Delineating changes in soil erosion risk zones using RUSLE model based on confusion matrix for the Urmodi river watershed, Maharashtra, India

Bagwan, Wasim Ayub; Gavali, R. S.

doi:10.1007/s40808-020-00965-w

Cited by 24 publications

(8 citation statements)

References 31 publications

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“…It was also demonstrated that about 44.9 % of the study area fell under very high soil erosion risk, while high-probability zones covered 42.9% of the study areas (Figure 11). Compared with an earlier study [63] in the Urmodi river watershed (India), a significant output regarding increased conversion was obtained from very slight to very severe risk zone; research concluded the existence of extensive variation in very severe soil vulnerable risk zones over the study period. Overall, the findings of this study and other studies carried out in Rwanda and east African regions similar to results reported by Karamage [30,62], which seem to overestimate soil loss rates.…”

Section: Estimation Of Soil Loss and Probability Zonescontrasting

confidence: 66%

Application of the Adapted Approach for Crop Management Factor to Assess Soil Erosion Risk in an Agricultural Area of Rwanda

Maniraho

Mind’je

Liu

et al. 2021

Land

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Land use and land cover (LULC) management influences the severity of soil erosion risk. However, crop management (C) is one factor of the Revised Universal Soil Loss Equation (RUSLE) model that should be taken into account in its determination, as it influences soil loss rate estimations. Thus, the present study applied an adapted C-factor estimation approach (CvkA) modified from the former approach (Cvk) to assess the impact of LULC dynamics on soil erosion risk in an agricultural area of Rwanda taking the western province as a case study. The results disclosed that the formerly used Cvk was not suitable, as it tended to overestimate C-factor values compared with the values obtained from t CvkA. An approximated mean soil loss of 15.1 t ha−1 yr−1, 47.4 t ha−1 yr−1, 16.3 t ha−1 yr−1, 66.8 t ha−1 yr−1 and 15.3 t ha−1 yr−1 in 2000, 2005, 2010, 2015 and 2018, respectively, was found. The results also indicated that there was a small increase in mean annual soil loss from 15.1 t ha−1 yr−1 in 2000 to 15.3 t ha−1 yr−1 in 2018 (1.3%). Moreover, the soil erosion risk categories indicated that about 57.5%, 21.8%, 64.9%, 15.5% and 73.8% had a sustainable soil erosion rate tolerance (≤10 t ha−1 yr−1), while about 42.5%, 78.2%, 35.1%, 84.5% and 16.8% had an unsustainable mean soil erosion rate (>10 t ha−1 yr−1) in 2000, 2005, 2010, 2015 and 2018, respectively. A major portion of the area fell under the high and very high probability zones, whereas only a small portion fell under the very low, low, moderate and extremely high probability zones. Therefore, the CvkA approach presents the most suitable alternative to estimate soil loss in the western province of Rwanda with reasonable soil loss prediction results. The study area needs urgent intervention for soil conservation planning, taking into account the implementation of effective conservation practices such as terracing for soil erosion control.

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Section: Estimation Of Soil Loss and Probability Zonescontrasting

confidence: 66%

Application of the Adapted Approach for Crop Management Factor to Assess Soil Erosion Risk in an Agricultural Area of Rwanda

Maniraho

Mind’je

Liu

et al. 2021

Land

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“…It is a method for classifying the predicted values, namely the matching degree between the predicted values of the model to be trained on the test set and the real values on the test set. When the predicted value is equal to the true value, the matrix attains the correct classification that is located on the diagonal of the matrix, and non-diagonal elements represent the wrong classification [ 30 , 31 ].…”

Section: Screening and Function Prediction Model Of Degs Based On Dee...mentioning

confidence: 99%

Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things

Guo

et al. 2022

PLoS ONE

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The deep neural network is used to establish a neural network model to solve the problems of low accuracy and poor accuracy of traditional algorithms in screening differentially expressed genes and function prediction during the walnut endocarp hardening stage. The paper walnut is used as the research object to analyze the biological information of paper walnut. The changes of lignin deposition during endocarp hardening from 50 days to 90 days are observed by microscope. Then, the Convolutional Neural Network (CNN) and Long and Short-term Memory (LSTM) network model are adopted to construct an expression gene screening and function prediction model. Then, the transcriptome and proteome sequencing and biological information of walnut endocarp samples at 50, 57, 78, and 90 days after flowering are analyzed and taken as the training data set of the CNN + LSTM model. The experimental results demonstrate that the endocarp of paper walnut began to harden at 57 days, and the endocarp tissue on the hardened inner side also began to stain. This indicates that the endocarp hardened laterally from outside to inside. The screening and prediction results show that the CNN + LSTM model’s highest accuracy can reach 0.9264. The Accuracy, Precision, Recall, and F1-score of the CNN + LSTM model are better than the traditional machine learning algorithm. Moreover, the Receiver Operating Curve (ROC) area enclosed by the CNN + LSTM model and coordinate axis is the largest, and the Area Under Curve (AUC) value is 0.9796. The comparison of ROC and AUC proves that the CNN + LSTM model is better than the traditional algorithm for screening differentially expressed genes and function prediction in the walnut endocarp hardening stage. Using deep learning to predict expressed genes’ function accurately can reduce the breeding cost and significantly improve the yield and quality of crops. This research provides scientific guidance for the scientific breeding of paper walnut.

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“…In addition, the confusion matrix is a prominent way to analyze data and resolve problems that require classification [43,44]. The primary diagonal line displays the total number of classes correctly predicted by the intelligent system.…”

Section: Accuracymentioning

confidence: 99%

A Multi-Criteria Decision Intelligence Framework to Predict Fire Danger Ratings in Underground Engineering Structures

Kamran,

Chaudhry,

Wattimena

et al. 2023

Fire

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A wide variety of natural catastrophes are induced by coal mining, with fire hazard being one of the most significant threats to underground engineering structures. In recent years, there has been an alarming rise in mine fire accidents due to the abundance of coal deposits around the world. Underground fires and explosions have continuously been the primary reason for a significant proportion of deaths and the destruction of infrastructure over the last few decades. Underground mining fires deplete natural coal resources, have an adverse impact on the environment by releasing hazardous chemicals and greenhouse gases into the atmosphere, and cause subsidence due to coal depletion during the combustion process. This study aims to predict fire danger rating of underground mining production processes by using the application of state-of-the-art unsupervised and supervised machine learning techniques. The developed k-nearest-neighbors-based isometric feature mapping and fuzzy c-means clustering algorithm has shown its dependability and superiority with a higher accuracy and has been advantageous to the monitoring and prevention of fire danger in underground mining production processes. The proposed multi-criteria decision intelligence framework permits early fire detection, providing the emergency response team extra time to respond the critical situations in order to prevent the fire from spreading, hence promoting sustainable, green, climate-smart, environmentally friendly and safe mining engineering operations.

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Delineating changes in soil erosion risk zones using RUSLE model based on confusion matrix for the Urmodi river watershed, Maharashtra, India

Cited by 24 publications

References 31 publications

Application of the Adapted Approach for Crop Management Factor to Assess Soil Erosion Risk in an Agricultural Area of Rwanda

Application of the Adapted Approach for Crop Management Factor to Assess Soil Erosion Risk in an Agricultural Area of Rwanda

Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things

A Multi-Criteria Decision Intelligence Framework to Predict Fire Danger Ratings in Underground Engineering Structures

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