Machine learning-based modeling of saturated hydraulic conductivity in soils of tropical semi-arid zone of India

More, Satish Bhaurao; Deka, Paresh Chandra; Patil, Amit Prakash; Naganna, Sujay Raghavendra

doi:10.1007/s12046-022-01805-6

Cited by 6 publications

(9 citation statements)

References 53 publications

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“…In our study, we employed a novel feature selection technique, guided backpropagation, to identify and rank the most relevant features correlated with soil moisture evaporation rates. While direct experimental studies to correlate each individual feature with moisture evaporation were not conducted due to the scope and resource limitations of our work, the selection of features was grounded in an extensive literature review and prior experimental 10.3389/feart.2024.1344690 findings from related studies (Yilmaz and Kaynar, 2011;Chou et al, 2016;Kumar et al, 2019;Wani et al, 2021;Kardani et al, 2022;More et al, 2022;Nguyen et al, 2022;Verma et al, 2023), including the foundational work of (Uday and Singh, 2013) that detailed the impact of various soil and environmental factors on evaporation rates. The guided backpropagation method allowed us to prioritize features scientifically known to influence evaporation, such as temperature, humidity, soil type, and specific gravity, among others.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, researchers have employed Random Forest (RF) models to study this phenomenon, while others have utilized K-nearest neighbor (KNN) models (Kardani et al, 2022;Verma et al, 2023). Additionally, support vector machine (SVM) models have been developed by various researchers for this purpose (Chou et al, 2016;More et al, 2022). Furthermore, multilayer perceptron (MLP) models have been explored by different researchers, and multiple regression (MR) models have also been investigated in relevant studies (Yilmaz and Kaynar, 2011;Wani et al, 2021;Nguyen et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…In the literature related to ML in the geotechnical field, several ML models have been developed to investigate the influence of geotechnical factors on soil cracks (Yilmaz and Kaynar, 2011;Chou et al, 2016;Kumar et al, 2019;Wani et al, 2021;Kardani et al, 2022;More et al, 2022;Nguyen et al, 2022;Verma et al, 2023). For instance, researchers have employed RF models to study this phenomenon, while others have utilized KNN models (Kumar et al, 2019;Kardani et al, 2022;Verma et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…The model's performance shows significant improvement compared to other reported models, contributing valuable insights for predicting FRS behavior in soil engineering. In a recent study, researchers investigated the application of 3 ML models, namely, Extreme Learning Machine (ELM), SVM, and Adaptive Neuro-Fuzzy Inference System (ANFIS), to construct Pedotransfer Functions (PTFs) for the estimation of saturated hydraulic conductivity (Kfs) based on fundamental soil parameters (More et al, 2022). Among these models, SVM demonstrated notable performance, yielding relatively high Nash-Sutcliffe Efficiency (NSE) values.…”

Section: Introductionmentioning

confidence: 99%

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Can machine learning models predict soil moisture evaporation rates? An investigation via novel feature selection techniques and model comparisons

Priyanka,

Kumar,

Panda

et al. 2024

Front. Earth Sci.

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Can machine learning models predict soil moisture evaporation rates? An investigation via novel feature selection techniques and model comparisons

Priyanka,

Kumar,

Panda

et al. 2024

Front. Earth Sci.

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“…Li et al [25] suggested a technique to calculate green and blue water EV T 0 amount in ArcGIS areas using geographic information to enhance water management efficiency. Patil and Deka [26] suggested calculating weekly EV T 0 based on several environmental factors. They applied their solution in India and contrasted it with the Hargreaves (H) method [27].…”

Section: Literature Surveymentioning

confidence: 99%

Smart irrigation for coriander plant: Saving water with AI and IoT

Paria,

Giri,

Dutta

et al. 2024

Preprint

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Accurate forecasting of water requirements is crucial for optimizing irrigation and water preservation. However, the Food and Agriculture Organization(FAO Irrigation and Drainage paper 56) Penman-Monteith(PM) model is observed as the highest quality method for evapotranspiration (EVT0 ) forecasting. However, using the PM model is often restricted by the need for predicted climatic factors, particularly solar radiation. This research article presents a real-time intelligent watering system for coriander plants that can be monitored using smartphones. The system uses a hybrid machine-learning technique and Internet of Things (IoT) sensors to sense weather circumstances directly from the crop field. Nine distinct hybrid neural network models ((HML1, HML2 …, HML9)) are developed to predict water requirements using climate and environmental variables. These models are optimized using a genetic algorithm to achieve optimal efficiency. The EVT0 forecasts of the proposed approach are being compared against the standard FAO56 Penman-Monteith technique. An in-depth analysis of the highly successful HML4 model is conducted, and the findings are used in a developed Android application that enables real-time monitoring. In addition, the most favourable parameters are determined to achieve even more improved outcomes. This intelligent irrigation system can significantly minimize flood irrigation, water consumption, and labour expenses.

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Machine learning applications in vadose zone hydrology: A review

Li,

Nieber,

Kumar

2024

Vadose Zone Journal

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Machine learning (ML) has been broadly applied for vadose zone applications in recent years. This article provides a comprehensive review of such developments. ML applications for variables corresponding to different complex vadose zone processes are summarized mostly in a prediction context. By analyzing and assessing these applications, we discovered extensive usages of classic ML models with relatively limited applications of deep learning (DL) approaches in general. We also recognized a lack of benchmark datasets for soil property research as well as limited integration of physics‐based vadose zone principles into the ML approaches. To facilitate this interdisciplinary research of ML in vadose zone characterization and processes, a paradigm of knowledge‐guided machine learning is suggested along with other data‐driven and ML model‐based research suggestions to advance future research.

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Machine learning-based modeling of saturated hydraulic conductivity in soils of tropical semi-arid zone of India

Cited by 6 publications

References 53 publications

Can machine learning models predict soil moisture evaporation rates? An investigation via novel feature selection techniques and model comparisons

Can machine learning models predict soil moisture evaporation rates? An investigation via novel feature selection techniques and model comparisons

Smart irrigation for coriander plant: Saving water with AI and IoT

Machine learning applications in vadose zone hydrology: A review

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