Erosion potential model-based ANN-MLP for the spatiotemporal modeling of soil erosion in wadi Saida watershed

Cherif, Kessar; Yahia, Nasrallah; Bilal, Beldjilali; Bilal, Blissag

doi:10.1007/s40808-022-01657-3

Cited by 14 publications

(2 citation statements)

References 94 publications

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“…The Cellular Automata (CA) model has been used in simulating the spatial LULCC by estimating the state of a pixel according to its initial state, surrounding neighbourhood effects and transition rules (Eastman 2016 Arti cial neural network (ANN) is a popular tool used in the analysis of satellite remotely sensed data (Mas and Flores 2008) mainly due to the fact that it characterizes a comparatively new approach to developing predictive models (Blackard and Dean 1999). ANN-MLP has been applied in spatio-temporal modeling of soil erosion studies (Cherif et al 2023). In the medical eld, this approach has been used to simulate biological nervous systems (Gopal 2017 Predicting LULCC is signi cant for a number of developmental issues including urban expansion, deforestation and forest degradation (Li and Yeh 2002).…”

Section: Introductionmentioning

confidence: 99%

Multi-Decade Land Cover/Land Use Dynamics and Future Predictions for Zambia: 2000 - 2030

Chisanga,

Phiri,

Mubanga

2023

Preprint

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Accurate and up-to-date information on land use/land cover change (LULCC) is important in land use planning and natural resource management; however, in sub-Saharan Africa, detailed information on LULCC is still lacking. Therefore, this study assessed the dynamics of LULC change (2000–2020) and future projections (2020–2030) for Zambia. The 2000 and 2010 LULC maps were used to simulate the 2020 LULC scenario using Artificial Neural Network (Multi-layer Perception) algorithms in Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS 2.18.14. The 2010 and 2020 maps were used to predict the 2030 LULC classes. The reference 2020 and predicted 2020 LULC maps were used to validate the model. The validation between the predicted and observed 2020 LULC map, Kappa (loc) was 0.9869. The ANN-MLP simulated the 2020 LULC patterns successfully as indicated by the high accuracy level of more than 95%. LULC classes were predicted for 2030 using the 2010–2020 calibration period. The expected LULC types for 2030 revealed that built-up area will increase by 447.20 km2 (71.44%), while 327.80 km2 (0.73%) of cropland will be lost relative to 2020 LULC map. Dense forest (0.19%), grassland (0.85%) and bare land (1.37%) will reduce from 2020–2030. However, seasonally flooded, sparse forest, shrub land, wetland and water body will increase marginally. The largest LULC change is from forest into other LULC types. The insights from this study show that ANN-MLP can be used to predict LULCC, and that the generated information can be employed in land use planning at a national scale.

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Section: Introductionmentioning

confidence: 99%

Multi-Decade Land Cover/Land Use Dynamics and Future Predictions for Zambia: 2000 - 2030

Chisanga,

Phiri,

Mubanga

2023

Preprint

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“…Also, during research, the adaptive neural-fuzzy inference system model has been used in simulating and forecasting the electric energy demand of the G8 countries until 2020 [15] . Cherif et al [16] have used the combined neural-fuzzy and particle mass algorithm in their studies to predict the longterm demand for electric energy in the Algeria until 2025. The results of his study show the high power of the combinational algorithm of particle masses and the adaptive neural-fuzzy inference system in forecasting.…”

Section: Introductionmentioning

confidence: 99%

Proposing a new optimized forecasting model for the failure rate of power distribution network thermal equipment for educational centers

Shaghaghi,

Omidifar,

Zahedi

et al. 2023

Therm. Sci. Eng.

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To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables to the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

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Soil erosion vulnerability and soil loss estimation for Siran River watershed, Pakistan: an integrated GIS and remote sensing approach

Mehwish,

Nasir,

Raziq

et al. 2023

Environ Monit Assess

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Erosion potential model-based ANN-MLP for the spatiotemporal modeling of soil erosion in wadi Saida watershed

Cited by 14 publications

References 94 publications

Multi-Decade Land Cover/Land Use Dynamics and Future Predictions for Zambia: 2000 - 2030

Multi-Decade Land Cover/Land Use Dynamics and Future Predictions for Zambia: 2000 - 2030

Proposing a new optimized forecasting model for the failure rate of power distribution network thermal equipment for educational centers

Soil erosion vulnerability and soil loss estimation for Siran River watershed, Pakistan: an integrated GIS and remote sensing approach

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