Priyanka Priyanka, Praveen Kumar, Sucheta Panda, Tejinder Thakur, K. V. Uday, Varun Dutt
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To tackle the high number of features and potential uncorrelated features, a novel guided backpropagation-based feature selection technique was developed to rank the most relevant features. The top-10 features, highly correlated with evaporation rate, were selected for ML model input, alongside the top-5 and all features. Several ML models, including multiple regression (MR), K-nearest neighbor (KNN), multilayer perceptron (MLP), sequential minimal optimization regression (SMOreg), random forest (RF), and a novel K-Nearest Oracles (KNORA) ensemble, were constructed for the purpose of forecasting the evaporation rate. The average error of these models was assessed using the root mean squared error (RMSE). Experimental results showed that the KNORA ensemble model performed the best, achieving a 7.54 mg/h RMSE in testing with the top-10 features. MLP was followed closely by a 25.1 mg/h RMSE in the same testing. 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引用次数: 0
摘要
极端天气事件和全球气候变化加剧了蒸发率问题。因此,准确预测影响土壤开裂的土壤水分蒸发率变得至关重要。然而,人们对新型特征工程技术和机器学习预测如何估算土壤水分蒸发率知之甚少。本研究的重点是利用机器学习(ML)模型预测土壤蒸发率。数据集由 21 个地面参数组成,包括温度、湿度和土壤相关特征,用作预测蒸发潜力的特征。针对大量特征和潜在的不相关特征,开发了一种新颖的基于反向传播引导的特征选择技术,对最相关的特征进行排序。除前 5 个特征和所有特征外,还选择了与蒸发率高度相关的前 10 个特征作为 ML 模型的输入。为了预测蒸发率,构建了多个 ML 模型,包括多元回归模型(MR)、K-近邻模型(KNN)、多层感知器模型(MLP)、连续最小优化回归模型(SMOreg)、随机森林模型(RF)和新型 K-近邻模型(KNORA)。这些模型的平均误差采用均方根误差(RMSE)进行评估。实验结果表明,KNORA 集合模型表现最佳,在使用前 10 个特征进行测试时,RMSE 为 7.54 mg/h。MLP 紧随其后,在相同测试中的 RMSE 为 25.1 毫克/小时。使用所有特征的经验模型显示出更高的均方根误差(RMSE),达到 1319.1 毫克/小时,这表明 ML 模型在准确预测蒸发率方面更具优势。我们强调了我们的结果对现实世界中由气候引起的土壤开裂的影响。
Can machine learning models predict soil moisture evaporation rates? An investigation via novel feature selection techniques and model comparisons
Extreme weather events and global climate change have exacerbated the problem of evaporation rates. Thus, accurately predicting soil moisture evaporation rates affecting soil cracking becomes crucial. However, less is known about how novel feature engineering techniques and machine-learning predictions may account for estimating the soil moisture evaporation rate. This research focuses on predicting the evaporation rate of soil using machine learning (ML) models. The dataset comprised twenty-one ground-based parameters, including temperature, humidity, and soil-related features, used as features to predict evaporation potential. To tackle the high number of features and potential uncorrelated features, a novel guided backpropagation-based feature selection technique was developed to rank the most relevant features. The top-10 features, highly correlated with evaporation rate, were selected for ML model input, alongside the top-5 and all features. Several ML models, including multiple regression (MR), K-nearest neighbor (KNN), multilayer perceptron (MLP), sequential minimal optimization regression (SMOreg), random forest (RF), and a novel K-Nearest Oracles (KNORA) ensemble, were constructed for the purpose of forecasting the evaporation rate. The average error of these models was assessed using the root mean squared error (RMSE). Experimental results showed that the KNORA ensemble model performed the best, achieving a 7.54 mg/h RMSE in testing with the top-10 features. MLP was followed closely by a 25.1 mg/h RMSE in the same testing. An empirical model using all features showed a higher RMSE of 1319.1 mg/h, indicating the superiority of the ML models for accurate evaporation rate predictions. We highlight the implications of our results for climate-induced soil cracking in the real world.
期刊介绍:
Frontiers in Earth Science is an open-access journal that aims to bring together and publish on a single platform the best research dedicated to our planet.
This platform hosts the rapidly growing and continuously expanding domains in Earth Science, involving the lithosphere (including the geosciences spectrum), the hydrosphere (including marine geosciences and hydrology, complementing the existing Frontiers journal on Marine Science) and the atmosphere (including meteorology and climatology). As such, Frontiers in Earth Science focuses on the countless processes operating within and among the major spheres constituting our planet. In turn, the understanding of these processes provides the theoretical background to better use the available resources and to face the major environmental challenges (including earthquakes, tsunamis, eruptions, floods, landslides, climate changes, extreme meteorological events): this is where interdependent processes meet, requiring a holistic view to better live on and with our planet.
The journal welcomes outstanding contributions in any domain of Earth Science.
The open-access model developed by Frontiers offers a fast, efficient, timely and dynamic alternative to traditional publication formats. The journal has 20 specialty sections at the first tier, each acting as an independent journal with a full editorial board. The traditional peer-review process is adapted to guarantee fairness and efficiency using a thorough paperless process, with real-time author-reviewer-editor interactions, collaborative reviewer mandates to maximize quality, and reviewer disclosure after article acceptance. While maintaining a rigorous peer-review, this system allows for a process whereby accepted articles are published online on average 90 days after submission.
General Commentary articles as well as Book Reviews in Frontiers in Earth Science are only accepted upon invitation.