Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model

Abstract

These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the country's environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and detection of drought can provide guidance to water resources management for employing drought mitigation policies. In this paper, we used a multilayer perceptron neural network (MLPNN) algorithm for drought forecasting. We applied and tested MLPNN algorithm on monthly time series data of Standardized Precipitation Evapotranspiration Index (SPEI) for seventeen climatological stations located in Northern Area and KPK (Pakistan). We found that MLPNN has potential capability for SPEI drought forecasting based on performance measures (i.e., Mean Average Error (MAE), the coefficient of correlation ( ), and Root Mean Square Error (RMSE)). Water resources and management planner can take necessary action in advance (e.g., in water scarcity areas) by using MLPNN model as part of their decision-making.

A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya’s Operational Drought Monitoring

“…Most of the approaches are based on the use of single variables of either precipitation or vegetation conditions. Examples of predictions based on precipitation data are either based on SPI as is the case in Ali [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16], [1,2,20,24] others define a super index of drought indices in the approach of [23] and [25] that define Multi-variate standardised dry index (MSDI) and Drought defining Index (DDI) respectively. The use of vegetation conditions in [21] in a forecast study stands-out in its use of 11 attributes to predict vegetation conditions.…”

“…ANNs have several characteristics making them suitable for the purpose of predictive modelling: (1) instances can be represented by many attribute value pairs, (2) the target function is either discrete, real or vector valued, (3) training examples may contain errors, (4) non-linear relations can be modeled, and (5) execution (after training) is very quick, …(4) long training times are acceptable while faster evaluation is required since the process will be run on a monthly basis.…”

“…A drought is a recurrent event marked by lack of precipitation for extended period of times (Morid, Smakhtin & Bagherzadeh, 2007;Bordi et al, 2005) [1,2]. Droughts are one of the most complex and less understood disasters, having the greatest impacts on people and usually affecting large regions (Morid, Smakhtin & Bagherzadeh, 2007;Ali et al,2017) [1,3].…”

Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data

“…Droughts are generally classified into four categories, namely, meteorological, hydrological, agricultural, and socio-economic droughts [9].In recent years, the prevalence of machine learning methodologies, and frequent droughts and floods around the world, have increased the prediction of agricultural drought. However, the uncertainties of prediction caused by combination of meteorological factors [10][11][12][13], still remain a problem. According to the Intergovernmental Panel on Climate Change (IPCC) AR5 guidance note, the complex use of different models, complexity of models, and inclusion of additional processes in the analysis are the main reasons for the increase in uncertainties [14].…”

A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes

“…At this moment, the time step for weighted Kappa is decided according to the steady state nature of the transition probability matrix. If transition probability matrix Table 1: Drought classification criteria of SPTI [27].…”