scholarly journals Risk Assessment of Runoff Generation Using Artificial Neural Network and Field Plots in The Area of Roads and Forest Lands

2021 ◽  
Author(s):  
Pejman Dalir ◽  
Ramin Naghdi ◽  
Vahid Gholami ◽  
Farzam Tavankar ◽  
Francesco Latterini ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Texture ◽  
Slope Gradient ◽  
Runoff Generation ◽  
The Caspian Sea ◽  
Good Tool ◽  
Forest Road ◽  
Runoff Volume ◽  
Artificial Neural

Abstract Runoff generation potential (RGP) on hillslopes is an important issue in the forest roads network monitoring process. In this study, the artificial neural network (ANN) was used to predict RGP in forest road hillslopes. We trained, optimized, and tested the ANN by using field plot data from the Shirghalaye watershed located in the southern part of the Caspian Sea (Iran). 45 plots were installed to measure actual runoff volume (RFP) in different environmental conditions including land cover, slope gradient, soil texture, and soil moisture. A multi-layer perceptron (MLP) network was implemented. The runoff volume was the output variable and the ground cover, slope gradient, initial moisture of soil, soil texture (clay, silt, and sand percentage) were the network inputs. The results showed that ANN can predict runoff volume within the values of an appropriate level in the training (R2=0.95, MSE= 0.009) and test stages (R2=0.80, MSE= 0.01). Moreover, the tested network was used to predict the runoff volume on the forest road hillslopes in the study area. Finally, an RGP map was generated based on the results of the prediction of the ANNs and the GIS capabilities. The results showed that using both an ANN and a GIS is a good tool to predict the RGP in the forest road hillslopes.

Soil texture and plant degradation predictive model (STPDPM) in national parks using artificial neural network (ANN)

2020 ◽  
Vol 6 (2) ◽  
pp. 715-729 ◽  
Author(s):  
Zahra Mosaffaei ◽  
Ali Jahani ◽  
Mohammad Ali Zare Chahouki ◽  
Hamid Goshtasb ◽  
Vahid Etemad ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
National Parks ◽  
Soil Texture ◽  
Plant Degradation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Using Artificial Neural Network to Filter Spam for Chinese Mail

2011 ◽  
Vol 55-57 ◽  
pp. 762-766
Author(s):  
Shih Ming Pi ◽  
Hsiu Li Liao ◽  
Su Houn Liu ◽  
Ding Kang Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Text Classification ◽  
False Positives ◽  
The Internet ◽  
Network Approach ◽  
Good Tool ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Computing Speed

As the Internet developed, the problem of spam has become increasingly serious. Not only caused great distress to individuals, but also have a great business costs. With improvements in computing speed, neural network is becoming a very good tool for text classification. The purpose of this study is to conduct few experiments by using neural network approach for Chinese mails’ content. The result shows that neural network approach is effective for Chinese mails’ spam-identification and the adjustments of some parameters (the number of keywords, the number of nodes, and the number of categories) also increase the accurate rate, while reducing false positives.

scholarly journals Artificial Neural Network Application for Thermal Image Based Condition Monitoring of Zinc Oxide Surge Arresters

2017 ◽  
Vol 7 (3) ◽  
pp. 593
Author(s):  
Novizon Novizon ◽  
Zulkurnain Abdul-Malek ◽  
Aulia Aulia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Leakage Current ◽  
Thermal Image ◽  
Histogram Method ◽  
Good Tool ◽  
Third Harmonic ◽  
Surge Arresters ◽  
Long Time ◽  
Artificial Neural

<p>Manual analysis of thermal image for detecting defects and classifying of condition of surge arrester take a long time. Artificial neural network is good tool for predict and classify data. This study applied neural network for classify the degree of degradation of surge arrester. Thermal image as input of neural network was segmented using Otsu’s segmentation and histogram method to get features of thermal image. Leakage current as a target of supervise neural network was extracted and applied Fast Fourier Transform to get third harmonic of resistive leakage current. The classification results meet satisfaction with error about 3%.</p>

scholarly journals A simulation of the rainfall-runoff process using artificial neural network and HEC-HMS model in forest lands

2021 ◽  
Vol 67 (No. 4) ◽  
pp. 165-174
Author(s):  
Vahid Gholami ◽  
Mohammad Reza Khaleghi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Soil Infiltration ◽  
Hydrologic Modelling ◽  
Moisture Condition ◽  
Simulation Performance ◽  
Artificial Neural ◽  
Forest Lands

Simulation of the runoff-rainfall process in forest lands is essential for forest land management. In this research, a hydrologic modelling system (HEC-HMS) and artificial neural network (ANN) were applied to simulate the rainfall-runoff process (RRP) in forest lands of Kasilian watershed with an area of 68 square kilometres. The HMS model was performed using the secondary data of rainfall and discharge at the climatology and hydrometric stations, the Soil Conservation Service (SCS) for simulating a flow hydrograph, the curve number (CN) method for runoff estimation, and lag time method for flow routing. Further, a multilayer perceptron (MLP) network was used for simulating the rainfall-runoff process. HEC-HMS model was used to optimize the initial loss (IL) values in the rainfall-runoff process as an input. IL reflects the conditions of vegetation, soil infiltration, and antecedent moisture condition (AMC) in soil. Then, IL values and also incremental rainfall were applied as inputs into ANN to simulate the runoff values. The comparison of the results of simulating the RRP in two scenarios, using IL and without IL, showed that the IL parameter has a high effect in increasing the simulation performance of the rainfall-runoff process. Moreover, ANN predictions were more precise in comparison with those of the HMS model. Further, forest lands can significantly increase IL values and decrease runoff generation.

scholarly journals Rainfall and earthquake-induced landslide susceptibility assessment using GIS and Artificial Neural Network

2012 ◽  
Vol 12 (8) ◽  
pp. 2719-2729 ◽  
Author(s):  
Y. Li ◽  
G. Chen ◽  
C. Tang ◽  
G. Zhou ◽  
L. Zheng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Success Rate ◽  
Landslide Susceptibility ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Slope Gradient ◽  
Entire Study ◽  
Weight Variation ◽  
Artificial Neural

Abstract. A GIS-based method for the assessment of landslide susceptibility in a selected area of Qingchuan County in China is proposed by using the back-propagation Artificial Neural Network model (ANN). Landslide inventory was derived from field investigation and aerial photo interpretation. 473 landslides occurred before the Wenchuan earthquake (which were thought as rainfall-induced landslides (RIL) in this study), and 885 earthquake-induced landslides (EIL) were recorded into the landslide inventory map. To understand the different impacts of rainfall and earthquake on landslide occurrence, we first compared the variations between landslide spatial distribution and conditioning factors. Then, we compared the weight variation of each conditioning factor derived by adjusting ANN structure and factors combination respectively. Last, the weight of each factor derived from the best prediction model was applied to the entire study area to produce landslide susceptibility maps. Results show that slope gradient has the highest weight for landslide susceptibility mapping for both RIL and EIL. The RIL model built with four different factors (slope gradient, elevation, slope height and distance to the stream) shows the best success rate of 93%; the EIL model built with five different factors (slope gradient, elevation, slope height, distance to the stream and distance to the fault) has the best success rate of 98%. Furthermore, the EIL data was used to verify the RIL model and the success rate is 92%; the RIL data was used to verify the EIL model and the success rate is 53%.

A Novel Integrated Rainfall-Runoff Model Based on TOPMODEL and Artificial Neural Network

2013 ◽  
Vol 423-426 ◽  
pp. 1405-1408 ◽  
Author(s):  
Shuang Liu ◽  
Jing Wen Xu ◽  
Jun Fang Zhao ◽  
Yong Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Technique ◽  
Integrated Model ◽  
Physical Models ◽  
Efficiency Coefficient ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Time Step ◽  
Artificial Neural

Conventional process-based rainfall-runoff models are difficult to catch the non-linear factors and to take full advantages of previous information of rainfall and runoff. However, these factors are closely related to the initial watershed average saturation deficit at each time step. Therefore, in order to address the issue, this study selected the parameter about initial underground flow in TOPMODEL (TOPOgraphic driven Model) as the breakthrough point. Then we used the previous two-day observed runoff and rainfall data as the inputs of an artificial neural network (ANN) and initial subsurface flow of present day as an output, then integrated ANN into runoff generation module in TOPMODEL and finally applied the integrated model for daily runoff modeling in Yingluoxia watershed with 10009km2, China. In addition, this work also utilized particle swarm optimization technique (PSO) to avoid the local optimization, especially for the integration of black-box and physical models. The result shows that during the validation period the Nash-Sutcliffe efficiency coefficient (NE) and root mean square error (RMSE) of TOPMODEL are 0.45 and 3.88×10-4m respectively while the NE of 0.70 and RMSE of 2.85×10-4m for the integrated model. Significantly, the integrated model performs much better than the traditional model. Hence, this new method of integrating ANN with the runoff generation module of TOPMODEL is promising and easily extended to other process-based rainfall-runoff models as well.

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