Priyanka Priyanka, Praveen Kumar, Sucheta Panda, Tejinder Thakur, K. V. Uday, Varun Dutt
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引用次数: 0
Abstract
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.