How to easily use ANN for prediction mapping using GIS data?

Learn the theory of artificial neural networks, including input, hidden, and output layers, weights, bias, activation functions, backpropagation, and supervised learning for GIS data prediction mapping.

Apply the neural net package to landslide distribution modeling with GIS factors like digital elevation models and land uses; train, calibrate, and optimize a multilayer perceptron for improved predictions.

Unlock neural network based prediction mapping for GIS data by converting inputs to numerical features, training and testing an ANN, and visualizing results on accessible hardware.

Add your developed models to the QGIS 3.14 processing library by importing models, configuring the toolbox, saving, restarting, and applying grid and slope units to point features.

Convert land cover strings to numeric codes in QGIS, create an integer field, map seven classes to 1–7, then rasterize the vector to a 30-meter land cover raster.

Learn step 1 of using geospatial tools to process polygon and point data, compute slope and landslide within slope, and identify safe areas for prediction mapping.

Prepare training and testing data for landslide prediction with GIS data, create 100 by 100 grids (30 m pixels), apply a 70/30 split, then run the tools for pixel-based mapping.

Explore how to use an artificial neural network for pixel-by-pixel landslide prediction with GIS data, training and testing, and layers such as elevation, slope, and land cover.

Copy pixel values into Excel, convert to numeric codes, and prepare training and testing data for ANN-based GIS prediction, converting land cover strings to numbers.

Prepare GIS data for an artificial neural network by selecting relevant features such as roughness, elevation, aspect, and land cover, while cleaning missing values and standardizing training and testing sets.

Learn to build a neural network model for landslide susceptibility using GIS data, covering data preparation, training/testing splits, scaling, sensitivity analysis, and prediction calibration with AUC evaluation.

Define a working directory and libraries, read training data from Excel into a data frame, clean inputs, handle missing data, and convert aspect to numeric categories for ANN mapping.

Convert slope aspect from a categorical to numeric by binning 0–360 into eight 45-degree sectors labeled A–H, then create individual variables for ANN-based GIS prediction.

Convert land-cover categories into numeric values for ann-based prediction mapping by encoding 1-7 and treating zero as a separate, unused level, while removing the original aspect and land-cover variables.

Check the training data distribution, apply min-max scaling to map features to 0–1 for ANN predictions on GIS data, and learn how to revert to original values.

Process testing data by converting land cover categories to factors, validating structure, aligning names with training data, applying zero-to-one scaling, and preparing the neural network function.

Run neuralnet to map predictions from GIS data, detailing data prep, scaling, training vs testing data, and setting a hidden layer with five nodes before backpropagation.

Learn to plot an ann neural network and assess error estimation for gis-based prediction mapping, including adjusting thresholds, repetition, and exporting plots for analysis.

Add the neural network output as a new column in the training data frame, renaming variable 23 to a readable label, and inspect the 0 to 1 prediction range.

Learn how to recover original values from scaled data using the inverse transformation with the range and minimum, and apply this to GIS data in ANN-based prediction mapping.

Analyze pairwise Barwise plots to reveal nonlinear relationships between covariates and the response, assess significance, and evaluate prediction tied to land cover and roughness.

Apply the generalized weight plot to evaluate covariate influence in training data, focusing on slope, land cover, elevation, and other factors to improve gis-based prediction mapping.

Run a neural network on GIS data using NNET or end net, generate a relative importance bar plot of weights, and compare Barwise plots while noting randomness affects results.

Plot and visualize a neural network with multiple hidden layers and one output using the nnet library for prediction mapping with gis data, illustrating weights with thick and light lines.

Explore how sensitivity analysis in a neural net maps how elevation, slope, and other factors influence a response variable, revealing linear versus nonlinear relationships while holding others constant.

Run the neural network's compute output prediction on training data, then validate with testing data using cross-tabulation to assess calibration and model performance.

Update the dataframe by removing the roughness, retrain with the adjusted data, and re-evaluate training and testing accuracy to ensure consistent inputs in ANN-based prediction mapping with GIS data.

Remove roughness and land cover seven from the training data to update the dataframe for gis-based ann predictions using cross tabulation, then assess how accuracy changes.

Test an ANN with GIS data, removing the first variable to 21 features, and use cross tabulation to report 91 percent training performance and 74 percent prediction rate.

Compute the area under the ROC curve to evaluate ANN-based prediction and success rates with GIS data, and learn data cleaning and training versus testing performance.

Install the roster packages in R studio, import the layers, align extents, rename the data layers, and stack land cover and elevation layers for GIS-based ANN prediction mapping.

Align raster extents, reassemble rasters by resampling to a reference land cover, then stack and prepare a data frame for ANN prediction using elevation, curvature, and slope.

Scale rasters for gis data by harmonizing curvature, elevation, and land cover, then merge x and y with covariates for neural network prediction.

Run the compute prediction function on rasters stack data using an artificial neural network for GIS-based prediction mapping, removing nonmatching variables and integrating results into a data frame.

Use an artificial neural network to produce a final raster map from GIS data, converting the data frame to raster with x, y, and projection, then export the prediction map.

Export the predicted raster landslide map to QGIS from GIS data, visualize susceptible areas with low and high values, and generate a final layout for decision making.

Learn end-to-end neural network modeling with GIS data, from data preparation and feature encoding to scaling, training a five-hidden-layer network, evaluating accuracy, and generating a prediction map.