The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco

IF 2.6 Q2 WATER RESOURCES
Tarik Bouramtane, Marc Leblanc, Ilias Kacimi, Hamza Ouatiki, Abdelghani Boudhar
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引用次数: 1

Abstract

The planning and management of groundwater in the absence of in situ climate data is a delicate task, particularly in arid regions where this resource is crucial for drinking water supplies and irrigation. Here the motivation is to evaluate the role of remote sensing data and Input feature selection method in the Long Short Term Memory (LSTM) neural network for predicting groundwater levels of five wells located in different hydrogeological contexts across the Oum Er-Rbia Basin (OER) in Morocco: irrigated plain, floodplain and low plateau area. As input descriptive variable, four remote sensing variables were used: the Integrated Multi-satellite Retrievals (IMERGE) Global Precipitation Measurement (GPM) precipitation, Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), MODIS land surface temperature (LST), and MODIS evapotranspiration. Three LSTM models were developed, rigorously analyzed and compared. The LSTM-XGB-GS model, was optimized using the GridsearchCV method, and uses a single remote sensing variable identified by the input feature selection method XGBoost. Another optimized LSTM model was also constructed, but uses the four remote sensing variables as input (LSTM-GS). Additionally, a standalone LSTM model was established and also incorporating the four variables as inputs. Scatter plots, violin plots, Taylor diagram and three evaluation indices were used to verify the performance of the three models. The overall result showed that the LSTM-XGB-GS model was the most successful, consistently outperforming both the LSTM-GS model and the standalone LSTM model. Its remarkable accuracy is reflected in high R 2 values (0.95 to 0.99 during training, 0.72 to 0.99 during testing) and the lowest RMSE values (0.03 to 0.68 m during training, 0.02 to 0.58 m during testing) and MAE values (0.02 to 0.66 m during training, 0.02 to 0.58 m during testing). The LSTM-XGB-GS model reveals how hydrodynamics, climate, and land-use influence groundwater predictions, emphasizing correlations like irrigated land-temperature link and floodplain-NDVI-evapotranspiration interaction for improved predictions. Finally, this study demonstrates the great support that remote sensing data can provide for groundwater prediction using ANN models in conditions where in situ data are lacking.
遥感和输入特征选择对摩洛哥Oum Er-Rbia盆地LSTM神经网络地下水位预测的贡献
在缺乏现场气候数据的情况下规划和管理地下水是一项微妙的任务,特别是在干旱地区,地下水资源对饮用水供应和灌溉至关重要。本文的目的是评估遥感数据和输入特征选择方法在长短期记忆(LSTM)神经网络中的作用,以预测位于摩洛哥Oum Er-Rbia盆地(OER)不同水文地质背景下的5口井的地下水位:灌溉平原,洪泛区和低高原地区。本文采用综合多卫星检索(IMERGE)全球降水测量(GPM)降水、中分辨率成像光谱辐射计(MODIS)归一化植被指数(NDVI)、MODIS地表温度(LST)和MODIS蒸散发4个遥感变量作为输入描述变量。建立了三种LSTM模型,并进行了严格的分析和比较。LSTM-XGB-GS模型使用GridsearchCV方法进行优化,并使用输入特征选择方法XGBoost识别的单个遥感变量。构建了另一个优化的LSTM模型,该模型采用4个遥感变量作为输入(LSTM- gs)。此外,建立了一个独立的LSTM模型,也将这四个变量作为输入。利用散点图、小提琴图、泰勒图和三个评价指标验证了三种模型的性能。总体结果表明,LSTM- xgb - gs模型是最成功的,始终优于LSTM- gs模型和独立LSTM模型。其显著的准确性体现在较高的r2值(训练期间为0.95 ~ 0.99,测试期间为0.72 ~ 0.99),最低的RMSE值(训练期间为0.03 ~ 0.68 m,测试期间为0.02 ~ 0.58 m)和MAE值(训练期间为0.02 ~ 0.66 m,测试期间为0.02 ~ 0.58 m)。LSTM-XGB-GS模型揭示了水动力学、气候和土地利用如何影响地下水预测,强调了灌溉土地温度联系和漫滩- ndi -蒸散发相互作用等相关性,以改进预测。最后,本研究表明,在缺乏原位数据的情况下,遥感数据可以为利用人工神经网络模型进行地下水预测提供巨大的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Water
Frontiers in Water WATER RESOURCES-
CiteScore
4.00
自引率
6.90%
发文量
224
审稿时长
13 weeks
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