Regression Modelling for Precipitation Prediction Using Genetic Algorithms

This paper discusses the formation of an appropriate regression model in precipitation prediction. Precipitation prediction has a major influence to multiply the agricultural production of potatoes in Tengger, East Java, Indonesia. Periodically, the precipitation has non-linear patterns. By using a non-linear approach, the prediction of precipitation produces more accurate results. Genetic algorithm (GA) functioning chooses precipitation period which forms the best model. To prevent early convergence, testing the best combination value of crossover rate and mutation rate is done. To test the accuracy of the predicted results are used Root Mean Square Error (RMSE) as a benchmark. Based on the RMSE value of each method on every location, prediction using GA-Non-Linear Regression is better than Fuzzy Tsukamoto for each location. Compared to Generalized Space-Time Autoregressive-Seemingly Unrelated Regression (GSTAR-SUR), precipitation prediction using GA is better. This has been proved that for 3 locations GA is superior and on 1 location, GA has the least value of deviation level

Rainfall Prediction in Tengger, Indonesia Using Hybrid Tsukamoto FIS and Genetic Algorithm Method

Countries with a tropical climate, such as Indonesia, are highly dependent on rainfall prediction for many sectors, such as agriculture, aviation, and shipping. Rainfall has now become increasingly unpredictable due to climate change and this phenomenon also affects Indonesia. Therefore, a robust approach is required for more accurate rainfall prediction. The Tsukamoto Fuzzy Inference System (FIS) is one of the algorithms that can be used for prediction problems, but if its membership functions are not specified properly, the prediction error is still high. To improve the results, the boundaries of the membership functions can be adjusted automatically by using a genetic algorithm. The proposed genetic algorithm employs two selection processes. The first one uses the Roulette wheel method to select parents, while the second one uses the elitism method to select chromosomes for the next generation. Based on this approach, a rainfall prediction experiment was conducted for Tengger, Indonesia using historical rainfall data for ten-year periods. The proposed method generated root mean square errors (RMSE) of 6.78 and 6.63 for the areas of Tosari and Tutur respectively. These results are better compared with the results using Tsukamoto FIS and the Generalized Space Time Autoregressive (GSTAR) model from previous studies

Hybrid Genetic Algorithm for Optimization of Food Composition on Hypertensive Patient

The healthy food with attention of salt degree is one of the efforts for healthy living of hypertensive patient. The effort is important for reducing the probability of hypertension change to be dangerous disease. In this study, the food composition is build with attention nutrition amount, salt degree, and minimum cost. The proposed method is hybrid method of Genetic Algorithm (GA) and Variable Neighborhood Search (VNS). The three scenarios of hybrid GA-VNS types had been developed in this study. Although hybrid GA and VNS take more time than pure GA or pure VNS but the proposed method give better quality of solution. VNS successfully help GA avoids premature convergence and improves better solution. The shortcomings on GA in local exploitation and premature convergence is solved by VNS, whereas the shortcoming on VNS that less capability in global exploration can be solved by use GA that has advantage in global exploration

Fodder composition optimization using modified genetic algorithm

Determination of the fodder composition is a difficult process because it should simultaneously consider several constraints, such as minimizing the total cost of feed ingredients and maximizing the nutrient needs required by livestock. This study uses a modified genetic algorithm to solve the problem in order to obtain better results. The modification is done by applying numerical methods in generating an initial population of the genetic algorithm. Testing results show that the optimal parameters that can be used to produce the optimal solution are as follows: population size (popsize) is 300, generation number is 400, crossover rate (cr) value is 0.2, and mutation rate (mr) value is 0.6. The modified genetic algorithm provides an average fitness value of 0.142357, while the classical genetic algorithm provides an average fitness value of 0.094354. With additional computational time equal to 110 ms, the use of modified genetic algorithm offered has proven to provide a better result, with a higher fitness value compared with classical genetic algorithm

The Segmentation of Printed Arabic Characters Based on Interest Point

Arabic characters are different compared to the other characters whether from their forms or the way they are read. Before conducting a recognition process, we should conduct segmentation or divide each character to identify each Arabic character of the word. The enormous problem of segmenting the connected Arabic characters is dividing each character with different positions, forms, and sizes for each character. Therefore, we suggested a method in segmentation process by using the interesting point, which successfully obtains the 86.5% average accuracy

Hybridizing PSO With SA for Optimizing SVR Applied to Software Effort Estimation

This study investigates Particle Swarm Optimization (PSO) hybridization with Simulated Annealing (SA) to optimize Support Vector Machine (SVR). The optimized SVR is used for software effort estimation. The optimization of SVR consists of two sub-problems that must be solved simultaneously; the first is input feature selection that influences method accuracy and computing time. The next sub-problem is finding optimal SVR parameter that each parameter gives significant impact to method performance. To deal with a huge number of candidate solutions of the problems, a powerful approach is required. The proposed approach takes advantages of good solution quality from PSO and SA. We introduce SA based acceptance rule to accept new position in PSO. The SA parameter selection is introduced to improve the quality as stochastic algorithm is sensitive to its parameter. The comparative works have been between PSO in quality of solution and computing time. According to the results, the proposed model outperforms PSO SVR in quality of solutio

Earthquake Magnitude and Grid-Based Location Prediction using Backpropagation Neural Network

Earthquakes, a type of inevitable natural disaster, is responsible for the highest average death toll per year compared to other types of a natural disaster. Even though it is inevitable, but it can be anticipated to minimize damage and casualties, such as predicting the earthquake‘s magnitude using a neural network. In this study, a backpropagation algorithm is used to train the multilayer neural network to weekly predict the average magnitude of earthquakes in grid-based locations in Indonesia. Based on the findings in this research, the neural network is able to predict the magnitude of earthquakes in grid-based locations across Indonesia with a minimum error rate of 0.094 in 34.475 seconds. This best result is achieved when the neural network is trained for 210 epochs, with 16 neurons used in the input and output layer, one hidden layer consisted of 5 neurons and a learning rate of 0.1. This result showed backpropagation has pretty good generalization capability in order to map the relations between variables when mathematical function is not explicitly available

Optimization of Vehicle Routing Problem with Time Window (VRPTW) for Food Product Distribution Using Genetics Algorithm

Food distribution process is very important task because the product can expire during distribution and the further the distance the greater the cost. Determining the route will be more difficult if all customers have their own time to be visited. This problem is known as the Vehicle Routing Problem with Time Windows (VRPTW). VRPTW problems can be solved using genetic algorithms because genetic algorithms generate multiple solutions at once. Genetic algorithms generate chromosomes from serial numbers that represent the customer number to visit. These chromosomes are used in the calculation process together with other genetic operators such as population size, number of generations, crossover and mutation rate. The results show that the best population size is 300, 3,000 generations, the combination of crossover and mutation rate is 0.4:0.6 and the best selection method is elitist selection. Using a data test, the best parameters give a good solution that minimize the distribution route

Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting

The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other well-known method in the literature