Water Goes Where? A Water Resource Allocation Method Based on Multi-Objective Decision-Making

• URL: http://arxiv.org/abs/2208.04138v1
• Date: Wed, 3 Aug 2022 09:37:37 GMT
• Title: Water Goes Where? A Water Resource Allocation Method Based on Multi-Objective Decision-Making
• Abstract summary: We study the problem of allocation scheme for Glen Canyon Dam and Hoover Dam in the Colorado River Basin. Taking into consideration of various factors, we build models to achieve optimal scheduling.
• Score: 7.558392810342939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For a long time, water and hydroelectric power are relatively important resources. Their rational distribution is closely related to regional agriculture, industry, residents, etc. In this paper, we mainly study the problem of allocation scheme for Glen Canyon Dam and Hoover Dam in the Colorado River Basin. Taking into consideration of various factors, we build models to achieve optimal scheduling. Firstly, we propose the Water Strategy Decision Model, which can obtain different distribution methods for the different water levels. Also, we connect the two dams in series to consider the coupling effect between them and integrate this part into the main model. Secondly, we propose three criteria of allocation for water and power generation, namely, economic, social, and environmental benefits. Social benefits mainly include the minimum shortage of water and electricity for agriculture, industry, and residents. For this multi-objective plan, the model is solved using a multi-objective ant colony genetic algorithm under the constraint of constant total water volume, and finally, the current reservoir capacities of the two lakes are input to derive the annual water supply to the five states. Thirdly, based on the location and development characteristics of the five states, we obtain a water scheduling model based on the priority of geographic-industry characteristics. Fourthly, we can regard the model as a four-dimensional space of industrial, agricultural, residential and power generation water demand. The partial derivative calculation formula of multi-dimensional space can be used to obtain the results. Finally, we analyze the sensitivity of the model, and it shows that the model has strong adaptability and is easier to popularize. Moreover, we discuss the advantages and disadvantages of the models.

Related papers

• Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings [48.54230513143053]
  This study examines whether AlphaEarth Foundation embeddings offer a more informative way to describe basin characteristics.<n>We find that models using them achieve higher accuracy when predicting flows in basins not used for training.
  arXiv  Detail & Related papers  (2026-01-04T15:14:16Z)
• Informed Learning for Estimating Drought Stress at Fine-Scale Resolution Enables Accurate Yield Prediction [10.780371055923304]
  Water is essential for agricultural productivity. Assessing water shortages and reduced yield potential is a critical factor in decision-making.<n>Crop simulation models, which align with physical processes, offer intrinsic explainability but often perform poorly.<n>Machine learning models for crop yield modeling are powerful and scalable, yet they commonly operate as black boxes and lack adherence to the physical principles of crop growth.
  arXiv  Detail & Related papers  (2025-10-21T13:58:04Z)
• How Effective are Large Time Series Models in Hydrology? A Study on Water Level Forecasting in Everglades [1.147912082971675]
  The Everglades play a crucial role in flood and drought regulation, water resource planning, and ecosystem management.<n>Traditional physics-based and statistical methods for predicting water levels often face significant challenges.<n>Recent advancements in large time series models have demonstrated the potential to address these limitations.
  arXiv  Detail & Related papers  (2025-05-02T17:48:20Z)
• DiTEC-WDN: A Large-Scale Dataset of Hydraulic Scenarios across Multiple Water Distribution Networks [41.94295877935867]
  This dataset comprises 36,000 unique scenarios simulated over either short-term (24 hours) or long-term (1 year) periods.<n>DiTEC-WDN can support a variety of machine-learning tasks, including graph-level, node-level, and link-level regression, as well as time-series forecasting.<n>This contribution, released under a public license, encourages open scientific research in the critical water sector, eliminates the risk of exposing sensitive data, and fulfills the need for a large-scale water distribution network benchmark for study comparisons and scenario analysis.
  arXiv  Detail & Related papers  (2025-03-21T14:14:03Z)
• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• Prioritizing Potential Wetland Areas via Region-to-Region Knowledge Transfer and Adaptive Propagation [9.99241578238986]
  Wetlands are important to communities, offering benefits ranging from water purification to flood protection to recreation and tourism. Data-driven solutions are feasible, but are complicated by significant data sparsity due to the low proportion of wetlands in many areas of interest in the southwestern US. This makes it hard to develop data-driven models that can help guide the identification of additional wetland areas.
  arXiv  Detail & Related papers  (2024-06-08T21:25:24Z)
• Social Media and Artificial Intelligence for Sustainable Cities and Societies: A Water Quality Analysis Use-case [2.263152338732333]
  We propose a Natural Language Processing (NLP) framework to automatically collect and analyze water-related posts from social media. For text classification, we propose a merit-fusion-based framework incorporating several Large Language Models (LLMs) In topic modeling, we employed the BERTopic library to discover the hidden topic patterns in the water-related tweets.
  arXiv  Detail & Related papers  (2024-04-23T12:33:14Z)
• Water-Based Metaheuristics: How Water Dynamics Can Help Us to Solve NP-Hard Problems [49.7719149179179]
  Water-based optimization metaheuristics have been introduced during the last decade. Most of them emulate, in some way or another, how drops collaboratively form paths down to the sea. In general the resulting algorithms are quite different in terms of their searching approach or their solution construction approach.
  arXiv  Detail & Related papers  (2024-01-16T11:39:42Z)
• TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting [4.915744683251151]
  Hydrometric prediction of water quantity is useful for a variety of applications, including water management, flood forecasting, and flood control. We propose atemporal forecasting model that augments the hidden state in Graph Convolution Recurrent Neural Network (GCRN) encoder-decoder. We present a new benchmark dataset of water flow from a network of Canadian stations on rivers, streams, and lakes.
  arXiv  Detail & Related papers  (2023-12-10T18:23:40Z)
• FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems [56.0640340392818]
  We introduce a framework, FREE, that enables the use of varying features and available information to train a universal model.<n>The core idea is to map available environmental data into a text space and then convert the traditional predictive modeling task in environmental science to a semantic recognition problem.<n>Our evaluation on two societally important real-world applications, stream water temperature prediction and crop yield prediction, demonstrates the superiority of FREE over multiple baselines.
  arXiv  Detail & Related papers  (2023-11-17T00:53:09Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Management [0.0]
  This project aims to forecast soil moisture using domain knowledge and machine learning for crop management decisions. Inspired by this domain knowledge, we have constructed a novel domain-inspired temporal graph convolution neural network. We have trained, validated, and tested our method on field-scale time series data consisting of approximately 99,000 hydrological response units spanning 40 years.
  arXiv  Detail & Related papers  (2022-12-12T14:36:39Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• Dam reservoir extraction from remote sensing imagery using tailored metric learning strategies [6.040904021861968]
  We propose a novel deep neural network based pipeline that decomposes dam reservoir extraction into water body segmentation and dam reservoir recognition. We establish a benchmark dataset with earth imagery data and human labelled reservoirs from river basins in West Africa and India.
  arXiv  Detail & Related papers  (2022-07-12T19:46:01Z)
• Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
  We find a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
  arXiv  Detail & Related papers  (2020-10-20T15:31:38Z)
• Towards a Peer-to-Peer Energy Market: an Overview [68.8204255655161]
  This work focuses on the electric power market, comparing the status quo with the recent trend towards the increase in distributed self-generation capabilities by prosumers. We introduce a potential multi-layered architecture for a Peer-to-Peer (P2P) energy market, discussing the fundamental aspects of local production and local consumption as part of a microgrid. To give a full picture to the reader, we also scrutinise relevant elements of energy trading, such as Smart Contract and grid stability.
  arXiv  Detail & Related papers  (2020-03-02T20:32:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.