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      An AHP based approach to forecast groundwater level at potential recharge zones of Uckermark District, Brandenburg, Germany

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          Abstract

          Uckermark, a district of the state Brandenburg, Germany is situated in one of the driest regions of Germany. The district is known for its agricultural activities and natural resources. But in recent times the district is being prone to groundwater deficit due to the dryness of its climate. In this research initiative, a GIS and Remote Sensing based approach has been made to detect the potential groundwater recharge zones of Uckermark district and observe the groundwater level condition over a period of 21 years (2000–2020). Analytic Hierarchy Process has been used to locate the potential groundwater recharge zones and later a Long Short-Term Memory (LSTM) based model has been developed to forecast the seasonal groundwater level for the upcoming five years in the potential groundwater recharge zones based on observation data from groundwater measurement points. This enabled us to see the groundwater condition of Uckermark in near future and point out the necessary steps to be taken.

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          Long Short-Term Memory

          Jürgen Schmidhuber, Jürgen Schmidhuber (2002)
          Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
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            River flow forecasting through conceptual models part I — A discussion of principles

            J.E. Nash, J.V. Sutcliffe (1970)
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              Groundwater depletion embedded in international food trade

              Carole Dalin, Yoshihide Wada, Thomas Kastner … (2017)
              Recent hydrological modelling and Earth observations have located and quantified alarming rates of groundwater depletion worldwide. This depletion is primarily due to water withdrawals for irrigation, but its connection with the main driver of irrigation, global food consumption, has not yet been explored. Here we show that approximately eleven per cent of non-renewable groundwater use for irrigation is embedded in international food trade, of which two-thirds are exported by Pakistan, the USA and India alone. Our quantification of groundwater depletion embedded in the world’s food trade is based on a combination of global, crop-specific estimates of non-renewable groundwater abstraction and international food trade data. A vast majority of the world’s population lives in countries sourcing nearly all their staple crop imports from partners who deplete groundwater to produce these crops, highlighting risks for global food and water security. Some countries, such as the USA, Mexico, Iran and China, are particularly exposed to these risks because they both produce and import food irrigated from rapidly depleting aquifers. Our results could help to improve the sustainability of global food production and groundwater resource management by identifying priority regions and agricultural products at risk as well as the end consumers of these products.
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                Author and article information

                Contributors
                Ahmed Tahmid Raihan: a.tahmid100@gmail.com
                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 16 April 2022
                Publication date PMC-release: 16 April 2022
                Publication date Collection: 2022
                Volume: 12
                Electronic Location Identifier: 6365
                Affiliations
                [1 ]GRID grid.434950.f, ISNI 0000 0001 2270 6264, Hochschule für Technik Stuttgart, ; Stuttgart, Germany
                [2 ]BASF Digital Farming GmbH, Köln, Germany
                Article
                Publisher ID: 10403
                DOI: 10.1038/s41598-022-10403-9
                PMC ID: 9013400
                PubMed ID: 35430610
                SO-VID: c333b16b-1c7e-407f-ac47-f72edb421e79
                Copyright © © The Author(s) 2022
                License:

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 27 October 2021
                Date accepted : 5 April 2022
                Funding
                Funded by: Hochschule für Technik Stuttgart (3377)
                Open Access :

                Open Access funding enabled and organized by Projekt DEAL.

                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2022

                ScienceOpen disciplines: Uncategorized
                Keywords: environmental impact,hydrology
                Data availability:
                ScienceOpen disciplines: Uncategorized
                Keywords: environmental impact, hydrology

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