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Malang is one of the areas in East Java - Indonesia that has the potential to experience flooding caused by high rainfall. Floods have a negative impact on both agriculture and public health. Therefore, it is necessary to create a rainfall prediction model in Malang that supports the creation of an early warning system for flood disasters. In this research, a machine learning approach was used, namely the Long Short Term Memory model, to model monthly rainfall at three stations in Malang Regency, namely Abd. Saleh Airbase Station, Karangkates Station and Karangploso Station. Several inputs were tried, namely rainfall lag from the station itself and from neighboring stations as well as other weather variables, namely air humidity, air pressure and air temperature. The results obtained from this research are the best LSTM model for Abd. Saleh Airbase Station rainfall in is similar to the best LSTM model for Karangploso Station rainfall, namely a LSTM model with input rainfall lag 6 for the station itself. Meanwhile, the best model for Karangkates Station rainfall is a model with input rainfall lag 1, lag 4 and lag 6 from the three stations. [ABSTRACT FROM AUTHOR]

A spatial cross-correlation is a measure of the interaction of one local variable on another variable of the surrounding regions. In regional economics, the observed variables are mostly dynamic over time. When the time dimension is accommodated using spatial panel data, the time dependence can be addressed properly. Therefore, this study aims to formulate a dynamic time-dependent spatial cross-correlation index based on spatial panel data. The conducted simulation study shows the good performance of the measure. It also performs well in measuring the spatial cross-correlation between regional GDP growth and the percentage of urban population of the 38 East Java regencies/municipalities. [ABSTRACT FROM AUTHOR]

Floods are one of the hydrological disasters that are often faced by the City and Regency of Malang and other regions in Indonesia. Floods generally occur due to high rainfall. In addition to floods, droughts are also often experienced in some areas due to the lack of rainfall in these areas. Therefore, it is necessary to do modeling to predict rainfall so that related parties can anticipate the occurrence of floods and droughts. The purpose of this study is to form a Spatial Vector Autoregressive -- Neural Network model to predict rainfall and other weather variables in Malang and Karangkates, Indonesia. In this study, we modified SpVAR with additional time indicator variables indicating the rainy or dry season, and conducting Neural Network modeling with input from SpVAR modeling. The result of this research is the best model in this study is SpVAR -- NN with Indicator Variable because it has smallest RMSE. The inputs used are seasonal indicator variables, rainfall, average of temperature, average of air humidity, and average of wind speed in Malang and in Karangkates one to four days earlier, the output used is rainfall, average of temperature, average of air humidity, and average of wind speed in Malang and in Karangkates, the activation functions is logistic sigmoid and the number of units in the hidden layer is 5. [ABSTRACT FROM AUTHOR]

Indonesia is a tropical country with varying rainfall patterns. Rainfall pattern determines the cropping pattern of rice and other crops in Indonesia. This study aims to make models of rainfall, average of air humidity, average of temperature, duration of sun exposure and average of wind speed at two climatological and geophysical stations in Malang Regency simultaneously using the Spatial Vector Autoregressive model. The results of this study are (1) Rainfall in Malang is only related to weather variables in Malang, namely rainfall, average of air humidity and average of wind speed in the previous period. (2) Rainfall in Karangkates is related to weather variables in Karangkates, namely rainfall, average of temperature, duration of sun exposure and average of wind speed and and average of temperature in Malang in the previous period. (3) Average of temperature, average of air humidity, duration of sun exposure and average of wind speed in Malang are related to several other weather variables in Malang and Karangkates. Likewise, average of temperature, average of air humidity, duration of sun exposure and average of wind speed in Karangkates. [ABSTRACT FROM AUTHOR]

The disaggregation technique must be applied in certain cases when information only available at a high-level spatial unit, to draw any conclusion for the lower level spatial unit. This problem commonly occurs in the area of regional economics, agricultural economics or environment study. One of the disaggregation techniques, namely entropy-based distributionally weighted regression is particularly the main interest in this study. It conducts a simulation study to analyze the effect of the explanatory power of the predictor, and the type of correlation between response and weight variable, on the accuracy of the method. The simulation study shows that the technique works well for predictors with at least medium explanatory power. The use of negatively correlated weight will not lead to a serious accuracy problem, as long as the correlation is kept at a low degree. [ABSTRACT FROM AUTHOR]

Land value is the product of past decision of its use. It is also affected by the observed externalities. The effect of each factor on land value has dynamic and spatial virtues, which can be captured based on an empirical model using a spatial panel dynamic setting. The model is useful to estimate the extent and the dynamic nature of land use externalities, important information to formulate an effective urban growth management's policy. The objective of this paper is to derive the instant and the dynamic indirect effects - externalities on land value.The result is applied to analyse the significance of land use externalities on agricultural land value in the Jakarta's fringe. The analysis indicates the significance of the negative externalities, the previous land value and the local accessibility . The effects are accumulated dynamically over years, but they will fully affect the land value after 6 years. [ABSTRACT FROM AUTHOR]