006 Supreme Court ruling, the Clean Water Act protects two-thirds of US streams and more than half of wetlands; under a 2020 White House rule, it protects less than half of streams and a fourth of wetlands, implying deregulation of 690,000 stream miles, 35 million wetland acres, and 30% of waters around drinking-water sources. Our framework can support permitting, policy design, and use of machine learning in regulatory implementation problems.
    MeSH term(s) Drinking Water/legislation & jurisprudence ; Machine Learning ; Rivers ; Water Pollution/legislation & jurisprudence ; Water Pollution/prevention & control ; Water Quality ; Wetlands ; Conservation of Natural Resources
    Chemical Substances Drinking Water
    Language English
    Publishing date 2024-01-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.adi3794
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks.

    Park, Minok / Grbčić, Luka / Motameni, Parham / Song, Spencer / Singh, Alok / Malagrino, Dante / Elzouka, Mahmoud / Vahabi, Puya H / Todeschini, Alberto / de Jong, Wibe Albert / Prasher, Ravi / Zorba, Vassilia / Lubner, Sean D

    Advanced science (Weinheim, Baden-Wurttemberg, Germany)

    2024  , Page(s) e2401951

    Abstract: This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate ... ...

    Abstract This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one-to-many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root-mean-squared-error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser-matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.
    Language English
    Publishing date 2024-04-29
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2808093-2
    ISSN 2198-3844 ; 2198-3844
    ISSN (online) 2198-3844
    ISSN 2198-3844
    DOI 10.1002/advs.202401951
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Deep Learning-Based Acoustic Mosquito Detection in Noisy Conditions Using Trainable Kernels and Augmentations

    Khandelwal, Devesh / Campos, Sean / Nagaraj, Shwetha / Nugen, Fred / Todeschini, Alberto

    2022  

    Abstract: In this paper, we demonstrate a unique recipe to enhance the effectiveness of audio machine learning approaches by fusing pre-processing techniques into a deep learning model. Our solution accelerates training and inference performance by optimizing ... ...

    Abstract In this paper, we demonstrate a unique recipe to enhance the effectiveness of audio machine learning approaches by fusing pre-processing techniques into a deep learning model. Our solution accelerates training and inference performance by optimizing hyper-parameters through training instead of costly random searches to build a reliable mosquito detector from audio signals. The experiments and the results presented here are part of the MOS C submission of the ACM 2022 challenge. Our results outperform the published baseline by 212% on the unpublished test set. We believe that this is one of the best real-world examples of building a robust bio-acoustic system that provides reliable mosquito detection in noisy conditions.
    Keywords Computer Science - Sound ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Audio and Speech Processing ; I.2.m ; I.2.1
    Publishing date 2022-07-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article: A new dataset of global irrigation areas from 2001 to 2015

    Nagaraj, Deepak / Proust, Eleanor / Todeschini, Alberto / Rulli, Maria Cristina / D'Odorico, Paolo

    Advances in water resources. 2021 June, v. 152

    2021  

    Abstract: About 40% of global crop production takes place on irrigated land, which accounts for approximately 20% of the global farmland. The great majority of freshwater consumption by human societies is associated with irrigation, which contributes to a major ... ...

    Abstract About 40% of global crop production takes place on irrigated land, which accounts for approximately 20% of the global farmland. The great majority of freshwater consumption by human societies is associated with irrigation, which contributes to a major modification of the global water cycle by enhancing evapotranspiration and reducing surface and groundwater runoff. In many regions of the world irrigation contributes to streamflow and groundwater depletion, soil salinization, cooler microclimate conditions, and altered land-atmosphere interactions. Despite the important role played by irrigation in food security, water cycle, soil productivity, and near-surface atmospheric conditions, its global extent remains poorly quantified. To date global maps of irrigated land are often based on estimates from circa year 2000. Here we apply artificial intelligence methods based on machine learning algorithms to satellite remote sensing and monthly climate data to map the spatial extent of irrigated areas between 2001 and 2015. We provide global annual maps of irrigated land at ≈9km resolution for the 2001-2015 and we make this dataset available online.
    Keywords agricultural land ; artificial intelligence ; crop production ; data collection ; evapotranspiration ; food security ; freshwater ; groundwater ; humans ; hydrologic cycle ; irrigated farming ; irrigation ; meteorological data ; microclimate ; runoff ; satellites ; soil productivity ; soil salinization ; stream flow ; water shortages
    Language English
    Dates of publication 2021-06
    Publishing place Elsevier Ltd
    Document type Article
    Note NAL-AP-2-clean
    ZDB-ID 2023320-6
    ISSN 1872-9657 ; 0309-1708
    ISSN (online) 1872-9657
    ISSN 0309-1708
    DOI 10.1016/j.advwatres.2021.103910
    Database NAL-Catalogue (AGRICOLA)

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  5. Book ; Online: Upscaling Global Hourly GPP with Temporal Fusion Transformer (TFT)

    Nakagawa, Rumi / Chau, Mary / Calzaretta, John / Keenan, Trevor / Vahabi, Puya / Todeschini, Alberto / Bassiouni, Maoya / Kang, Yanghui

    2023  

    Abstract: Reliable estimates of Gross Primary Productivity (GPP), crucial for evaluating climate change initiatives, are currently only available from sparsely distributed eddy covariance tower sites. This limitation hampers access to reliable GPP quantification ... ...

    Abstract Reliable estimates of Gross Primary Productivity (GPP), crucial for evaluating climate change initiatives, are currently only available from sparsely distributed eddy covariance tower sites. This limitation hampers access to reliable GPP quantification at regional to global scales. Prior machine learning studies on upscaling \textit{in situ} GPP to global wall-to-wall maps at sub-daily time steps faced limitations such as lack of input features at higher temporal resolutions and significant missing values. This research explored a novel upscaling solution using Temporal Fusion Transformer (TFT) without relying on past GPP time series. Model development was supplemented by Random Forest Regressor (RFR) and XGBoost, followed by the hybrid model of TFT and tree algorithms. The best preforming model yielded to model performance of 0.704 NSE and 3.54 RMSE. Another contribution of the study was the breakdown analysis of encoder feature importance based on time and flux tower sites. Such analysis enhanced the interpretability of the multi-head attention layer as well as the visual understanding of temporal dynamics of influential features.

    Comment: Accepted Oral Presentation at CVPR 2023 MultiEarth Workshop
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-06-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: High-resolution global irrigation prediction with Sentinel-2 30m data

    Weixin / Wu / Thakkar, Sonal / Hawkins, Will / Vahabi, Hossein / Todeschini, Alberto

    2020  

    Abstract: An accurate and precise understanding of global irrigation usage is crucial for a variety of climate science efforts. Irrigation is highly energy-intensive, and as population growth continues at its current pace, increases in crop need and water usage ... ...

    Abstract An accurate and precise understanding of global irrigation usage is crucial for a variety of climate science efforts. Irrigation is highly energy-intensive, and as population growth continues at its current pace, increases in crop need and water usage will have an impact on climate change. Precise irrigation data can help with monitoring water usage and optimizing agricultural yield, particularly in developing countries. Irrigation data, in tandem with precipitation data, can be used to predict water budgets as well as climate and weather modeling. With our research, we produce an irrigation prediction model that combines unsupervised clustering of Normalized Difference Vegetation Index (NDVI) temporal signatures with a precipitation heuristic to label the months that irrigation peaks for each cropland cluster in a given year. We have developed a novel irrigation model and Python package ("Irrigation30") to generate 30m resolution irrigation predictions of cropland worldwide. With a small crowdsourced test set of cropland coordinates and irrigation labels, using a fraction of the resources used by the state-of-the-art NASA-funded GFSAD30 project with irrigation data limited to India and Australia, our model was able to achieve consistency scores in excess of 97\% and an accuracy of 92\% in a small geo-diverse randomly sampled test set.

    Comment: 6 pages, 1 figure
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 571
    Publishing date 2020-12-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Self-supervised Contrastive Learning for Irrigation Detection in Satellite Imagery

    Agastya, Chitra / Ghebremusse, Sirak / Anderson, Ian / Reed, Colorado / Vahabi, Hossein / Todeschini, Alberto

    2021  

    Abstract: Climate change has caused reductions in river runoffs and aquifer recharge resulting in an increasingly unsustainable crop water demand from reduced freshwater availability. Achieving food security while deploying water in a sustainable manner will ... ...

    Abstract Climate change has caused reductions in river runoffs and aquifer recharge resulting in an increasingly unsustainable crop water demand from reduced freshwater availability. Achieving food security while deploying water in a sustainable manner will continue to be a major challenge necessitating careful monitoring and tracking of agricultural water usage. Historically, monitoring water usage has been a slow and expensive manual process with many imperfections and abuses. Ma-chine learning and remote sensing developments have increased the ability to automatically monitor irrigation patterns, but existing techniques often require curated and labelled irrigation data, which are expensive and time consuming to obtain and may not exist for impactful areas such as developing countries. In this paper, we explore an end-to-end real world application of irrigation detection with uncurated and unlabeled satellite imagery. We apply state-of-the-art self-supervised deep learning techniques to optical remote sensing data, and find that we are able to detect irrigation with up to nine times better precision, 90% better recall and 40% more generalization ability than the traditional supervised learning methods.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2021-08-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Snowpack Estimation in Key Mountainous Water Basins from Openly-Available, Multimodal Data Sources

    Moran, Malachy / Woputz, Kayla / Hee, Derrick / Girotto, Manuela / D'Odorico, Paolo / Gupta, Ritwik / Feldman, Daniel / Vahabi, Puya / Todeschini, Alberto / Reed, Colorado J

    2022  

    Abstract: Accurately estimating the snowpack in key mountainous basins is critical for water resource managers to make decisions that impact local and global economies, wildlife, and public policy. Currently, this estimation requires multiple LiDAR-equipped plane ... ...

    Abstract Accurately estimating the snowpack in key mountainous basins is critical for water resource managers to make decisions that impact local and global economies, wildlife, and public policy. Currently, this estimation requires multiple LiDAR-equipped plane flights or in situ measurements, both of which are expensive, sparse, and biased towards accessible regions. In this paper, we demonstrate that fusing spatial and temporal information from multiple, openly-available satellite and weather data sources enables estimation of snowpack in key mountainous regions. Our multisource model outperforms single-source estimation by 5.0 inches RMSE, as well as outperforms sparse in situ measurements by 1.2 inches RMSE.

    Comment: Accepted Oral Presentation at CVPR 2022 MultiEarth
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Publishing date 2022-08-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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